The Benefits of Predictive Customer Analytics
Customers are a key ingredient to the success of nearly any business in the market. Predictive customer analytics can help you to identify your most potential markets and their customers, to reach them through the most effective channels and, once you have acquired them, to offer them the best customer experience to keep them as your customer.
When the potential customer base is large and global, it is costly to search for means to target the right customer at the right price point with the right service and product features. There are also global competitors. Your business should try to acquire the markets and their prospects that are most likely to buy from you through the channels where they are most likely to be reached. You also need to retain existing customers.
Predictive customer analytics can greatly help companies in the entire customer life cycle: customer acquisition, upselling and cross-selling, customer experience, support and services, and customer retention.
Acquiring new customers and markets
Customer acquisition: predictive analytics helps to find which customers are most likely to buy your product and with each features, to determine their propensity. As an example, a company is able to select distinctive features of its product that would be a better fit for each different market where the product is to be launched, greatly increasing the chances of successful marketing campaigns and market acceptance.
Upselling and cross-selling: predictive analytics also helps with upselling and cross-selling by identifying which other products and services your customer has a high propensity to buy. You can sell a lot more by offering him these products. For example, association rules are used to determine which products and services are frequently bought together from previous transactions data so that they can be offered or recommended to customers in a bundle or with special discounts or simply after a purchase is made. The affinity of a certain service or product to a different service or product can also measured so that services or products that have a high affinity score can be offered together. You can then reach your customers with specific advertisements and offers.
In applying predictive customer analytics, a major first step is to identify use cases in the customer life cycle where analytics can be applied. Then to identify the data sources for the selected use cases (for example, visits to your website, previous purchases, preferences, demographics, product features per market). Once the data sources are defined, the data needs to engineered to be available to data scientists that will build models, evaluate them and finally make predictions that can have a major positive impact in your business.
