WO2025129274A1

WO2025129274A1 - Forecasting tool and method therefor

Info

Publication number: WO2025129274A1
Application number: PCT/AU2024/051404
Authority: WO
Inventors: Warwick KIRBY
Original assignee: Prevu Corp Pty Ltd
Current assignee: Prevu Corp Pty Ltd
Priority date: 2023-12-22
Filing date: 2024-12-23
Publication date: 2025-06-26
Anticipated expiration: 2026-06-22

Abstract

The present invention relates to a forecasting tool and artificial intelligence (Al) module in a forecasting tool. The forecasting tool calculates a first forecast for a sales event using a first methodology and a second forecast sing a second methodology, and compares these. The Al forecasting module can be used to retrieve information from customer relationship management (CRM) software as well as from online resources and use these to generate a recommended set of milestones that would be most effective in a sales event, as well as a prioritised list of information (which may be tasks, information to be requested or information to be sent) that would be most effective in driving a successful sales event. The Al module can also use the information to generate deal insights and/or coaching prompts for display to a sales person.

Description

FORECASTING TOOL AND METHOD THEREFOR

Field of the Invention

[001] The present invention relates to a forecasting tool and in particular to a forecasting tool for facilitating the more accurate prediction of incoming business.

[002] The invention has been developed primarily for use in/with sales and will be described hereinafter with reference to this application. However, it will be appreciated that the invention is not limited to this particular field of use.

Background of the Invention

[003] The ability to forecast sales and/or revenues accurately as a business is critical and key for the fiscal governance of any organisation. Most customer relationship management (CRM) systems and/or revenue forecasting solutions based there qualification stages on a linear framework or Kanban board to generate a weighted forecast. A weighted forecast allows for cross validation of an individual managers forecast with a waiting lens. One issue with the current systems is that not all sale processes are linear, so inaccuracies can occur in forecasting.

[004] Any discussion of the background art throughout the specification should in no way be considered as an admission that such background art is prior art, nor that such background art is widely known or forms part of the common general knowledge in the field in Australia or any other country.

Summary of the Invention

[005] The invention seeks to provide a forecasting tool and method therefor which will overcome or substantially ameliorate at least some of the deficiencies of the prior art, or to at least provide an alternative.

[006] According to a first aspect, the present invention may be said to involve a forecasting tool for facilitating the forecasting of revenues, the forecasting tool including: a. a processor operatively configured for executing digital instructions; b. digital storage media operatively connected to the processor and configured for storing instructions configured for directing the processor to carry out the steps of: i. receiving, by a processor, a first input indicative of at least one or more stages of completion of a first forecasting method for a sales event; ii. receiving, by a processor, a second input indicative of at least one or more stages of completion of a second forecasting method for the sales event; iii. calculating, by a processor, a first predicted forecast for the sales event using a first forecasting algorithm and at least the received first input; iv. calculating, by a processor, a second predicted forecast for the sales event using a second forecasting algorithm and at least the received second input; and v. generating, by a processor, an output comparing the calculated forecasts.

[007] In one embodiment, the forecasts are predicted revenues of the sales event.

[008] In one embodiment, the forecasts are weighted predicted revenue of the sales event.

[009] In one embodiment, the forecasts are probabilities of the success of the sales event. [010] In one embodiment, the system comprises at least one or more transceivers;

[011] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. transmitting, by a processor, the results of one or more of the calculations for display to a user.

[012] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. receiving, by a processor, an input of a personal forecast by a user for the sales event.

[013] In one embodiment, the first forecasting algorithm is a linear forecasting algorithm. [014] In one embodiment, the second forecasting algorithm is a milestone stage scoring algorithm.

[015] In one embodiment, the second forecasting algorithm is a milestone stage scoring algorithm including negative stages.

[016] In one embodiment, the received second input includes percentage inputs selected from one or more of the following: a. budget; b. authority; c. need; d. time frame; e. presentation; f. procurement: and g. proposal agreement.

[017] In one embodiment, the received first input includes inputs of the stage of the sales process.

[018] In one embodiment, the received first input includes inputs selected from one or more of the following: a. discovery; b. solution identified; c. economic wires; d. time frame agreed; e. demonstration/present; f. proposal sent; g. verbally agreed; h. contract and paperwork; and i. signed or won.

[019] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. receiving, by a processor, an input indicative of at least one or more stages of completion of a plurality of forecasting methods.

[020] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. calculating, by a processor, a predicted revenue for the sales event using a plurality of forecasting algorithms and at least the received input.

[021] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. receiving, by a processor, an input indicative of the result of the sales event. [022] In one embodiment, the input indicative of the result of the sales event may include one or more sale characteristics selected from: a. the sale revenue; b. the goods and/or services sold; c. the terms of payment; d. the duration of a contract; e. the size of the customer in revenue; f. the size of the customer in number of employees; g. the industry of the customer; h. the reason for no sale been concluded; i. the salesperson for the sales event; j. details of the salesperson for the sales event; k. or any other sale characteristics.

[023] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. training, by a processor, an artificial intelligence using as training data one or more selected from: i. the result of the sales event; ii. one or more sale characteristics; iii. the calculated predicted revenue using any one or more of a plurality of forecasting algorithms for the sales event; iv. the at least one or more stages of completion of a plurality of forecasting methods; v. one or more personal inputs; and vi. any other detail of the sales event.

[024] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. generating, by a processor, an artificial intelligence module from the trained artificial intelligence.

[025] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. predicting, by a processor using the trained artificial intelligence module, a prediction on future sales events when provided with one or more selected from: i. one or more sale characteristics; ii. the calculated predicted revenue using any one or more of a plurality of forecasting algorithms for the sales event; iii. the at least one or more stages of completion of a plurality of forecasting methods; iv. one or more personal inputs; and v. any other detail of the sales event.

[026] According to a further aspect, the present invention may be said to involve a method of forecasting a sales event, the method being carried out on an electronic device and including: a. receiving, by a processor, a first input indicative of at least one or more stages of completion of a first forecasting method for a sales event; b. receiving, by a processor, a second input indicative of at least one or more stages of completion of a second forecasting method for the sales event; c. calculating, by a processor, a first predicted revenue for the sales event using a first forecasting algorithm and at least the received first input; d. calculating, by a processor, a second predicted revenue for the sales event using a second forecasting algorithm and at least the received second input; and e. transmitting, by a processor, the results of one or more of the calculations for display to a user.

[027] In one embodiment, the method comprises the step of: a. receiving, by a processor, an input of a personal forecast by a user for the sales event.

[028] In one embodiment, the first forecasting algorithm is a linear forecasting algorithm.

[029] In one embodiment, the second forecasting algorithm is a milestone stage scoring algorithm.

[030] In one embodiment, the second forecasting algorithm is a milestone stage scoring algorithm including negative stages.

[031] In one embodiment, the received second input includes percentage inputs selected from one or more of the following: a. budget; b. authority; c. need; d. Timeframe; e. presentation; f. procurement: and g. proposal agreement.

[032] In one embodiment, the received first input includes inputs of the stage of the sales process. [033] In one embodiment, the received first input includes inputs selected from one or more of the following: a. discovery; b. solution identified; c. economic wires; d. time frame agreed; e. demonstration/present; f. proposal sent; g. verbally agreed; h. contract and paperwork; and i. signed or won.

[034] In one embodiment, the method comprises the step of: a. receiving, by a processor, an input indicative of at least one or more stages of completion of a plurality of forecasting methods.

[035] In one embodiment, the method comprises the step of: a. calculating, by a processor, a predicted revenue for the sales event using a plurality of forecasting algorithms and at least the received input.

[036] In one embodiment, the method comprises the step of: a. receiving, by a processor, an input indicative of the result of the sales event. [037] In one embodiment, the input indicative of the result of the sales event may include one or more sale characteristics selected from: a. the sale revenue; b. the goods and/or services sold; c. the terms of payment; d. the duration of a contract; e. the size of the customer in revenue; f. the size of the customer in number of employees; g. the industry of the customer; h. the reason for no sale been concluded; i. the salesman for the sales event; j. or any other sale characteristics.

[038] In one embodiment, the method comprises the step of: a. training, by a processor, an artificial intelligence using as training data one or more selected from: i. the result of the sales event; ii. one or more sale characteristics; iii. the calculated predicted revenue using any one or more of a plurality of forecasting algorithms for the sales event; iv. the at least one or more stages of completion of a plurality of forecasting methods; v. one or more personal inputs; vi. details of the personal inputs; and vii. any other detail of the sales event.

