Taking the customers understanding to the next level

Our clients:

Large and medium businesses with a wide product mix, for example, hardware stores, ticket offices, banks, online courses.

What we do:

We combine information about the client and his environment from open sources with information about the history of purchases. We build predictive models to tell which products should be offered to specific customers.

Benefits:

Increase of business profitability by increasing the efficiency of work with clients. The growth of loyalty among customers.

Details:

At the moment, companies can qualitatively analyze about 10% of their customers who make a large number of purchases. For customers who have made only a few purchases, it is impossible to make high-quality predictors without using external data. For this reason, we combine our data with the purchase history to work more effectively with the remaining 90% of customers. As a result, on the pooled data, we teach predictive models and suggest specific products for specific customers.

The format of the results:

At the first stage, we build a model which takes into account the product line of a specific customer. At the stage of use, data for each customer is provided in real time by api.

In addition, we can:

1. Improve your decision-making systems: the more data, you have, the more accurate forecasts you can make.
2. Create up-sell and next-sell models
3. Build the most effective sales funnels for different types of clients: one type of clients loves bright pictures and short slogans, while others pay attention to clear characteristics or prefer personal communication; we will define such types among your clients and provide the argumentation of our conclusions.

Our clients:

Public organizations, government agencies.

What we do:

We study the available information from open sources about the target audience: reviews, comments, interests, locations, likes, actions, and others to impose it on the information space.

Details:

We analyze digital traces of the desired target audience in open sources considering information which is only available to everyone. With the help of machine learning algorithms, we clear and structure information. We use our own neural networks and other algorithms for cleaning and structuring textual information. For each task, we develop a set of analytical tools, which allows us to obtain the maximum number of insights.

The format of the results:

Access to a personal account with large number of analytical tools. Information in your account is updated regularly.

Our clients:

Banks, insurance companies, retail, online business.

The aim of the analysis:

To understand why some people buy a particular product, while others do not buy it, you need to understand what type of people made the target action and analyze in detail how they differ from all other customers. Internal data of the company is not enough to determine the type of clients. Our solution is to analyze digital traces of the desired target audience in open sources: reviews, comments, interests, locations, likes, actions, and to compare them with internal data.

Benefits:

Our approach to the analysis allows us to achieve the set KPI to attract new customers, as well as the work with existing customers predictable and most effective.

Details:

We collect information from open sources; using machine learning algorithms, we clean and structure it. We create predictors for describing the target audience: demographics, interests, opinion leaders, mass media, and many others. We describe a given audience with the help of these predictors and define the way it differs from other clients.

The format of the results:

1. Onboarding. In a form of presentation, we show how to maximize the benefits of analytics for your business.
2. Access to a personal account in which you will have the opportunity to track all the changes in real-time and build the necessary graphs in the different sections.