Today we are used to receiving products and services recommendations from certain companies and we usually think these recommendations are made specially for of us. We believe products ads sent to us are personalized, targeted to each person, however, it isn´t quite true.
For most people, personalization and recommendation are the same thing. When a company adapts its services to meet customers´ needs, it can use recommendations to personalize their purchases, although these words may sound like synonyms, they aren´t.
The technological firm´s first approach to what constitutes a personalization engine is defined in its Gartner´s guide for Digital Personalization Engines in 2015 as:
A process that creates a relevant and individualized interaction between two parties designed to improve the customer´s experience. It uses information based on the recipient´s personal and behavioral data as well as behavioral data of similar people to offer an experience to meet specific needs and preferences.
Therefore, personalization engines are solution providers for the personalization process. They determine and provide the customer experience to deliver personalized messages through digital channels (emails, text, automatic notification) and provide analysis and reports to the business user.
Whereas product or content recommendations, involve a small selection of items in the catalogue sent to the user in a variety of ways (banners, messages, recommendation tables), in other words, a recommendation is more like a presentation of items.
However, a recommendation is a form of personalization, but a personalization is not a form of recommendation. Let´s take YouTube for example, Google video platform can suggest related videos based on users previous viewing habits. A recommendation would also be based on what other YouTubers watched. Hence, Facebook personalizes the news feed of your profile based on the activity of your friends but, does not recommend one or the other. This is personalization, as it is based on the individual´s specific habits rather than a general algorithm. The more you know about a person, the better, not only about videos habits. In other words, a recommendation is usually based on elements whereas a personalization is based on individuals.
Therefore, how can we talk about personalized recommendation systems when one refers to a product and the other to a person? Simple: incorporating the behavioral habits of the person in the recommendation engine. The more you know about a customer, the more personalized the products recommendation will be.
Today, there are only a few companies that can really personalize their behavioral marketing strategies as it is necessary to implement not only an algorithm for the recommendation, but also to incorporate all the customers and non-customers data. This is the challenge. Let´s give an example to explain how to overcome this.
Behavioral Marketing in banking
We know how the banking sector is moving ahead at a staggering speed to meet customers´ needs. Gone are the days when financial products ads were targeted to everybody regardless of their personal conditions causing many bad headaches.
Today, the sales and marketing departments of banks need to know better their customers to be able to offer them what they really need at the right time. Banking institutions are using a strategy consisting of incorporating the user´s browsing data.
Behavioral Marketing involves the sales, commercial and ads activities based on the customer´s behavior patterns obtained through cookies, social networks, web analytics, browsing and purchasing history and IPs. In this case, cookies collect anonymous information of browsing habits aiming at targeting ads according to their interests.
Therefore, Behavioral Marketing allows for ads customization based users profile and avoid sending ads to customers which will end up as spammers.
Consequently, when a customer accesses a bank web page it leaves a trace like a “bread trail” showing the yellow brick road of what, how long and what part of the web it has seen, whether it has taken any action like checking its credit. At the same time, if we incorporate all the available information of this customer (browsing activity, purchasing history, IP address, social networks, app data, etc.) we get knowledge of its propensity to certain banking products.
Keeping in mind the user identity will allow for customers characterization and their relationship with the institution through a series of variables that will describe more precisely each user. Using browsing habits as part of the user’s characterization will allow for a broader vision and therefore improve personalization of products recommendation.
Deep Learning and Machine Learning techniques allow to incorporate user´s data which could enhance the personalization model and would allow us to know the propensity of a specific person to buy a certain product. In other words, better conversion rates of their sales departments in their products offers.