[039] In one embodiment, the method comprises the step of: a. generating, by a processor, an artificial intelligence module from the trained artificial intelligence.

[040] In one embodiment, the method comprises the step of: a. predicting, by a processor using the trained artificial intelligence module, a prediction on future sales events when provided with one or more selected from: i. one or more sale characteristics; ii. the calculated predicted revenue using any one or more of a plurality of forecasting algorithms for the sales event; iii. the at least one or more stages of completion of a plurality of forecasting methods; iv. one or more personal inputs; and v. any other detail of the sales event.

[041] According to a further aspect, the present invention may be said to involve a method of training an artificial intelligence to forecast a sales event, the method being carried out on an electronic device and including: a. receiving, by a processor, training information for a plurality of sales events, the training formation comprising one or more selected from: i. sales characteristics of the sales event; ii. stage completion information indicative of at least one or more stages of completion of a plurality of forecasting methods for at least one or more sales event; iii. the result of the sales event; iv. a calculated predicted revenue using any one or more of a plurality of forecasting algorithms for the sales event; v. received revenue from the sales event; vi. one or more personal inputs indicative of a personal forecast of a sales event; and vii. details of the personal inputs; b. training, by a processor, an artificial intelligence to generate a forecast from the training information; and c. generating, by a processor, a forecast module from the trained artificial intelligence.

[042] In one embodiment, the details of the personal inputs includes one or more selected from: a. the name of the person making the personal forecast; b. the position of the person making the personal forecast; and c. any other detail of the personal forecast.

[043] In one embodiment, the sales characteristics include one or more selected from: a. the sale revenue; b. the goods and/or services sold; c. the terms of payment; d. the duration of a contract; e. the size of the customer in revenue; f. the size of the customer in number of employees; g. the industry of the customer; h. the reason for no sale been concluded; i. the salesman for the sales event; j. or any other sale characteristics.

[044] According to a further aspect, the present invention may be said to involve a machine learning based forecasting system that implements artificially intelligent forecasting, the forecasting system comprising: a. a processor operatively configured for executing digital instructions; b. digital storage media operatively connected to the processor and configured for storing instructions configured for implementing the machine learning based forecasting system by directing the processor to carry out the steps of: i. receiving, by the processor, one or more selected from:

1. at least one or more inputs indicative of at least one or more stages of completion of one or more forecasting methods for a sales event; 2. at least one or more inputs indicative of a personal forecast by one or more users for the sales event; and

3. at least one or more sales characteristics; ii. calculating, by the processor, at least one or more predicted revenues for the sales event using at least one or more forecasting algorithms and the at least one or more received inputs; and iii. predicting, by the processor using a machine learning module, a forecast for the sales event using the received inputs; and iv. generating, by a processor, an output from the predicted forecast.

[045] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. displaying the output on a display.

[046] According to a further aspect, the present invention may be said to involve a method of forecasting a sales event, the method being carried out on an electronic device and including: a. receiving, by the processor, one or more selected from: i. at least one or more inputs indicative of at least one or more stages of completion of one or more forecasting methods for a sales event; ii. at least one or more inputs indicative of a personal forecast by one or more users for the sales event; and iii. at least one or more sales characteristics; b. calculating, by the processor, at least one or more predicted revenues for the sales event using at least one or more forecasting algorithms and the at least one or more received inputs; and c. predicting, by the processor using a machine learning module, a forecast for the sales event using the received inputs; and d. generating, by a processor, an output from the predicted forecast.

[047] In one embodiment, the method includes the step of: a. displaying the output on a display.

[048] According to a further aspect, the present invention may be said to involve a method for facilitating forecasting revenue from sales event, the method comprising: a. retrieving stored historical details of sales events; b. training an artificial intelligence on the stored historical details of the sales events; c. generating an artificial intelligence forecasting module from the trained artificial intelligence; d. retrieving the current status of the sales event; and e. generating a prioritised list of information that is required that will allow for a more accurate forecast.

[049] In one embodiment, the prioritised list of information may be a prioritised list of milestones to be completed.

[050] In one embodiment, the method includes the step of: a. generating a predicted forecast for the sales event

[051] In one embodiment, the forecast is a forecast of revenue.

[052] In one embodiment, the forecast is a forecast of probability of success.

[053] In one embodiment, the method includes the step of: a. receiving an input indicative of feedback by a customer.

[054] In one embodiment, the method includes the step of: a. utilising the input indicative of feedback by a customer in the generation of the prioritised list of information.

[055] In one embodiment, the method includes the step of: a. displaying the prioritised list of information to a user.

[056] In one embodiment, the method includes the step of: a. receiving an input of additional information on the prioritised list.

[057] In one embodiment, the method includes the step of: a. generating an updated forecast based on the received additional information.

[058] In one embodiment, the method includes the step of: a. retrieving customer profile information.

[059] In one embodiment, the method includes the step of one or more selected from: a. retrieving customer profile information from publicly available sources; b. retrieving opportunity information from publicly available sources; c. receiving information relating to sales techniques; and d. receiving an input of user insight information from a user.

[060] In one embodiment, the method includes the step of one or more selected from: a. training the artificial intelligence using the customer profile information; b. training the artificial intelligence using the opportunity information; c. training the artificial intelligence using the sales techniques; and d. training the artificial intelligence using the user insight information [061] In one embodiment, the method includes the step of one or more selected from: a. utilising the customer profile information in the generation of the prioritised list; b. utilising the opportunity information in the generation of the prioritised list; c. utilising the sales techniques information in the generation of the prioritised list; and d. utilising the user insight information in the generation of the prioritised list.

[062] In one embodiment, the method includes the step of: a. requesting additional information from a user.

[063] According to a further aspect, the present invention may be said to involve a method for facilitating forecasting revenue from sales event, the method comprising: a. retrieving stored historical details of sales events; b. training an artificial intelligence on the stored historical details of the sales events; c. generating an artificial intelligence forecasting module from the trained artificial intelligence; d. retrieving the current status of the sales event; e. generate a recommended set of milestones for use in a milestone forecasting method.

[064] In one embodiment, the method includes the step of: a. generating a predicted forecast for the sales event based on the recommended set of milestones and the current status of the sales event.

[065] In one embodiment, the forecast is a forecast of revenue.

[066] In one embodiment, the forecast is a forecast of probability of success.

[067] In one embodiment, the method includes the step of: a. displaying the recommended set of milestones to a user.

[068] In one embodiment, the method includes the step of: a. generating an updated forecast based on the received additional information.

[069] In one embodiment, the method includes the step of:

[070] In one embodiment, the method includes the step of: a. receiving an input indicative of feedback by a customer; b. retrieving customer profile information from publicly available sources. c. retrieving opportunity information from publicly available sources. d. receiving information relating to sales techniques. e. receiving an input of user insight information from a user.

[071] In one embodiment, the method includes the step of: a. training the artificial intelligence using the customer profile information. b. training the artificial intelligence using the opportunity information. c. training the artificial intelligence using the sales techniques.

[072] In one embodiment, the method includes the step of one or more selected from: a. utilising the customer profile information in the generation of the recommended set of milestones; b. utilising the opportunity information in the generation of the recommended set of milestones; c. utilising the sales techniques information in the generation of the prioritised list; and d. utilising the input indicative of feedback by a customer in the generation of the recommended set of milestones; e. utilising the user insight information in the generation of the recommended set of milestones.

[073] According to a further aspect, the present invention may be said to involve a method for facilitating forecasting revenue from sales event, the method comprising: a. retrieving stored historical details of sales events; b. training an artificial intelligence on the stored historical details of the sales events; c. generating an artificial intelligence forecasting module from the trained artificial intelligence; d. retrieving the current status of the sales event; e. generating one or more selected from i. deal insights, and ii. coaching prompts; and f. displaying the one or more selected from deal insights and coaching prompts to a user.

[074] In one embodiment, the forecast is a forecast of revenue.

[075] In one embodiment, the forecast is a forecast of probability of success.

[076] In one embodiment, the method includes the step of: a. generating a predicted forecast for the sales event. [077] In one embodiment, the method includes the step of:

[078] In one embodiment, the method includes the step of: a. generating an updated forecast based on the received additional information.

[079] In one embodiment, the method includes the step of: a. retrieving customer profile information.

