We’ve all found ourselves in front of an online comparator at least once in our life, whether it was to choose the best offer for electricity and gas, the most convenient telephone operator, or the next flight for the summer vacation. In an increasingly complex world, online comparators provide consumers with all the information necessary to be able to clarify the sea of ​​offers and find only the one that best suits their needs.

It is no coincidence, in fact, that it is a sector in constant growth and increasingly competitive in which to be able to stay at the top of the market, in addition to selling advertising space on its site, each comparator must be able to continuously increase the sales made through their portal, from which they receive commissions for each new customer or a percentage of the value of the signed contract.

The success of their business is therefore based on the need to provide a service that can respond to the needs of consumers quickly and efficiently, to finalize the conversion of customers. To do this, online comparators rely on contact centers (internal or outsourced), a fundamental touchpoint with the consumer in the modern world, they do so to be able to maintain human contact and to customize the sales process according to the consumer’s needs.

However, this approach hides some problems, for example, users who request information about the offers on the portal are usually managed according to the FIFO logic, that is based on the chronological order in which they made the contact request.

Put simply: if I click on the “contact me” button immediately after 100 other people, I will be placed in position 101 in the recontact queue.

In this way, however, there is a strong risk that the users most interested in buying a certain product or service will remain waiting to be called back and cool off, this causes the comparator to lose numerous sales opportunities.

To be able to eliminate this problem it is necessary to analyse all the contact requests and order them according to the actual probability of concluding a purchase with each of them, to be able to proceed with their processing according to this order and to avoid that the contacts more inclined to purchase cool down, thus maximizing sales opportunities. Furthermore, knowing the user’s interest (let’s say for example for a new insurance policy), it would be possible to assign him to the most experienced operator in that subject, to make conversion even more probable and the contact more effective.

But how is it possible to obtain all this information from contact requests?

Using the Artificial Intelligence and real-time API of the BigProfiles Platform, it is possible to get hold of all the information needed in real time to set up the best response strategies to user requests and maximize conversion rates. All without the needing knowledge in terms of coding or data science.

Would you like to know more? Come and visit us on our website and request a free demo with one of our consultants!