Indoor Mapping and Machine Learning were two tools that more business use every day thanks to the potential to multiply sales while improving the user experience, giving each one what they really want. We’ll tell you.
When the advantages are more than the disadvantages, the perspective facing the challenge element changes. And this is precisely what has happened with all the solutions of Location Intelligence and Machine Learning (ML), whose combination brought other solutions, such as Indoor Mapping, which despite being in the development phase, have already proven to be highly effective for making good decisions and the best, to increase the results.
It is important not to lose perspective, the terminology can be complex and every day new technologies and tools emerge that try to help companies to improve the decision making based on quality information, which is what is known as Business Intelligence. Here are two big plans to optimize sales: Predictive services – ML- and location intelligence -indoor mapping-
On the other hand, predictive services allow monitoring past behaviors and predicting, as the name suggests, the evolution of certain variables based on them, providing quality information that helps documented decision making. They are directly related to Machine Learning and more data, the greater the efficiency, which is why it is especially important when working with Big Data and IoT.
Meanwhile, Location Intelligence is what allows locating geographically the exact location of the data, so thanks to the incorporation of geolocation to the data collection, we can get an even richer perspective and see where, when and why we should act from one way or another. Indoor mapping can be identified as part of location intelligence, but as its word indicates, location takes place on the inside.
Machine learning: algorithms that predict
Now that we have laid the foundations, we will focus in Machine Learning. In essence, the functionality it has is, as its name implies, allow the machine to learn from the data it collects. The greater the amount of data analyzed, the lower the percentage of error on the trends that are marking according to the variable.
It is precisely this prediction of future behavior, along with the rapid processing of information, which makes solutions based on Machine Learning to be essential for the growth of your business. Examples of companies that use it are Amazon, eBay and one that touches us much more closely, OneBook Shelf. In this case, we have built a product recommendation system that constantly learns from the interaction of users with the articles: we look for latent properties in the products factoring matrix with the SciPy stack of Python. The result of the recommendations is very fast, since all the operations are vectorial, the favorites of the computers.
What makes this mechanism so efficient?
A combination of factors such as speed, efficiency at low cost, dynamization of data, proven technology and minimum margin of error are some of the factors that explain its efficiency.
As you well know, companies generate incredibly large amounts of data, which until recently, was collected and the processing of them became a real problem, leading to static information that many times at the time of decision making, it was already obsolete.
This is why Machine Learning and the predictive services change the rules of the game, since the information that is collected, is organized, catalogs, analyzes and generates predictive patterns about the habits of customers. These results are often available in real time.
The time gained in obtaining results and the ability to react to them immediately are advantages that otherwise cannot be obtained. In addition, by knowing the patterns of consumption of our customers, their tastes and preferences, it will be easier to make, not massive, channeled offers, thus obtaining better response rates.
Through the Predictive Services, the ML is also able to offer advantages that are aimed at improving services and improving the customer experience such as fraud detection, spam detection, document classification (valuation of our services by users), the prediction of customer renewal and even the optimization of customer service.
Indoor Mapping: What goes behind doors, is no longer secret
Indoor Mapping is the procedure that allows us to position people and objects in closed environments. It is an incipient technology, which still requires the combination of different indoor geolocation systems to guarantee an optimum result. Thus, in the case of Geographica, we collaborate with SITUM as a solution that combines different positioning technology, optimizing results.
Although indoor mapping is about placing people and objects indoors, when combined with location intelligence tools – such as CARTO- and our smart dashboards, the results are incredibly satisfying.
Within a business and institutional environment, Indoor Mapping has a multitude of uses and functions. Here is a list with the most outstanding:
- The positioning of people.
- The calculation of faster routes between two points.
- The location of hot spots for sale.
- Line management.
- Customization of offers in real time.
Indoor Mapping and Machine Learning together they do not add, they multiply
When you join Machine Learning and Indoor Mapping, the only thing that can happen is that the commercial advantages increase: imagine having all your information geolocated, cataloged and processed in real time. Control and quality about decision making can be even greater, allowing you not only to increase your profits but to offer a better service. In addition offer your customers a much more personalized offer, in real time and with non-invasive strategies.
Very discreetly, Machine Learning has already been installed in many of our daily activities (physical and virtual), such as social networks, searchers, emails, banks, large online sales platforms, airports, hospitals and so on.
It is thanks to the ML that annoying spam goes directly to the unwanted mail tray, since an algorithm, according to the experience of the collected data, has classified it. The same goes for social networks when someone recommend contacts, with online sales platforms like Amazon when someone suggest products or, even more so, when banks detect unusual patterns of use and prevent possible fraud.
In fact, as users, some changes that we perceive as improvements have been thanks to the intervention of these two services. But the best that little by little you will see in the coming months how indoor mapping solutions also proliferate, especially in the retail sector.
We must thank Indoor Mapping that in environments with large concentrations of people such as airports or hospitals, the most vulnerable populations (such as children or patients) can always be reached. Also, not being subtle in more everyday situations like locating from the mobile the store or product we came to buy.
In more commercial terms, Indoor Mapping is very useful to establish which are the most strategic outlets, and not only in the mall but within the stores. This allows you to play with the positioning of items that are easier to sell at the points of least purchases (and vice versa), improving in that way the final results.
More so: this system can be so sophisticated that it is able to offer consumers real-time offers, according to the article they carry in their hands.
Among the cases of successful implementation of Indoor Mapping solutions, Geographica has Tempo, the prototype we are developing for El Corte Ingles.
As far as ML is concerned, OneBookShelf is the referent. For this company of digital content of the United States we developed a recommendations system for the users able to learn from the experience of their purchases that later could make recommendations accord to their tastes, increasing the possibilities of sale.
Definitely, Indoor Mapping and Machine Learning are the tools you need not only to grow your sales, but to provide the best service experience to your customers.