[080] In one embodiment, the method includes the step of one or more selected from: a. receiving an input indicative of feedback by a customer; b. retrieving customer profile information from publicly available sources; c. retrieving opportunity information from publicly available sources; d. receiving information relating to sales techniques; and e. receiving an input of user insight information from a user.

[081] In one embodiment, the method includes the step of one or more selected from: a. training the artificial intelligence using the input indicative of feedback by a customer; b. training the artificial intelligence using the customer profile information; c. training the artificial intelligence using the opportunity information; d. training the artificial intelligence using the user insight information; and e. training the artificial intelligence using the sales techniques.

[082] In one embodiment, the method includes the step of one or more selected from: a. utilising the input indicative of feedback by a customer in the generation of the one or more selected from deal insights and coaching prompts; b. utilising the customer profile information in the generation of the one or more selected from deal insights and coaching prompts; c. utilising the opportunity information in the generation of the one or more selected from deal insights and coaching prompts; d. utilising the sales techniques information in the generation of the one or more selected from deal insights and coaching prompts; and e. utilising the user insight information in the generation of the prioritised list.

[083] In one embodiment, the method includes the step of: a. determining a psychological profile for the customer as part of the customer profile information.

[084] In one embodiment, the method includes the step of: a. determining a psychological profile for the customer representative as part of the customer profile information. [085] According to a further aspect, the present invention may be said to involve a machine learning based forecasting module that implements artificially intelligent forecasting, the forecasting module utilizing an artificial intelligence that has been trained on historical sales data, the forecasting module comprising: a. a processor operatively configured for executing digital instructions; b. digital storage media operatively connected to the processor and configured for storing instructions configured for implementing the machine learning based forecasting module by directing the processor to carry out the steps of: i. retrieving the current status of the sales event; and ii. generating a prioritised list of information that is required that will allow for a more accurate forecast.

[086] In one embodiment, the prioritised list of information is a prioritised list of milestones to be completed.

[087] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. generating a predicted forecast for the sales event

[088] In one embodiment, the forecast is a forecast of revenue.

[089] In one embodiment, the forecast is a forecast of probability of success.

[090] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. receiving an input indicative of feedback by a customer.

[091] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. utilising the input indicative of feedback by a customer in the generation of the one or more selected from deal insights and coaching prompts.

[092] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. displaying the prioritised list of information to a user.

[093] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. receiving an input of additional information on the prioritised list.

[094] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. generating an updated forecast based on the received additional information.

[095] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. retrieving customer profile information.

[096] In one embodiment, the instructions may be configured for directing the processor to carry out the step of one or more selected from: a. retrieving customer profile information from publicly available sources; b. retrieving opportunity information from publicly available sources; c. receiving information relating to sales techniques; and d. receiving an input of user insight information from a user.

[097] In one embodiment, the instructions may be configured for directing the processor to carry out the step of one or more selected from: a. retraining the artificial intelligence using the customer profile information; b. retraining the artificial intelligence using the opportunity information; c. retraining the artificial intelligence using the sales techniques; and d. retraining the artificial intelligence using the user insight information

[098] In one embodiment, the instructions may be configured for directing the processor to carry out the step of one or more selected from: a. utilising the customer profile information in the generation of the prioritised list; b. utilising the opportunity information in the generation of the prioritised list; c. utilising the sales techniques information in the generation of the prioritised list; and d. utilising the user insight information in the generation of the prioritised list. [099] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. requesting additional information from a user.

[0100] According to a further aspect, the present invention may be said to involve a machine learning based forecasting module that implements artificially intelligent forecasting, the forecasting module utilizing an artificial intelligence that has been trained on historical sales data, the forecasting module comprising: a. a processor operatively configured for executing digital instructions; b. digital storage media operatively connected to the processor and configured for storing instructions configured for implementing the machine learning based forecasting module by directing the processor to carry out the steps of: i. retrieving the current status of the sales event; c. generating one or more selected from i. deal insights, and ii. coaching prompts.

[0101] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. generating a predicted forecast for the sales event.

[0102] In one embodiment, the forecast is a forecast of revenue.

[0103] In one embodiment, the forecast is a forecast of probability of success of the sales event.

[0104] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. receiving an input indicative of feedback by a customer.

[0105] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. utilising the input indicative of feedback by a customer in the generation of one or more selected from deal insights, and coaching prompts.

[0106] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. displaying the one or more selected from deal insights and coaching prompts.

[0107] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. receiving an input of additional information relating to the one or more selected from deal insights, and coaching prompts.

[0108] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. generating an updated forecast based on the received additional information.

[0109] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. retrieving customer profile information. [0110] In one embodiment, the instructions may be configured for directing the processor to carry out the step of one or more selected from: a. retrieving customer profile information from publicly available sources; b. retrieving opportunity information from publicly available sources; c. receiving information relating to sales techniques; and d. receiving an input of user insight information from a user.

[0111] In one embodiment, the instructions may be configured for directing the processor to carry out the step of one or more selected from: a. training the artificial intelligence using the customer profile information; b. training the artificial intelligence using the opportunity information; c. training the artificial intelligence using the sales techniques; and d. training the artificial intelligence using the user insight information.

[0112] In one embodiment, the instructions may be configured for directing the processor to carry out the step of one or more selected from: a. utilising the customer profile information in the generation of one or more selected from deal insights, and coaching prompts; b. utilising the opportunity information in the generation of one or more selected from deal insights, and coaching prompts; c. utilising the sales techniques information in the generation of one or more selected from deal insights, and coaching prompts; and d. utilising the user insight information in the generation of one or more selected from deal insights, and coaching prompts.

[0113] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. requesting additional information from a user.

[0114] According to a further aspect, the present invention may be said to involve a machine learning based forecasting module that implements artificially intelligent forecasting, the forecasting module utilizing an artificial intelligence that has been trained on historical sales data, the forecasting module comprising: a. a processor operatively configured for executing digital instructions; b. digital storage media operatively connected to the processor and configured for storing instructions configured for implementing the machine learning based forecasting module by directing the processor to carry out the steps of: i. retrieving the current status of the sales event; ii. determining a recommended set of milestones for use in a milestone forecasting method.

[0115] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. generating a predicted forecast for the sales event based on the recommended set of milestones and the current status of the sales event.

[0116] In one embodiment, the forecast is a forecast of revenue.

[0117] In one embodiment, the forecast is a forecast of probability of success.

[0118] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. displaying the recommended set of milestones to a user.

[0119] In one embodiment, the instructions may be configured for directing the processor to carry out the step of: a. generating an updated forecast based on the received additional information.

[0120] In one embodiment, the instructions may be configured for directing the processor to carry out the step of one or more selected from: a. receiving an input indicative of feedback by a customer; b. retrieving customer profile information from publicly available sources. c. retrieving opportunity information from publicly available sources. d. receiving information relating to sales techniques. e. receiving an input of user insight information from a user.

[0121] In one embodiment, the instructions may be configured for directing the processor to carry out the step of one or more selected from: a. training the artificial intelligence using the customer profile information. b. training the artificial intelligence using the opportunity information. c. training the artificial intelligence using the sales techniques.

[0122] In one embodiment, the instructions may be configured for directing the processor to carry out the step of one or more selected from: a. utilising the customer profile information in the generation of the recommended set of milestones; b. utilising the opportunity information in the generation of the recommended set of milestones; c. utilising the sales techniques information in the generation of the prioritised list; and d. utilising the input indicative of feedback by a customer in the generation of the recommended set of milestones; e. utilising the user insight information in the generation of the recommended set of milestones.

[0123] It should be noted that the web server, client computing device and the computer readable storage medium provide the same or similar advantages as the advantages provided by the corresponding computer implemented method, some of which are described herein. Additionally the web server and/or client computing device provides the advantage of deployment across a computer network, such as the Internet, providing distribution, access and economy of scale advantages. Furthermore, the computer readable storage medium provides further advantages, such allowing the deployment of computer instructions for installation and execution by one or more computing devices.

[0124] Other aspects of the invention are also disclosed.

Brief Description of the Drawings

[0125] Notwithstanding any other forms which may fall within the scope of the present invention, a preferred embodiment of the invention will now be described, by way of example only, with reference to the accompanying drawings in which:

[0126] Figure 1 shows a network of computing devices on which the various embodiments described herein may be implemented in accordance with an embodiment of the present invention;

[0127] Figure 2 shows a computing device on which the various embodiments described herein may be implemented in accordance with an embodiment of the present invention; [0128] Figure 3 shows a schematic diagram of a forecasting tool, including software modules used by the forecasting tool; and

[0129] Figure 4 shows a schematic diagram setting out two types of forecasting methods and their stages of completion;

[0130] Figure 5 shows a schematic diagram setting out an embodiment of a display by a forecasting tool showing predicted forecasts;

[0131] Figures 6-10 show flowcharts setting out methodologies that may be carried out on and by the forecasting tool, forecasting system and forecasting module.

