A Generalised Flow for B2B Sales Predictive Modelling

PURPOSE: 

The paper and packaging company that provided the data for this research has a long history of sales expertise. This expertise is captured predominantly in the intuition of sales representatives, many of whom have worked in the industry for 20 years or more 

AIM: 

Intuition  is  not  easy  to  record  and  disseminate  across  an  entire  sales  force,  however,  and  thus  one  of  the company’s most valuable resources is inaccessible to the broader organization. As a result, the company tasked this  team  with  extracting  the  most  important  factors  in  driving  sales  success  and  modeling  win  propensities using data from their customer relationship management (CRM) system. 

OBJECTIVE 

Most  prior  work  in  this  space  has  been  performed  by  private  companies,  both  those  that  have  developed proprietary technologies for internal use and those that sell B2B services related to predictive sales modeling. As a result, research in the field is typically unavailable to the public. Some examples include Implisit —a company recently  acquired  by  Salesforce.com  that  focuses  on  data  automation  and  predictive  modeling—and  Insight 

Squared, which sells software that includes a capability to forecast sales outcomes. 

ABSTRACT: 

Predicting the  outcome of sales opportunities is a core part of successful business management. Conventionally, making this prediction has relied mostly on subjective human evaluations in the process of sales decision  making. In this paper,  we addressed the problem  of forecasting the outcome of  business  to  business 

(B2B)  sales  by  proposing  a  thorough  data-driven  Machine  Learning  (ML)  workflow  on  a  cloud-based computing platform: Microsoft  Azure Machine Learning Service (Azure ML). This workflow consists of two pipelines: (1) An ML pipeline to train probabilistic predictive models on the historical sales opportunities data. 

In this pipeline, data is enriched with an extensive feature enhancement step and then used to train an ensemble of ML classification models in parallel. (2) A prediction pipeline to utilize the trained ML model and infer the likelihood  of  winning  new  sales  opportunities  along  with  calculating  optimal  decision  boundaries.  The effectiveness of the proposed workflow was evaluated on a real sales dataset of a major global B2B consulting firm. Our results implied that decision-making based on the ML predictions is more accurate and brings a higher monetary value. 

Keywords:  Costumer  Relation  Management;  Business  to  Business  Sales  Prediction;  Machine  Learning; Predictive Modeling;