Are You Using Your Sales Data Effectively?

So, you find yourself glancing over last month's sales figures and you're not too impressed. Well, we are in the worst recession since the 1920s; lower sales figures must be reflective of low consumer confidence, right? Wrong. The market may be down, but customers are still buying. What has changed is that they are more sophisticated and demanding in their buying habits than ever before.The range of purchasing options and product information available in this information age will not disappear along with the recession. The former is here to stay. So, lower prices? Sell more of a particular product? No. The key, particularly for smaller businesses, does not lie in attempting to increase quantity or slash prices to bolster sales; it lies in identifying the key drivers of sales. Enter predictive analytics.Predictive analytics, simply put, is a powerful mode of business intelligence that allows businesses to analyse the factors that maximise the probability of future product sales and use this information to make more viable business decisions. Analysing past sales figures is not of much help if one cannot determine how that relates to the future. Predictive analytics has the capability of building statistical models to determine the probability of future events. For instance, a regression model might describe projected monthly sales data in this way:SALES = 5,000 + 50(PRODUCT A*CUSTOMER B) - 20(PRODUCT B*CUSTOMER B)Let's put some real English behind the jargon:• 5,000: Refers to the minimum sales your business can be expected to make per month based on past trends.


• 50(PRODUCT A*CUSTOMER B): Illustrates that when 1 unit of Product A is sold to customer B, revenues rise by €50 each time.• -20(PRODUCT B*CUSTOMER B): However, for every unit of Product B sold to Customer B, revenues drop by €20 each time - showing an overall net loss of revenue in our equation due to the opportunity cost of selling Customer B the product that is in more demand by this segment, in this case Product A.Without the above regression analysis, businesses cannot pinpoint where the loss in revenues lie. Here, a loss in revenues would be due to the fact that we are selling the wrong product to the wrong customer. It is not simply a matter of overall sales. Without knowing the cause behind lower revenues, a manager might decide to increase the sales of Product B to Customer B, without knowing that the opportunity cost of fewer targeted sales in other products is what is actually hurting revenues.Ultimately, it is a mistake to make a blind attempt at increasing overall sales. Business owners must discriminate in the combination of product and customer segments targeted to determine how to maximise overall revenues. For many business owners, persistently lower sales figures means that identifying the root cause is critical in order to remedy the problem. Predictive analytics is the ideal tool to do this, and is one that business managers increasingly cannot avoid.