Description of Embodiments

[0132] It should be noted in the following description that like or the same reference numerals in different embodiments denote the same or similar features. System of computing devices

[0133] Figure 1 shows a system 1000 of computing devices adapted for use as a forecasting tool, and on which the methods described below may be carried out.

[0134] As such, the system 1000 includes a server 1100 for serving web pages to one or more client computing devices 1200 over the Internet 1300.

[0135] In a preferred embodiment, the server 1100 is a web server having a web server application 1110 for receiving requests, such as Hypertext Transfer Protocol (HTTP) and File Transfer Protocol (FTP) requests, and serving hypertext web pages or files in response. The web server application 1110 may be, for example the Apache™ or the Microsoft™ IIS HTTP server.

[0136] The server 1100 is also provided with a hypertext preprocessor 1120 for processing one or more web page templates 1130 and data from one or more databases 1140 to generate hypertext web pages. The hypertext preprocessor may, for example, be the PHP: Hypertext Preprocessor (PHP) or Microsoft Asp™ hypertext preprocessor. The web server 1100 is also provided with web page templates 1130, such as one or more PHP or ASP files.

[0137] Upon receiving a request from the web server application 1110, the hypertext preprocessor 1120 is operable to retrieve a web page template from the web page templates 1130, execute any dynamic content therein, including updating or loading information from the one or more databases 1140, to compose a hypertext web page. The composed hypertext web page may comprise client-side code, such as Javascript, for Document Object Model (DOM) manipulating, asynchronous HTTP requests and the like.

[0138] The database 1140 is adapted for storing user account data representing one or more user accounts for users. Such user account data is created by the server 1100 during a user registration process. In this manner, the server 1100 is adapted to update the user account data in relation to the appropriate user account.

[0139] Client computing devices 1200 are preferably provided with a browser application 1210, such as the Google Chrome™, Mozilla Firefox™ or Microsoft Internet Explorer™ browser applications. The browser application 1210 requests hypertext web pages from the web server 1100 and renders the hypertext web pages on a display device for a user to view.

[0140] Client side code is also downloadable as applications on the client computing device 1200 and/or server 1100, in order to facilitate the operation of and /or interaction with the forecasting tool. Such applications could, for example, be downloaded from the Apple App Store™, Google Play™, or the like.

[0141] Client side code may also be provided as blockchain enabled code for suitable users of the system. Such blockchain enabled code may be configured for reading and writing directly to a node of the blockchain, or for communicating via a remote node such as a universal resolver node.

[0142] Client computing devices 1200 may communicate over the Internet 1300 via fixed line or wireless communication, for example using known networks of cellular communication towers 1400.

[0143] Preferably, communications between the various devices and/or systems and/or modules are over a secure communications network.

Computing device

[0144] Figure 2 shows a computing device 500. In a preferred embodiment, the computing device 500 takes the form of a server 1100 as described above. In this manner, the computing device 500 is adapted to comprise functionality for communication with the Internet 1300, storage capability (such as the database 1140) for storing user account data, records of communications, and the like.

[0145] However, it should be noted that the computing device 500 may be adapted for use as the client computing devices 1200 as is also shown in Figure 1. In this manner, the computing device 500 may comprise differing technical integers in order to achieve the functionality as set out below.

[0146] In other words, the technical integers of the computing device 500 as shown in Figure 2 are exemplary only and variations, adaptations and the like may be made thereto within the purposive scope of the embodiments described herein and having regard for the particular application of the computing device 500.

[0147] In particular the steps of the forecasting tool, as described in further detail below, can be implemented as computer program code instructions executable by the computing device 500.

[0148] The computer program code instructions may be divided into one or more computer program code instruction libraries, such as dynamic link libraries (DLL), wherein each of the libraries performs a one or more steps of the method. Additionally, a subset of the one or more of the libraries may perform graphical user interface tasks relating to the steps of the method. [0149] The computing device 500 preferably comprises semiconductor memory 510 comprising volatile memory such as random access memory (RAM) or read only memory (ROM). The memory 510 may comprise either RAM or ROM or a combination of RAM and ROM.

[0150] The computing device 500 comprises a computer program code storage medium reader 515 for reading the computer program code instructions from computer program code storage media 520. The storage media 520 may be optical media such as CD-ROM disks, magnetic media such as floppy disks and tape cassettes or flash media such as USB memory sticks.

[0151 ] The device further comprises I/O interface 530 for communicating with one or more peripheral devices. The I/O interface 530 may offer both serial and parallel interface connectivity. For example, the I/O interface 530 may comprise a Small Computer System Interface (SCSI), Universal Serial Bus (USB) or similar I/O interface for interfacing with the storage medium reader 515. The I/O interface 530 may also communicate with one or more human input devices (HID) 540 such as keyboards, pointing devices, joysticks and the like.

[0152] The I/O interface 530 may also comprise a computer to computer interface, such as a Recommended Standard 232 (RS-232) interface, for interfacing the device 500 with one or more personal computer (PC) devices 550. The I/O interface 530 may also comprise an audio interface 560 for communicate audio signals to one or more audio devices (not shown), such as a speaker or a buzzer.

[0153] The device 500 also comprises a network interface 570 for communicating with one or more computer networks 580, such as the Internet 1300. The network 580 may be a wired network, such as a wired Ethernet™ network or a wireless network, such as a Bluetooth™ network or IEEE 802.11 network. The network 580 may be a local area network (LAN), such as a home or office computer network, or a wide area network (WAN), such as the Internet or private WAN. The device 500 can also include an antenna 575 configured for wireless communication with network 580.

[0154] The device 500 comprises an arithmetic logic unit or processor 590 for performing the computer program code instructions. The processor 590 may be a reduced instruction set computer (RISC) or complex instruction set computer (CISC) processor or the like. The computing device 500 further comprises a storage device 600, such as a magnetic disk hard drive or a solid state disk drive for storing data and/or software instructions.

[0155] Computer program code instructions may be loaded into the storage device 600 from the storage media 520 using the storage medium reader 515 or from the network 580 using network interface 570. Alternatively, computer program code instructions may be loaded into the storage device 600 from an online resource via the network 580 and network interface 570.

[0156] During the bootstrap phase, an operating system and one or more software applications are loaded from the storage device 600 into the memory 510. During the fetch-decode-execute cycle, the processor 590 fetches computer program code instructions from memory 510, decodes the instructions into machine code, executes the instructions and stores one or more intermediate results in memory 510.

[0157] In this manner, the instructions stored in the memory 510, when retrieved and executed by the processor 590, configures the computing device 500 as a specialpurpose machine that may perform the functions described herein.

[0158] The computing device 500 can also include an audio/video interface 610 for conveying video signals to a display device 620, such as a liquid crystal display (LCD), light emitting diode (LED) display, organic light emitting diode (OLED) display, cathoderay tube (CRT) or similar display device.

[0159] The device 500 preferably includes a communication bus subsystem 630 for interconnecting the various devices described above. The bus subsystem 630 may offer parallel connectivity such as Industry Standard Architecture (ISA), conventional Peripheral Component Interconnect (PCI) and the like or serial connectivity such as PCI Express (PCIe), Serial Advanced Technology Attachment (Serial ATA) and the like. The computing device 500 can also include a clock device 640 configured for providing accurate time stamps for use by the processor 590.

[0160] Preferably the client computing device that is operable by a user of the forecasting tool will by a mobile device such as a mobile phone, laptop, tablet or similar device and will have a near filed communications (NFC) chip 650 installed, which may operate in conjunction with a suitable NFC antenna 660 in order to transmit and receive signals using the NFC protocol. Such an NFC chip 650 and antenna 660 can receive NFC or similar electromagnetic signals from similarly equipped devices. In alternative embodiments it is envisage that alternative protocols may be used where NFC is mentioned in describing the functionality below, such as Bluetooth™, or any of the IEEE802.11 protocols, however these are not preferred.

[0161] It is further anticipated that the computing device can include a physical random number generator 670. However, in alternative embodiments it is envisaged that the random number generator may be provided as part of a software module. [0162] Lastly, it is anticipated that the computing device 500 can include a camera 680. The camera can be used to scan and/or input documents. The camera 680 may be connected via the I/O interface 530 or may be built into the computing device.

