Data Analytics

Sales forecasting is used in most businesses for planning and budgeting purposes. It is often the start point in the business planning cycle, and determines the various components of functional activities for the following year.

In B2B businesses a pipeline approach is often used for sales forecasting. This entails accounting for the relatively long sales cycles in these businesses and arbitrarily assigning the likelihood of a deal in the pipeline closing by a certain time. The usual method is for a group of experts in the business allocating a % chance, or probability of a deal closing.

Depending on the nature of the products sold and markets the B2B business serves, it is often essential to understand the impact of the longer term trend and seasonality in the historical product volumes and revenues. The impact of analyzing this data through Time Series methods is to be able to predict the seasonal impact of strong and weak sales months as well as growth and declining trends. This in turn enables the business to understand how segments within a product portfolio are impacting the aggregate trends in the overall portfolio. Very often these forecasts can also be benchmarked against peer group competitors in the industry.

Marketing is constantly faced with the challenge of coping with increasing number of competing products, differing customer preferences and the variety of channels available to interact with each consumer. Predictive modeling can help identify consumers with high likelihood of responding to a given marketing offer. The predictive models are built with data from consumers past purchasing history and response rate for each channel. Additional demographic, geographic and other characteristics can produce more sophisticated predictive models. Targeting only these customers can lead to substantial increase in response rates which result in significant reduction in cost per customer acquisition. Apart from identifying target prospects, predictive modeling can also identify the most effective combination of products and marketing channels that need to be used to target a given consumer.

With heightened competitive activity, businesses need to focus efforts on maintaining customer satisfaction. In such competitive markets, consumer loyalty need to be rewarded and customer attrition minimized. Businesses tend to react to customer attrition in a reactive way, acting only after the customer has initiated the process to terminate service. At this stage the customer is almost always lost. Proper use of predictive modeling can lead to a more proactive retention strategy.

By frequent analysis of a customer’s spending and other behavior patterns, predictive models can determine the likelihood of a customer wanting to terminate his relationship in the near future. Intervention with the appropriate offers can often retain valuable customers. Predictive analytics can also predict behavior that imply silent attrition and therefore allow the business to increase customer activity.

Often organizations collect and maintain customer databases including transaction data. This is seldom utilized to identify lost revenue opportunities through cross-sell and up-sell. When the organization offers multiple products to existing customers, analysis of product associations and multiple product ownership, enables the identification of “next best product” to sell to individual customers. Thus increasing revenue and profitability.

All portfolios have delinquent customers who don’t pay on time. The financial institution has to undertake collection activities on these customers to recover the amount due. A lot of collection resources are wasted on customers who are difficult, or impossible to recover. Predictive analytics can help optimize the allocation of collection resources by identifying customers to contact in a prioritized way and then assign appropriate contact strategies tailored to each customer thus containing further delinquency and losses.

Today fraud is a significant problem for many different types of businesses. Inaccurate credit applications, fraudulent transactions, identify thefts, and false insurance claims are some examples of this problem. These types of problems plague credit card issuers, insurance companies, retail merchants, manufacturers and business-to-business suppliers. This is an area where predictive models are often used to weed out the failures and reduce business exposure to fraud.