Greg would like to thank Soumendra Mohanty for his contributions to this month’s column.

One sector that’s ripe with benefits to be yielded today is customer analytics. This is especially true with the opportunity to use data analytics to increase share of wallet - maximizing your market share and the percentage of the available customer dollars. Data analytics can help improve your bottom line by increasing your sales.

The goal is to increase market share for your product or service. Most enterprises typically favor three distinct approaches to do this: expand business with existing customers, enter new markets and increase penetration of existing markets. To choose which actions to take, a business must first understand the sales potential within each approach and become predictive about their outcomes.

Because it’s always easier to sell to those who already know you, increasing sales to existing clients certainly makes the most sense. There is also an economic incentive. A bank could typically spend about $350 to attract each new customer. Instead, selling additional products and/or services to the existing customer base could require virtually zero new spend.

However, that’s not always a slam dunk. Having a customer who is satisfied with your bank does not mean he or she will necessarily be interested in all your product offerings. The customer might be very pleased with the bank for basic products, such as checking and savings. However, that same customer may never consider using the bank for investment activities for any number of reasons, including that the customer is unaware such services are available, doesn’t perceive that service being of sufficiently high quality or receives that service somewhere else. As a result, this becomes a lost opportunity for your bank.

If companies expect to grow market share, they must begin by determining the location of the most promising sources of new revenue. This means employing two measures: share of wallet of existing customers and market potential of prospective ones. Measuring share of wallet allows an enterprise to assess its ability to capture more business from its existing customers. This is based on historical data and measuring past performance of the enterprise regarding offerings and customer activities. In contrast, being predictive about the share of wallet and monitoring it continuously enables the enterprise to effectively use cross-selling and up-selling techniques.

Cross selling involves getting existing customers to purchase additional services. Up selling refers to convincing these same customers to trade up to products more profitable for the vendor. Companies can no longer devise plans for the year (based on traditional approaches like segmenting and clustering) and then sit back and relax. Today, businesses must seek to maximize the potential of these strategies by determining each customer’s best next offer - the offer that is most likely to elicit a positive response from that individual.

Successful cross selling/up selling requires more than having an array of attractive products. The successful cross seller needs to know what specific products it should offer to whom and how predictive the outcome will be.

Cross selling and up selling are natural applications for predictive analytics, because the enterprise generally knows far more about current customers than it could possibly find out about external prospects. Furthermore, the information gathered about customers in the course of normal business operations tends to be much more reliable than the data on external prospects that can be purchased.

One recommended approach to sucessfully cross/up sell is to develop individual propensity-to-buy predictive analytics models for each product or offering. These models then need to be combined to create a set of best next offers for each customer. Each customer gets a set of scores, which represent the likelihood the customer will want to purchase each product. The top-scoring products for each customer become the customer’s best next offers.

The traditional propensity-to-buy model scores customers based on their similarity to past purchases. Yet, past purchases may look different now from how they looked when they were purchased. Going back to the bank scenario, certificates of deposit (CDs) provide a good example. Customers who own CDs probably do not have large balances lying around in their ordinary savings accounts. This does not necessarily mean the bank should look for CD prospects among those customers with low savings balances. Prior to purchasing a CD, the customer must have had the purchase price available somewhere else, probably in a savings account. The solution is to build propensity-to-buy models based on the way purchasers look just before they purchase the product and then leverage the analysis to predict outcomes of prospects.

The propensity-to-buy models are based primarily on historical data and may or may not provide additional decision-making ability to customer service representatives (CSRs) as they experience customer behavior in real time. Feeding real-time data to the propensity-to-buy models and injecting predictive analytics functionality, such as decision trees, will enable the CSRs to employ cross-sell or up-sell techniques effectively, thereby capturing greater share of wallet. No matter how wonderful your product or service may be, potential customers have a limited disposable income. As their personal income and economies might tighten, the importance of selling the correct new offering to your customer becomes that much more important.


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