Forecasting tool

[0163] An embodiment of a forecasting tool application 2000 that may be applied to the computing devices described above is shown in figure 3, showing a schematic diagram of the software modules that may be operable to provide the methodologies of the forecasting tool that will be described in more detail below.

[0164] Now referring to figure 3, the forecasting tool application 2000 includes an input module 2050. The input module 2050 is configured for receiving inputs by users that are indicative of at least one or more stages of completion of a plurality of forecasting methods relating to sales event. The plurality of forecasting methods may preferably include at least a linear forecasting method and a milestone forecasting method.

[0165] The linear forecasting method may define a series of steps that are typical for a sales process, and which are carried out in sequence, with a percentage completion being associated with each step that is completed. The particular series of steps defined by a user, or can be a standardised series of steps. As an example, the series of sequential steps of a linear forecasting method may include: a. discovery; b. solution identified; c. economic wires; d. time frame agreed; e. demonstration/present; f. proposal sent; g. verbally agreed; h. contract and paperwork; and i. signed or won.

[0166] An input from a user may merely select which of steps has been completed and a percentage may be allocated to each of the steps. An example of a linear stages forecast is shown in shown in figure 4, where 6 out of 10 steps of a sale have been completed (shown with a hatched pattern), with each of the steps being allocated 10%, resulting in a likelihood or probability of the sale being completed of 60%.

[0167] The milestone forecasting method may define a series of milestones that, once completed, either increase or decrease the probability of success of a sales event. A positive or negative (or both) percentage may be associated with each of the milestones. An example of a milestone forecast is shown in figure 4, including 12 milestones of those milestones marked with a “+” (shown with a light hatching) would indicate that the associated percentage would be added to the probability of success, while

(shown as a dark hatching) would indicate that the associated percentage would be taken away from the probability of success of the sales event if it is input as having occurred. In the example shown in figure 4, the total number of positive milestones sums to 45%, while the total number of negative milestones sums to -5%, resulting in the total forecast probability of the success of the sales event being 40%. It should be noted that the milestone “Sentiment” can be both a positive, negative or neutral percentage. It should also be noted that a combination of various and/or negative milestones may result in the same percentage probability of success of the sales event.

[0168] It is envisaged that additional forecasting methods may include Metrics, Economic Buyer, Decision Criteria, Decision Process, Paper Process, Location of Pain, Champion, Petition (MEDDPICC) forecasting method; and/or the budget, authority, needs and timeline (BANT) forecasting method; and/or any other forecast method. It is envisaged that additional forecasting methods may be provided. It is further envisaged that individually customisable forecasting steps and/or milestones can be provided, which can be input through the input module 2050. It is further envisaged that individually customisable forecasting algorithms can be input through the input module 2050, that allows for class to be calculated individually customisable algorithms.

[0169] The forecasting tool application 2000 further includes a forecasting module 2100. The forecasting module 2100 is configured to determine forecasts according to the particular algorithms of each of the different forecasting methods, including the individually customisable forecasting methods. For example, in the linear forecasting method shown in figure 4, the forecasting module 2100 would determine some six steps of 10% each to determine a forecast of 60%. Similarly, the forecasting module 2100 would sum the positive and negative milestones for the milestone forecast method.

[0170] It is envisaged that the milestones of a milestone forecasting method may include, but not be limited to, any one or more selected from: a. location of the prospect; b. budget; c. authority; d. need; e. time frame; f. presentation; g. return on investment identified; h. economic buyer (access to funds); i. decision criteria (fit); j. decision process; k. sentiment; l. deal age; m. deal delay; n. impact of no decision; o. verbal confirmation; p. sponsor; q. procurement: and r. proposal agreement.

[0171] Referring again to figure 3, the forecasting tool application 2000 further includes a sales event module 2150 that is configured for receiving inputs, either directly from a user, or through connection to a Customer Relationship Management (CRM) system. The sales event module 2150 is configured for storing and allowing access to details of a sales event, including the subject of the sale, users involved in the sale (including salespersons, their managers, and customers and/or customer primary contacts), sales event characteristics. It is envisaged that the sales event module may also be able to allocate particular tasks to users that may be related to either the sequential steps of a linear forecast, or may be related to milestones that have been achieved, or not achieved yet. Details of the sales event characteristics can include, but is not limited to, any one or more of the following: a. the sale revenue; b. potential sale amount; c. the goods and/or services sold; d. the terms of payment; e. the duration of a contract; f. the size of the customer in revenue; g. industry type; h. technology type; i. date of sale; j. the size of the customer in number of employees; k. the industry of the customer; l. the reason for no sale having been concluded; m. the salesperson for the sales event; n. or any other sale characteristics.

[0172] The forecasting tool application 2000 may further include personnel module 2200 that figured for retrieving current personnel within the organisation, for example from a human resources system, and allocating sales events to particular salespeople. Allocation of salespeople can be carried out in an automated fashion based on any of the sales event characteristics.

[0173] The forecasting tool application 2000 can further include a historical sales event database 2250 that can be used to store details of past sales events. Such details can include any of the sales event characteristics, salespeople details, dates, or any other features of the sale mentioned above. The historical sales event database 2250 can a record of successful and unsuccessful sales events.

[0174] The forecasting tool application 2000 may include a reporting module 2000 hundred that is configured for assimilating and displaying forecasts and details of sales events. An example of such a display of the selected information is shown in figure 5. The display can also be configured for receiving inputs for use by the input module 2050. [0175] The forecasting tool application 2000 can further include the financial module 2350 that is configured for facing financial and/or accounting systems organisation.

[0176] In addition, the forecasting tool application 2000 can include an artificial intelligence based Al forecasting module 2400. The Al forecasting module 2400 has preferably been created by training an artificial intelligence on the details of past historical sales events. Such details can include, but not be limited to, one or more selected from: a. the result of the sales event; b. one or more sale characteristics; c. the calculated predicted revenue using any one or more of a plurality of forecasting algorithms for the sales event; d. the at least one or more stages of completion of a plurality of forecasting methods; e. one or more personal inputs of personal predictions for the sales event; and f. any other detail or characteristic of the sales event or associated personnel. [0177] After training the artificial intelligence on the details of past historical sales event, the Al forecasting module 2400 will be generated from the trained artificial intelligence. The Al forecasting module 2400 is used to make predictive forecasts on the current sales event, taking into account any one or more selected from the following: a. one or more sale characteristics; b. the calculated predicted revenue using any one or more of a plurality of forecasting algorithms for the sales event; c. the at least one or more stages of completion of a plurality of forecasting methods; d. one or more personal inputs; e. one or more inputs of personal predictions for the sales event for me; f. any information available from the other modules; and g. any other detail of the sales event.

[0178] Predictive forecasts by the Al forecasting module 2400 may also be reported by the reporting module 2300.

[0179] It is further envisaged that the results of current sales event, together with all surrounding details relating to the sales event may be stored in the historical sales event database 2250. Such stored sales events, together with the surrounding details can be used to further train and refine the Al forecasting module 2400.

Functionality

[0180] The functionality of the various embodiments described above will now be explained with reference to the flowcharts shown in figures 6-7. In a discussion of the functionality below, communications between parties are preferably over a secure communication network.

[0181] Now shown with reference to figure 6, it is envisaged that the forecasting tool location 2000 will initially be configured for receiving 1 details of a sales event. Such details may be received by direct input form a user, or by interfacing with other systems such as a CRM system, a marketing system, or an accounting system. The details of the sales event can include any of the sales event characteristics mentioned previously, or any other related details.

[0182] The forecasting tool will also be configured for receiving 2 an input indicative of the stage of completion of a sales event using a first forecasting method, as well as for receiving 4 an input indicative of the stage of completion of the sales event using a second forecasting method. It is envisaged that it may be configured for receiving a plurality of further inputs relating to the stages of completion of a plurality of forecasting methods.

[0183] Preferably the forecast method includes at least a linear forecasting method and a milestone forecasting method, although other forecasting methods are also envisaged. [0184] The forecasting tool will then calculate 6 a first predicted forecast for the sales event using a first forecasting algorithm associated with the first forecasting method.

[0185] The forecasting tool will then similarly calculate 8 a second predicted forecast for the sales event using a second forecasting algorithm associated with the second forecasting method.

