The relationship between a consumer and a company does not end at the moment of conversion, rather, it can be said that this moment is only the beginning. From the company’s efforts to try to acquire new customers, we move on to all those activities and strategies that need to be implemented to keep the newly acquired customer tied to the brand and to increase their Customer Lifetime Value, by trying to sell them complementary products that meet their needs.
We have already looked at strategies to retain our customers here whereas, in this article, we will focus on how a company should behave in order to make the most of its Customer Base by making sales after the initial conversion of the consumer.
We will therefore be dealing with Cross-Selling, which is, the sale of a product or service complementary to the one that the customer has already purchased from the company.
Usually, cross-selling campaigns are managed by BPOs, who receive a list of contacts coming directly from the CRM of the client company with the aim of making as many sales as possible in an attempt to maximize the revenue from the Customer Base.
Let’s look at an example: every month a Call Center receives from a bank a list of 100,000 prospects to contact in order to sell a new product to its current account holders. Normally, the processing of this list takes place randomly, that is, by proceeding to the contact the prospects in the list without a basic strategy, but simply hoping for the luck to contact those profiles that are actually interested in buying the new product.
In order to optimize its sales campaign and increase conversion rates, the BPO would need to analyse the profiles on the list in advance, so as to make targeted contacts and eliminate, or at least reduce the use of resources on customers who are less interested and who are very difficult to convert.
To be able to carry out this preliminary study of the lists it is necessary to equip oneself with technologies able to predict the purchase probability of the people who will be contacted. One of these is Artificial Intelligence, with which BigProfiles has created an ideal platform for this type of sales strategy.
Using BigProfiles, it becomes possible to analyse the conversion history on the bank’s Customer Base and infer from it which customers are most likely to purchase a new product, in order to know who to proceed to contact.
Returning to the previous example, the BPO, needing to make 100,000 contacts per month, could ask the bank for 130,000 prospects, or 30% more, analyse the the full list they have obtained and then keep the 100,000 most likely to purchase that product, while giving back to the bank the 30,000 with the lowest chance of converting.
By doing this, the BPO is able to increase sales by 30% without increasing the number of operators or contacts made for processing the list.
If you want to learn more, visit us on bigprofiles.com, or request a demo.