When I open any website offering services or goods, I always check how well a recommender system works.  It’s my personal marketing thing, tic, and oddity. Whatever you call it. Big business also adores recommender engines as much as I do, so I am in good company.

“Recommender engines or recommenders, as they are sometimes called, are the most useful applications of Machine Learning Algorithms.” – Harvard Business Review.

No matter whether I am choosing Netflix movies or ordering delivery from a local grocery, I want personal recommendations:

Recommender engines help me to choose among millions of tracks on Spotify: 

And they help me to choose another plant to the disappointment of my husband ( “One more plant? We have a dozen of them already!”).

Each time I notice accurate and pleasant recommendations, the marketer in me, the one who strongly believes that bringing value to clients is the key to success, is pleased. Extremely pleased.

Why?

Recommenders are like gifts from best friends who know you well enough to take into account your tastes insead of their own.

What are other benefits of recommender engines for clients?

1. In all the 3 cases described above, recommendation engines (Spotify, Netflix, Epicentr K) saved me a lot of time. The more  I use them, the more accurate the recommendations become. Next time when I aim to buy a plant ( “Another one!”) or choose music, I have useful hints.

2. As a busy customer, and that may sound like a total coming out, I don’t always know what I want. I ask friends about good films or books, post on Facebook questions like “where should I eat in Split?” and ask about the brand name of a grill I am about to buy. I am in doubt and anxious to buy something good.

I adore personal recommendations. We all do. They are based on someone else’s mistakes.

3. So, in many cases, recommender engines help me to choose the things I need, offering the most popular foods, services that are often worth attention. Simply because they are checked by other people. And, no, I don’t believe that I am lured into buying things I don’t need because of some gross manipulations. I am immune to ads, and it’s almost impossible to make me buy things that I was not planning to buy in the first place, thanks to strict budgeting habits.

The more I listen to Spotify, the better music it offers to me, and consequently, the more I like it. The more shopping I do on one of the popular marketplaces, the better goods I see in my recommendation.  According to this article from towardsdatascience, Spotify uses collaborative filtering, Natural Language Processing algorithms to analyze lyrics, comments, to make predictions of what I may enjoy.

Spotify analysts claim that 40% of the time, users are not sure what they want to listen to.

That was the confession of the user. Now, let’s take a look at the real stuff: business value. I don’t view recommender engines as a marketing gimmick, rather as a tool, a serious business tool for serious purposes.

For businesses it is important to get the main idea: recommender engines are not the examples of the tactics that will help to sell more, they are a resource of never – ending insight resources compared to intuition and competitiors monitoring.

What is the real business value of recommender engines?

Recommenders can be a starting point for building a real “data culture” inside the company, the culture that will help to make decisions on the basis of knowledge (not the intuition of a marketing director or the gut feeling of the main investor of the company).  Until companies realize that they need to analyze the data that is sometimes scattered between different systems, they will miss the goal.

Recommender engines look a bit complicated at the moment, but marketers need to remember principles on which recommender engines are based: collaborative filtering and content-based filtering.

All in all, 38% of all AI solutions in businesses are dedicated to building algorithms that can find patterns in big volumes of data and use this knowledge to predict what a particular customer likes. Having this knowledge, e-commerce businesses can offer more relevant and worthy goods and services to their customers and increase loyalty.

Using modern capabilities of AI, it is possible to reach the heart of the consumer:

1. To build recommendations dynamically filling the slots on the basis of analysis of browsing and purchasing history;

2. Build lists and sort pages based on the popularity rate of products/services;

3. Show personalized search results;

4. Show recommendations for special events, like Halloween or Easter ( don’t forget to buy eggs!);

5. Show recommendations that can complete your order in some sense. You were buying 3 types of goat cheese. Why not offer a good wine that matches all those cheeses?

After all, we all like buying things but hate when someone sells us stuff. Recommender engines become the golden mean between being sold and pleasant shopping.

This article is republished from hackernoon.com by Olga Teslenko.