[0186] After this, it is envisaged that the forecasting tool will generate 10 an output comparing the first calculated forecast and the second calculated forecast. The generated 14 output may then be transmitted to a display for being displayed 12 to a user. It is envisaged that additional information relating to the sales event may be displayed alongside the generated output in order to assist or guide a user in determining a personal forecast of the sales event.

[0187] The forecasting tool will preferably then receive 14 an input from a user indicative of personal forecast the sales event by a user. Such a personal forecast may be based on the gut feel or experience of the user, and need not necessarily be based on any data, although it is envisaged that the user may use the calculated first predicted forecast and second predicted forecast to guide them in their personal forecast.

[0188] After this, details of the first calculated forecast and the second calculated forecast, together with the personal forecast may be stored 16 in sales event database 2250, preferably together with all of the previously received 1 details of the sales event. The forecast tool will further receive 18 an input indicative of the result of the sales event. This can include details of whether a sale was concluded, what revenue was generated, the goods that were sold, the location of the sale, or any other details that were not already saved, or which need to be updated. Such an input may be received directly from a user, or from an associated system such as an accounting system, an enterprise resource planning system, a stock management system, a CRM system, or the like.

[0189] These additional details related to the result of the sale will also be stored 20 in association with the sales event. It should be noted that each sales event may be allocated a unique identifier that allows information to be stored in association with the sales event on the sales event database, preferably with a time stamp.

[0190] It is further envisaged that the information stored on the sales event database 2250 can be used in the development of an artificial intelligence based forecasting module as will be described in more detail below with reference to figure 7.

[0191] Initially the data stored in association with a plurality of sales events will be retrieved 22 from the sales event database 2250. [0192] In addition, information relating to sales techniques and deal closing may be retrieved 24.

[0193] In addition, information relating to company structures and organizational management may be retrieved 26.

[0194] The retrieved historical details of the sales events as well as the customer profile information will then be used to train 28 an artificial intelligence to be able to generate a forecast for a future sales event, given similar details. It should be noted that the forecast may include any one or more of predicted revenues of a sales event, weighted revenues of a sales event, and the probability of success of the sales event.

[0195] Alternately, the artificial intelligence may be trained to generate a prioritised list of information required from the customer that is most likely to result in a successful sale event, and/or that is most likely to be able to provide an accurate forecast of whether a sale event will be successful. This list will be discussed in more detail below.

[0196] Alternatively and/or additionally, the artificial intelligence may be trained to generate a recommended set of milestones for use in a milestone forecasting method, the recommended set of milestones being the most relevant milestones to be completed for a successful sales event. This will be discussed in more detail below.

[0197] Alternatively and/or additionally, the artificial intelligence may be trained to generate deal insights and/or coaching prompts. These will be discussed in more detail below.

[0198] An artificial intelligence forecasting module will then be generated 30 from the trained 28 artificial intelligence. The generated 30 artificial intelligence forecasting module may then preferably be incorporated 32 into the forecasting tool as the Al forecasting module 2400 in order to be able to predict forecasts for future sales events, given certain information about the sales events.

[0199] As one example, following on from reference letter B in figure 8, when a current sales event is being contemplated, the artificial intelligence module may initially retrieve 40 the current status of the sales event and related transaction data. Examples of information relating to the current status may be information that is already been input relating to the linear forecasting model as well as the milestone forecasting model, as well as other transaction data such as customer name, customer primary contact name, potential sale size, and the like.

[0200] The artificial intelligence module may then receive 42 an input of customer profile information. This could be employed by a user such as a salesperson. The types of customer profile information that are envisaged would be details of the customer website, as well as other social media and online profiles such as Instagram, Linkedln, Facebook, and the like. Other examples of customer profile information may be the customer ID on the associated Customer Relationship Management (CRM) database.

[0201] The artificial intelligence module may then retrieve 44 customer profile information from these public sources, preferably over the Internet. Examples of customer profile information that may be retrieved 24 may include: a. business plans; b. sales and marketing plans; c. product/service documentation; d. customer feedback and surveys; e. competitive analysis reports; f. financial reports; g. organisational chart; h. implementation and support plans; i. company culture and values documents; j. personal profiles of the customer contact; and k. regulatory and compliance documents.

[0202] The artificial intelligence module may then search online to retrieve 46 additional opportunity information relating to the customer, which can be added to the customer profile information. Examples of opportunity information may include upcoming expected mergers, acquisitions, international and local expansion plans, new products, new employee hires, and the like.

[0203] The artificial intelligence module may also receive 48 an input from the user such as a salesman that can add any additional user insights that may be useful.

[0204] The artificial intelligence module may then generate 50 a prioritised list of information that should be requested that would allow for a more accurate forecast. It is envisaged that the prioritised list of information may further be a prioritised list of tasks for the user to carry out that would increase the probability of success of the sales event. Such a prioritised list of tasks would be weighted in favour of those tasks that favourably affect the likelihood that the sales event would be a success.

[0205] This prioritised list of information and/or prioritised list of tasks will then be displayed 52 to the user.

[0206] It is further envisaged that in the artificial intelligence module may then receive 54 an input of additional information on the prioritised list of information from the user. [0207] The artificial intelligence module will then predict 56 a forecast of revenues of the sales event based on the additionally received information.

[0208] In another example following on from reference numeral B as shown in figure 9, the artificial intelligence module will initially retrieve 60 the current status of the present sales event, together with the transaction data as explained previously.

[0209] The artificial intelligence module will then further receive 62 an input of customer profile information as described previously. After this the artificial intelligence module will then similarly retrieve 64 customer profile information from public sources as described previously. In addition, opportunity information may be retrieved 66 as described previously. Further, additional inputs of user insights may be received 68.

[0210] At this stage, the artificial intelligence module may generate 70 deal insights and/or coaching tips from the information it has available to it. It is envisaged that the artificial intelligence module may go so far as to retrieve personality information from online sources relating to the customer primary contact with a view to advising users how to best build rapport with the client primary contact. In addition, the artificial intelligence module may review recorded conversations with the client primary contact with a view to recognizing the personality profile of the client primary contact. It is further envisaged that the artificial intelligence module may review real-time conversations with the client primary contact with a view to recognizing the personality profile of the client primary contact. In this way, the artificial intelligence module can advise the user or salesperson on the use of tone of voice and assertiveness with a view to building rapport with the client primary contact. In addition, a corporate personality profile may be determined from the information available. Such corporate personality profile may relate to organisational structures and/or processes that will allow a user to provide the required information to facilitate the rate of progress of a sales event.

[0211] These deal insights and/or coaching tips will then be displayed 72 to the user.

[0212] Now further following on from reference numeral B in figure 10, as an additional and/or alternative methodology, the artificial intelligence module will the artificial intelligence module will initially retrieve 80 the current status of the present sales event, together with the transaction data as explained previously.

[0213] The artificial intelligence module will then further receive 82 an input of customer profile information as described previously. After this the artificial intelligence module will then similarly retrieve 84 customer profile information from public sources as described previously. Further, additional inputs of user insights may be received 86. In addition, opportunity information may be retrieved 88 as described previously. [0214] At this stage, the artificial intelligence module will generate 90 a set of custom recommended milestones, which may be displayed 92 to the user. The recommended milestones can include information such as criteria for the milestone to be green, situations where the milestone is definitely read, activities undertaken to seek to move this milestone from red to green, customer personas critical to the milestone, and may provide examples where the milestone would be red or green.

[0215] It is envisaged that the recommended milestones may be displayed 92, preferably alongside other forecasting methodologies, such as typical milestone forecasting methodologies and/or linear forecasting methodologies.

[0216] It is further envisaged that any information received and/or retrieved from online sources may be stored in the customer relationship management system and/or sales event database 2250 at any time with a view to having it available for future sales events.

Interpretation

[0217] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. For the purposes of the present invention, additional terms are defined below. Furthermore, all definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms unless there is doubt as to the meaning of a particular term, in which case the common dictionary definition and/or common usage of the term will prevail.

[0218] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular articles “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise and thus are used herein to refer to one or to more than one (i.e. to “at least one”) of the grammatical object of the article. By way of example, the phrase “an element” refers to one element or more than one element.

[0219] The term “about” is used herein to refer to quantities that vary by as much as 30%, preferably by as much as 20%, and more preferably by as much as 10% to a reference quantity. The use of the word ‘about’ to qualify a number is merely an express indication that the number is not to be construed as a precise value. [0220] Throughout this specification, unless the context requires otherwise, the words “comprise”, “comprises” and “comprising” will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements.

