Predictive Analytics Bridges the Gap between Sales and Marketing

Dastani Consulting responds to the question of how companies can apply prediction to enhance their profitability. No matter whether one communicates with sales or marketing managers; their shared objective is of paramount importance. An article submitted on customer think makes it clear that this is still
not reflected in standard practice. 

Early this February, tech-blogger Raja Satish posed the provocative question: “Is technology bridging the sales and marketing divide?” This is provocative because firstly, the silo mentality across different departments inevitably leads to stagnation, and secondly, because in these digitally turbulent times, sales and marketing should be working hand in hand rather than squandering valuable time and energy in unproductive conflict.

Raja Satish outlines the classis distribution of roles: “In a traditional set up, sales and marketing have been looked at as two separate functions wherein the latter is used for lead generation and the former is about conversion of prospects to customers.“ Generating leads, addressing prospects and finally converting them to customers describes the stony path to fresh turnover. But the sheer variety of communications channels available today brings the individual steps into confusion: Does the addressee of a personalised newsletter still ‘belong’ to the marketing function, or already to the sales function?  Numerous marketing activities are inherently directed at motivating existing customers to purchase, so is that sales or is it marketing?

Big Data approaches such as predictive analytics are suited to today’s sales and marketing mix – for example much more so than sales-oriented CRM systems, for which Raja Satish already regards the addition of marketing actions as progress. Predictive analytics functions reliably and in principle consistently across departmental divides.

  • Ask the right question What must vendors be aware of in order to raise their turnover or profit margin:
    • Which B2B customers are most likely to buy their products, and which products would be of interest to them?
    • What level of turnover can one achieve with each existing customer, and in which product segment?
    • Which products is an individual online shopper most likely to purchase on his next visit? Which next-best offers should I present to him?
  • Analyse the database Data from the business software usually provides an excellent basis for predictive analytics
  • Develop and apply a forecasting model A forecasting model adapted to an individual requirement as well as to the existing data structure can process very large data volumes and is self-learning. The results delivered get better and better

If we detect a ‘rift’ in our project work, it will not be between sales and marketing, but rather between management and the specialist departments, or between staff elements who are either enthusiastic and sceptical with regard to data usage. However, even the first relevant metrics are usually sufficient to silence the critics – because throughout the sales organisation, in the final analysis the journey is not the reward, but rather the profit that results from it. In contrast to Raja Satish’s assumption, it is the profit and not the technology that closes the rift, irrespective of which parties it runs through.