Qualified lead generation for new customer acquisition using AI

Fernando Pavón

CEO of Gamco

Today we are going to talk about the generation of qualified leads for the acquisition of new customers through AI.

At Gamco we develop software based on Artificial Intelligence for advanced risk management and business optimization. In recent years we have deployed our solutions in large corporations, being able to analyze huge amounts of data. Thanks to this, today we have been able to package these solutions so that small and medium-sized enterprises (SMEs) can also take advantage of the benefits of Artificial Intelligence.

How does AI help in the generation of qualified leads? 

In this case, the client had a clear objective: to double the number of members. They wanted to have them in their membership. Based on this approach, what made it more difficult to reach this goal was to identify and find potential customers that met the target objective. They shared these thoughts with us and we began the challenge.

The challenge was to generate qualified leads, which from a commercial point of view, would not be the typical lead that you buy in a database. But that Once identified, it was necessary to sort them by equal characteristics and create different segments of potential customers in order to know and act on their particular needs. In addition, it had to be easily interpretable for commercial management. In this way, the person in charge of processing this data could have a priority order, dedicated to the generation of trust and rapprochement with customers. 

If you want to go deeper, we recommend our article: What is machine learning?

The phases for the generation of qualified leads

The first fundamental step is be able to obtain information from different data sources. On the one hand, we have the ERP, with the data of current partners. And the CRM, with contact data generated mainly through events and news subscriptions. Considering the primary databases, the first important step is to filter, merge and prepare this information for machine learning. 

Once we have the data correctly extracted and normalized for useWe identified a number of potential companies for which, logically, we do not have much prior data. We resort to the search of information in external and public data services through the Internet. In this way, we enrich the information of the potential client, but also that of the current client. Once we have enriched this information, it is create a series of predictive models. That is to say, a knowledge learning structure, where, on the one hand, the current customer is compared and on the other hand, the segment of potential customers that are similar to this one is identified, according to the sector in which it is working, the field of action, economic activity...

Thus, we move on to the customer segmentation. At this point similar ones are compared with each other, to finally quantify and prioritize each of these customers in two ways. On the one hand, the probability of conversion, but on the other hand, being able to see which customer is interesting for the business.

The penultimate step is present this information through a web interface dynamic and easily understandable for business managers. Through this web portal, the user will see a list ordered by probability of conversion of customers to contact, collecting feedback from each contact. There are a series of commercial steps, according to the funnel defined by the commercial activity. In this step, information is retained and fed back to the system.

Finally, the calculate performance reports and return on investment. These systems have a very important return but you have to show it for the business. There are four steps: 

  1. Merge data from different sources of information and enrich that data with new sources of information through the web.
  2. Predictive modeling: machine learning in different layers.
  3. Simple and easy presentation to the commercial.
  4. Performance reports and return calculation.

Are you getting results with qualified lead generation?

We can say that the customer confirms that he is fully satisfied in two respects. First of all, in the results that have to do with the monetization point of view, since the investment is amortized and is already in terms of profitability. Secondly, they wanted the results were perfectly explainableThey considered it very important to have a commitment on the ethical use of algorithms (as recommended by the European Commission) and Gamco did so.

In addition, we consider it essential to see the people we work with satisfied.. There is often a barrier with employees. They think that our AI systems are going to take away their jobs, and what we really do is encourage, unleash and leverage talent where the machine can't reach.

In conclusion, we would like to emphasize that artificial intelligence has the capacity to carry out the generation of qualified leads, The products are truly ordered and ready to be exploited by the different sales channels.

If you have found it interesting, we leave you with GAMCO's participation in the Company and Society event held on June 17. In this video, our CEO Fernando Pavón, will explain everything we have seen in the article about the generation of qualified data Do not miss it!

If you want to go deeper, we recommend our article: What is machine learning?

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