[0221] The term “real-time” for example “displaying real-time data,” refers to the display of the data without intentional delay, given the processing limitations of the system and the time required to accurately measure the data.

[0222] As used herein, the term “exemplary” is used in the sense of providing examples, as opposed to indicating quality. That is, an “exemplary embodiment” is an embodiment provided as an example, as opposed to necessarily being an embodiment of exemplary quality for example serving as a desirable model or representing the best of its kind.

[0223] The phrase “and/or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

[0224] As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of,” or, when used in the claims, “consisting of’ will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0225] As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc. [0226]

Bus

[0227] In the context of this document, the term “bus” and its derivatives, while being described in a preferred embodiment as being a communication bus subsystem for interconnecting various devices including by way of parallel connectivity such as Industry Standard Architecture (ISA), conventional Peripheral Component Interconnect (PCI) and the like or serial connectivity such as PCI Express (PCIe), Serial Advanced Technology Attachment (Serial ATA) and the like, should be construed broadly herein as any system for communicating data.

In accordance with:

[0228] As described herein, ‘in accordance with’ may also mean ‘as a function of’ and is not necessarily limited to the integers specified in relation thereto.

Composite items

[0229] As described herein, ‘a computer implemented method’ should not necessarily be inferred as being performed by a single computing device such that the steps of the method may be performed by more than one cooperating computing devices.

[0230] Similarly objects as used herein such as ‘web server’, ‘server’, ‘client computing device’, ‘computer readable medium’ and the like should not necessarily be construed as being a single object, and may be implemented as a two or more objects in cooperation, such as, for example, a web server being construed as two or more web servers in a server farm cooperating to achieve a desired goal or a computer readable medium being distributed in a composite manner, such as program code being provided on a compact disk activatable by a license key downloadable from a computer network.

Database:

[0231] In the context of this document, the term “database” and its derivatives may be used to describe a single database, a set of databases, a system of databases or the like. The system of databases may comprise a set of databases wherein the set of databases may be stored on a single implementation or span across multiple implementations. The term “database” is also not limited to refer to a certain database format rather may refer to any database format. For example, database formats may include MySQL, MySQLi , XML or the like.

Wireless:

[0232] The invention may be embodied using devices conforming to other network standards and for other applications, including, for example other WLAN standards and other wireless standards. Applications that can be accommodated include IEEE 802.11 wireless LANs and links, and wireless Ethernet.

[0233] In the context of this document, the term “wireless” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated devices do not contain any wires, although in some embodiments they might not. In the context of this document, the term “wired” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a solid medium. The term does not imply that the associated devices are coupled by electrically conductive wires.

Processes:

[0234] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as “processing”, “computing”, “calculating”, “determining”, “analysing” or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities into other data similarly represented as physical quantities. Processor:

[0235] In a similar manner, the term “processor” may refer to any device or portion of a device that processes electronic data, e.g., from registers and/or memory to transform that electronic data into other electronic data that, e.g., may be stored in registers and/or memory. A “computer” or a “computing device” ora “computing machine” or a “computing platform” may include one or more processors.

[0236] The methodologies described herein are, in one embodiment, performable by one or more processors that accept computer-readable (also called machine-readable) code containing a set of instructions that when executed by one or more of the processors carry out at least one of the methods described herein. Any processor capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken are included. Thus, one example is a typical processing system that includes one or more processors. The processing system further may include a memory subsystem including main RAM and/or a static RAM, and/or ROM.

Computer-Readable Medium:

[0237] Furthermore, a computer-readable carrier medium may form, or be included in a computer program product. A computer program product can be stored on a computer usable carrier medium, the computer program product comprising a computer readable program means for causing a processor to perform a method as described herein.

Networked or Multiple Processors:

[0238] In alternative embodiments, the one or more processors operate as a standalone device or may be connected, e.g., networked to other processor(s), in a networked deployment, the one or more processors may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to- peer or distributed network environment. The one or more processors may form a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. [0239] Note that while some diagram(s) only show(s) a single processor and a single memory that carries the computer-readable code, those in the art will understand that many of the components described above are included, but not explicitly shown or described in order not to obscure the inventive aspect. For example, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Additional Embodiments:

[0240] Thus, one embodiment of each of the methods described herein is in the form of a computer-readable carrier medium carrying a set of instructions, e.g., a computer program that are for execution on one or more processors. Thus, as will be appreciated by those skilled in the art, embodiments of the present invention may be embodied as a method, an apparatus such as a special purpose apparatus, an apparatus such as a data processing system, or a computer-readable carrier medium. The computer-readable carrier medium carries computer readable code including a set of instructions that when executed on one or more processors cause a processor or processors to implement a method. Accordingly, aspects of the present invention may take the form of a method, an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of carrier medium (e.g., a computer program product on a computer-readable storage medium) carrying computer-readable program code embodied in the medium.

Carrier Medium:

[0241] The software may further be transmitted or received over a network via a network interface device. While the carrier medium is shown in an example embodiment to be a single medium, the term “carrier medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “carrier medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by one or more of the processors and that cause the one or more processors to perform any one or more of the methodologies of the present invention. A carrier medium may take many forms, including but not limited to, nonvolatile media, volatile media, and transmission media.

Blockchain

[0242] A blockchain is a collection of information that is stored electronically in blocks on one or more computer systems, the blocks storing sets of information and being chained onto a previously filled block, forming a chain of data known as the blockchain. New information that follows a freshly added block is compiled into a newly formed block that will also be added to the chain and preferably time stamped once the block is filled. Blockchains are typically implemented as a decentralised, distributed network, in which a plurality of nodes of the network are synchronised to store the same blockchain information. Implementation:

[0243] It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e., computer) system executing instructions (computer-readable code) stored in storage. It will also be understood that the invention is not limited to any particular implementation or programming technique and that the invention may be implemented using any appropriate techniques for implementing the functionality described herein. The invention is not limited to any particular programming language or operating system.

Means For Carrying out a Method or Function

[0244] Furthermore, some of the embodiments are described herein as a method or combination of elements of a method that can be implemented by a processor of a processor device, computer system, or by other means of carrying out the function. Thus, a processor with the necessary instructions for carrying out such a method or element of a method forms a means for carrying out the method or element of a method. Furthermore, an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the invention.

Connected

[0245] Similarly, it is to be noticed that the term connected, when used in the claims, should not be interpreted as being limitative to direct connections only. Thus, the scope of the expression a device A connected to a device B should not be limited to devices or systems wherein an output of device A is directly connected to an input of device B. It means that there exists a path between an output of A and an input of B which may be a path including other devices or means. “Connected” may mean that two or more elements are either in direct physical or electrical contact, or that two or more elements are not in direct contact with each other but yet still co-operate or interact with each other.

Embodiments:

[0246] Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to one of ordinary skill in the art from this disclosure, in one or more embodiments.

[0247] Similarly it should be appreciated that in the above description of example embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description of Specific Embodiments are hereby expressly incorporated into this Detailed Description of Specific Embodiments, with each claim standing on its own as a separate embodiment of this invention.

[0248] Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention, and form different embodiments, as would be understood by those in the art. For example, in the following claims, any of the claimed embodiments can be used in any combination.

Specific Details

[0249] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description. [0250] It will be appreciated that the methods/apparatus/devices/systems described/illustrated above at least substantially provide a forecasting tool.

[0251] The forecasting tool described herein, and/or shown in the drawings, are presented by way of example only and are not limiting as to the scope of the invention. Unless otherwise specifically stated, individual aspects and components of the forecasting tool may be modified, or may have been substituted therefore known equivalents, or as yet unknown substitutes such as may be developed in the future or such as may be found to be acceptable substitutes in the future. The forecasting tool may also be modified for a variety of applications while remaining within the scope and spirit of the claimed invention, since the range of potential applications is great, and since it is intended that the present invention be adaptable to many such variations. Terminology

[0252] In describing the preferred embodiment of the invention illustrated in the drawings, specific terminology will be resorted to for the sake of clarity. However, the invention is not intended to be limited to the specific terms so selected, and it is to be understood that each specific term includes all technical equivalents which operate in a similar manner to accomplish a similar technical purpose. Terms such as "forward", "rearward", "radially", "peripherally", "upwardly", "downwardly", and the like are used as words of convenience to provide reference points and are not to be construed as limiting terms.

