Using AI to Target Consumers More PreciselyBy Jan Hogewoning | Thu, 04/16/2020 - 10:58
Q: What opportunities does Mexico offer that make it an attractive market for your services?
A: In terms of technology, Mexico still lags behind the US. However, there are many large national and international companies in Mexico, which makes it attractive to enter. We have a great deal of experience working with banks, but also in the food and beverage sector. Potential clients here could be Grupo Femsa, PepsiCo and Procter & Gamble. Many of the companies we have been working with in the US also have a presence in Mexico, which would smoothen our potential collaboration. Because there are a few large companies that dominate certain sectors, it would be relatively easy to capture a significant share of the market quite rapidly. Other potential clients include large educational institutions, such as universities.
Q: There are other companies in Mexico which offer data analytics services. What distinguishes you from competitors?
A: There are various companies active in Mexico that work with databases, both national and international. What makes us unique is our application of artificial intelligence to create predictive models of consumer behavior. Our models use various methods including random forests, neural networks and deep learning. We can crunch data faster than our competitors, and this data can be used to make fast and effective decisions with regards to commercial strategy. There is a great deal of ongoing growth in the area of artificial intelligence; however, we have a proven track record of 100 percent satisfaction with our clients. They have seen their returns increase by 25 percent and above.
Q: Could you mention an example of how your predictive information could be applied here in Mexico?
A: Large banks operate across different sectors and offer services to different segments of the population. Many banks still run conventional marketing campaigns where they target large heterogenous groups of people with relatively ineffective tools, such as email. They do not know exactly which client they are reaching and they have little certainty on whether this client will purchase the product. Our predictive models allow a determination of which product is best for which type of client, when it should be offered, in what form and through which channel. A younger person is not going to have the same motivations to get a credit card as an older person with a family. We help banks to personalize their offer, and send the right sales pitch at the right moment. For example, a younger person who travels a lot could receive a credit card with a promotion for travel points. The channel could be a personalized text message, sent between 8-10 p.m. when the person is highly likely to open the message. The impact of this personalized approach will be reflected in the commercial results.
Q: How do you position yourself against the competition?
A: Our track record helps us. Our results in the US, Canada, and now Brazil are very strong. Some of the companies we are working with are in the top of the Fortune 500. One challenge for companies in developing their own tech teams is that it takes a lot of time and requires significant investment. This makes a collaboration with us more attractive, as it saves time and provides a proven service. There is also a cultural obstacle, as very good data scientists are increasingly choosing to bypass working at traditional corporations to work autonomously. We can help banks to adopt the tech tools they need while at the same time saving significant financial costs.
Q: Focusing more specifically on the retail sector, what needs to be done to push the usage of technology?
A: I think the greatest impact on the sector will continue to come from players like Mercado Libre and Amazon, which continue to grow. Mexico has one problem, which is that e-commerce sales still remains a relatively small share of overall sales. One of the reasons is because a large group of people do not have access to digital payment methods. In the area of fast-moving consumer goods, I believe the potential is not necessarily in e-commerce, but in bringing improvements in the traditional sales channels. To take Coca-Cola as an example, there can be far greater analysis of the performance of points of sale. If you analyze the points of sale nationally, you can see which need to improve their inventory to meet consumer demand. We can then suggest discounts to distributors that need to stock more Coca-Cola at certain points in time. At the same time, the distributors themselves can adapt their own promotions to sell the product.
Q: How can Mexico develop more technological platforms and tools with added value?
A: I believe what is missing are people who take risks and execute their plans. There may be technological hubs in several parts of the country, but in the end the main service there tends to be forms of outsourcing. Mexico needs to create its own NuBank, Rappi or Uber. It is sad that Mexico has not produced such an example yet. The country has good engineers and there has been a lot of investment in education, but there is a greater need for critical thinking and risk-taking.
My wish would be to see someone from a less economically privileged background looking at their surroundings and coming up with an idea to solve a particular problem. They then develop that idea and venture capital players step in to fund. This kind of entrepreneur, however, does not really exist. The system tells these kids to continue doing their regular job. Many startups have been started by more privileged people who, after having gone abroad to do a master’s, decided to create a Mexican version of an existing company. Because these people remain the main source for startups, there is a bias in the sector that slows down creativity. We need to stimulate greater groups of people to think critically, and then support them with the means to succeed.
Fligoo is an advanced data analytics company based in San Francisco. It works with a range of companies in the US, Canada and Brazil. The company uses tools such as artificial intelligence, machine learning and Big Data to provide predictive information on consumer behavior and optimize commercial strategies