Applications Sales Implementation Guide
11g Release 1 (11.1.2)
Part Number E20373-02
This chapter contains the following:
Define Sales Prediction Configuration
Select Model Entities and Attributes
Predictive models analyze sales data to evaluate buying patterns and selling win or loss rates. The model can be used to identify customer profiles with a greater likelihood to buy certain target products. After evaluation of model results, lead generation can be scheduled in order to disseminate lead recommendations to users who can benefit from the insight gathered. Each lead recommendation includes win likelihood in addition to the average expected revenue and sales cycle duration from past similar deals for the same product to similar customers.
Oracle Fusion Sales Prediction Engine leverages the power of predictive analytical models to identify patterns and correlation of data for the purpose of identifying what products to consider positioning next to your customers. The application leverages multiple mathematical models in order to formulate the likelihood a given customer will purchase a specific product, the estimated revenue which can be expected, and the duration of the estimated sales effort.
After the statistical model generates against the historical sales data based on the selected entities and attributes, summary and detail reports show critical insights as to what customers buy and can be used to predict the right products for the right customers. You can then use these insights to refine the process of generating leads based on what the customers are more likely to buy. Moreover, the same model can be used to predict the win likelihood of current opportunity revenue based on analysis of similar opportunities in the past. Also, product domain or market experts can write prediction rules to recommend products based on a set of rules conditions, utilizing all available customer profile attributes as well as other metrics.
The statistical analysis will identify which data has an influence in determining a likelihood to buy. After the statistical model generates against the historical sales data based on the selected entities and attributes, summary and detail reports show critical insights as to what customers buy and can be used to predict the right products for the right customers. As such, the decision regarding the selection of entities and attributes is critical. While the selection of certain entities and attributes may seem logical based on the awareness of sales behavior (customers in certain industries have a stronger affinity for certain products, for example), where there is uncertainty, the statistical model analysis will provide the necessary insight into whether patterns and correlation emerge. As such, as much available data as possible should be leveraged for the purpose of evaluation. Some factors which weight into the decision include the availability and accuracy of the data.
Additionally, the application allows for the inclusion of expert insight from product management and sales and marketing operations. These expert insights can be captured via prediction rules. The same data made available to and leveraged by the predictive model, is also available for rule authoring.
Your company sells a service that appeals mostly to larger companies, and another service that targets smaller customers. If a customer purchased one of your product packages, then the customer already has all service needs covered by the package. You want to know, given a product recommendation, if credit score, asset, and customer size are important predictors when it comes to recommending this particular product.
You select the following entities and attributes:
Customer Size Code
Past Purchased Products or Services
Assets and Service Contracts
These entities and associated attributes entail the data available to the enterprise which the predictive models can evaluate for identifying correlation, and which you can use to create prediction rules. Over time, you can further refine the selections based on availability of data and the cost to integrate that data for evaluation.