Different Instances of Objects

[0253] As used herein, unless otherwise specified the use of the ordinal adjectives “first”, “second”, “third”, etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

Combinations of features in embodiments

[0254] Different features are described in different embodiments in this specification, however it is envisaged that any features shown in any embodiment described may be used with any other features in any other embodiment in any combination, unless this is not logically possible.

Comprising and Including

[0255] In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word “comprise” or variations such as “comprises” or “comprising” are used in an inclusive sense, i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention.

[0256] Any one of the terms: including or which includes or that includes as used herein is also an open term that also means including at least the elements/features that follow the term, but not excluding others. Thus, including is synonymous with and means comprising.

Scope of Invention

[0257] Thus, while there has been described what are believed to be the preferred embodiments of the invention, those skilled in the art will recognize that other and further modifications may be made thereto without departing from the spirit of the invention, and it is intended to claim all such changes and modifications as fall within the scope of the invention. For example, any formulas given above are merely representative of procedures that may be used. Functionality may be added or deleted from the block diagrams and operations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present invention.

[0258] Although the invention has been described with reference to specific examples, it will be appreciated by those skilled in the art that the invention may be embodied in many other forms.

Chronological order

[0259] For the purpose of this specification, where method steps are described in sequence, the sequence does not necessarily mean that the steps are to be carried out in chronological order in that sequence, unless there is no other logical manner of interpreting the sequence.

Markush groups

[0260] In addition, where features or aspects of the invention are described in terms of Markush groups, those skilled in the art will recognise that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.

Industrial Applicability

[0261] It is apparent from the above, that the arrangements described are applicable to the marketing and sales industries.

Claims

1. A forecasting tool for facilitating the forecasting of revenues, the forecasting tool including: a. a processor operatively configured for executing digital instructions; b. digital storage media operatively connected to the processor and configured for storing instructions configured for directing the processor to carry out the steps of: i. receiving, by a processor, a first input indicative of at least one or more stages of completion of a first forecasting method for a sales event; ii. receiving, by a processor, a second input indicative of at least one or more stages of completion of a second forecasting method for the sales event; iii. calculating, by a processor, a first predicted forecast for the sales event using a first forecasting algorithm and at least the received first input; iv. calculating, by a processor, a second predicted forecast for the sales event using a second forecasting algorithm and at least the received second input; and v. generating, by a processor, an output comparing the calculated forecasts.

2. The forecasting tool as claimed in claim 1 , wherein the instructions may be configured for directing the processor to carry out the step of: a. receiving, by a processor, an input of a personal forecast by a user for the sales event.

3. The forecasting tool as claimed in claim 1 , wherein the first forecasting algorithm is a linear forecasting algorithm, and the second forecasting algorithm is a milestone stage scoring algorithm including negative stages.

4. The forecasting tool as claimed in claim 1 , wherein the instructions may be configured for directing the processor to carry out the step of: a. training, by a processor, an artificial intelligence using as training data one or more selected from: i. the result of the sales event; ii. one or more sale characteristics; iii. the calculated predicted revenue using any one or more of a plurality of forecasting algorithms for the sales event; iv. the at least one or more stages of completion of a plurality of forecasting methods; v. one or more personal inputs; and vi. any other detail of the sales event.

5. The forecasting tool as claimed in claim 1 , wherein the instructions may be configured for directing the processor to carry out the step of: a. generating, by a processor, an artificial intelligence module from the trained artificial intelligence.

6. The forecasting tool as claimed in claim 1 , wherein the instructions may be configured for directing the processor to carry out the step of: a. retrieving the current status of the sales event; and b. generating, by a processor using the trained artificial intelligence module, one or more selected from: i. a prioritised list of information that is required that will allow for a more accurate forecast; ii. a recommended set of milestones for use in a milestone forecasting method; iii. one or more selected from deal insights, and coaching prompts.

7. A method of forecasting a sales event, the method being carried out on an electronic device and including: a. receiving, by a processor, a first input indicative of at least one or more stages of completion of a first forecasting method for a sales event; b. receiving, by a processor, a second input indicative of at least one or more stages of completion of a second forecasting method for the sales event; c. calculating, by a processor, a first predicted revenue for the sales event using a first forecasting algorithm and at least the received first input; d. calculating, by a processor, a second predicted revenue for the sales event using a second forecasting algorithm and at least the received second input; and e. transmitting, by a processor, the results of one or more of the calculations for display to a user.

8. The method as claimed in claim 7, wherein the method comprises the step of: a. receiving, by a processor, an input of a personal forecast by a user for the sales event.

9. The method as claimed in either of claims 7 or 8, wherein the first forecasting algorithm is a linear forecasting algorithm, and the second forecasting algorithm is a milestone stage scoring algorithm including negative stages.

10. The method as claimed in any one of claim 7 to 9, wherein the method comprises the step of: a. training, by a processor, an artificial intelligence using as training data one or more selected from: i. the result of the sales event; ii. one or more sale characteristics; iii. the calculated predicted revenue using any one or more of a plurality of forecasting algorithms for the sales event; iv. the at least one or more stages of completion of a plurality of forecasting methods; v. one or more personal inputs; vi. details of the personal inputs; and vii. any other detail of the sales event.

11. The method as claimed in any one of claims 7 to 10, wherein the method comprises the step of: a. generating, by a processor, an artificial intelligence module from the trained artificial intelligence.

12. The method as claimed in any one of claims 7 to 11 , wherein the method comprises the step of: a. retrieving the current status of the sales event; and b. generating, by a processor using the trained artificial intelligence module, one or more selected from: i. a prioritised list of information that is required that will allow for a more accurate forecast; ii. a recommended set of milestones for use in a milestone forecasting method; iii. one or more selected from deal insights, and coaching prompts.

13. A method for facilitating forecasting revenue from sales event, the method comprising: a. retrieving stored historical details of sales events; b. training an artificial intelligence on the stored historical details of the sales events; c. generating an artificial intelligence forecasting module from the trained artificial intelligence; d. retrieving the current status of the sales event; and e. generating one or more selected from: i. a prioritised list of information that is required that will allow for a more accurate forecast; ii. a recommended set of milestones for use in a milestone forecasting method; iii. one or more selected from deal insights, and coaching prompts.

14. The method as claimed in claim 13, wherein the prioritised list of information may be a prioritised list of milestones to be completed.

15. The method as claimed in either of claims 13 or 14, wherein the method includes the step of: a. generating a predicted forecast for the sales event

16. The method as claimed in any one of claims 13 to 15, wherein the method includes the step of: a. displaying the prioritised list of information to a user.

17. The method as claimed in any one of claims 13 to 16, wherein the method includes the step of: a. receiving an input of additional information on the prioritised list.

18. The method as claimed in claim 17, wherein the method includes the step of: a. generating an updated forecast based on the received additional information.

19. The method as claimed in any one of claims 13 to 18, wherein the method includes the step of: a. retrieving customer profile information.

20. The method as claimed in any one of claims 13 to 19, wherein the method includes the step of: a. retrieving customer profile information from publicly available sources; b. retrieving opportunity information from publicly available sources; c. receiving information relating to sales techniques; and d. receiving an input of user insight information from a user.

21. The method as claimed in claim 20, wherein the method includes the step of: a. training the artificial intelligence using the customer profile information; b. training the artificial intelligence using the opportunity information; c. training the artificial intelligence using the sales techniques; and d. training the artificial intelligence using the user insight information

22. The method as claimed in any one of claims 19 to 21 , wherein the method includes the step of: a. utilising the customer profile information in the generation of the prioritised list; b. utilising the opportunity information in the generation of the prioritised list; c. utilising the sales techniques information in the generation of the prioritised list; and d. utilising the user insight information in the generation of the prioritised list.

23. The method as claimed in any one of claims 19 to 21 , wherein the method includes the step of: a. utilising the customer profile information in the generation of the recommended set of milestones; b. utilising the opportunity information in the generation of the recommended set of milestones; c. utilising the sales techniques information in the generation of the recommended set of milestones; and d. utilising the user insight information in the generation of the recommended set of milestones.

24. The method as claimed in any one of claims 19 to 21 , wherein the method includes the step of: a. utilising the customer profile information in the generation of the one or more selected from deal insights, and coaching prompts; b. utilising the opportunity information in the generation of the one or more selected from deal insights, and coaching prompts; c. utilising the sales techniques information in the generation of the one or more selected from deal insights, and coaching prompts; and d. utilising the user insight information in the generation of the one or more selected from deal insights, and coaching prompts.

END