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Determining Customers Predisposed to Purchasing

If category or market share are key initiatives for your organization, it’s likely that you are trying to determine your most promising sources of new revenue.  Companies can no longer devise plans for the year (based on traditional approaches such as 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 propensity to buy – that is which customers are most likely to purchase more of the same product or another product. Because it’s always easier to sell to existing customers, it makes sense to focus your initial data analytics on building models to help maximize your share of your customers’ (share of wallet) business. Cross selling and up selling are natural applications for these kinds models which are leverage predictive analytics, because an organization generally knows far more about current customers than it could possibly find out about external prospects.

Create Marketing Models

To address share of wallet, many organizations would like to know the following:
•    which customers are most likely to purchase a specific product (propensity to buy models)
•    what is the next product a customer is likely to buy (next most likely product model)
•    which customers are likely to defect or reduce expenditures (attrition predictor models)

All these models use data analytics. Data analytics can help improve your bottom line by increasing your revenue and growth. These models require having historical data and measuring past performance of the enterprise regarding offerings and customer activities so that you can effectively deploy 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 that are more profitable.

Building these models requires all the usual data cleansing, transformation, initial and ongoing validation etc. As with all model building, the key challenges are defining a customer, extracting quality data and analytical and modeling skills.  These models require data related to firmographics, purchase behavior, contact types and number, current marketing interactions, and macroeconomic data.  The data is used to create a model that essentially scores each customer. The score represents the likelihood the customer will want to purchase. The top-scoring products for each customer become the customer’s best next offers.

Build your Propensity to Purchase model.

Use Data to Create Customer-Centric Models

Propensity-to-Purchase Models

Traditional propensity-to-buy models score customers based on their similarity to past purchases. However, history is not always a predictor of the future. 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.

Feeding real-time data to the propensity-to-buy models and injecting predictive analytics functionality, such as decision trees, are important for effectively using the model.  It is also important to have both a treatment and a control group to see what response you get. This allows you to identify the buyers in both groups and then evaluate what kind of people did not buy in the control group but did buy in the treatment group – these are those who only purchased because of the treatment. From this you can build the propensity to influence model. Need help, let’s talk about how you can tap our expertise.

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