Owing to the steady competition in every business domain, companies are focusing more on winning new customers through customer acquisition, their existing subscribers fade into the background which is quickly followed by them becoming easy targets to be wooed away by the offers of competing companies.

Experts have found that it is much easier to reactivate dormant customers than to win new ones. Acquiring new customers can cost 6 to 7 times more than reactivating an old one and the inactive customers are likely to avail a product/service than people who have never bought it before.But the main problem associated with customer reactivation is who to reactivate? One of the ways to get the answer to this question is look-alike modeling. Now let’s try to understand what is look-alike modeling?

Look-alike modeling is an artificial intelligence technique which uses an ensemble of machine learning algorithms and big data analytics to find groups of people (audiences) who are similar to a set of customers with known behaviors.

 

Suppose you have a customer ‘A’ which generates highest revenue/profit for you. Now your company has to run marketing campaigns to reactivate inactive customers. You have a list of people (target audience) whom you have to target through the marketing campaign. If you use a look-alike model it will find the attributes of the customer ‘A’ (who is your ideal customer) like age, frequency of visits per year etc. and then it will match this with the attributes of all the people present in the target audience. This way it will find out the people in the target audience who are most similar to your customer ‘A’ and thus are likely to become your profit generating customers once reactivated. So basically you have just found the look-alike of a particular type of customer.

To know more about look-alike modeling Click here.

To understand the problem better let’s look at a real life case of a company which was suffering from the same problem. The company offered services like wealth management, broking equity etc. and had over 1 million consumer base. These were mainly retail customers of which almost 90% were inactive or dormant (customers who had stopped trading). There were many reasons for dormancy like-

• Dissatisfactory services being offered by the firm
• Huge losses incurred by some customers
• Poor guidance and support from the company representatives.
• Better offers from competing firms.

So the company wanted to focus on reactivating dormant customers rather than acquiring new customers. But before this it was important for them to find out which customers will be suitable for reactivation.

The Company solved the problem by unleashing the power of data analytics using look-alike modeling. In the past the company had run a reactivation campaign and had identified 561 people out of the customers reactivated after the campaign who generated the highest revenue and this happened in the month of January. In the month of February the client wanted to find people like those 561 people, so by running a look-alike model on new set of 30k people they were able to generate a list of 5.2k people who were most similar to the 561 people identified earlier. After they ran the campaign on the base found by the look-alike model (5.2k) they realized 80-90% revenue from that base. So with this they were able to cut down their marketing cost by 70% and found maximum revenue generating customers among that base (5.2k).

Check out here in detail how the securities & brokerage firm solved the reactivation problem.