Customer segmentation and churn analysis

If you manage a large customer base one of the KPIs to monitor is what your churn rate is and how you can lower it as much as possible. Whether you are a telecom provider, web app service or some utility type of service, the question of keeping your customer base is a very important one. There are several elements that we believe should be addressed with regards to that question:

• A proper churn rate calculation needs to be performed to estimate churn for the respective categories of services and individual services in question, taking into account service migrations, customer growth rate, as well as other relevant factors.
• A predictive analytical model can be built to estimate the probability of churn based on customer characteristics. This will allow you to address the high-risk customers.
• A forecasting analytical model can be created to assess potential impact on churn based on various scenarios (e.g. price changes). This can be used as a decision making tool to improve your top as well as bottom line.

Overall, we believe that, in today’s market of increasing competitiveness and need for differentiation, analytical tools can be of substantial help in keeping and growing your customer base.

Predictive analytical solutions

There is a large set of problems in business and operations that require predicting a certain key parameter or set of parameters, that have key effect on your top or bottom line. Depending on your business, it could be planning your marketing campaigns, or maintaining your high-value equipment working, or perhaps detecting fraudulent behaviour by some of your customers. In all these cases even small improvements can lead to substantial benefits for the company. While experience and knowledge have traditionally been used to address all of them, more and more analytical tools are used for that purpose. By using predictive analytics a data driven decision can be made in all such cases, where substantial data exists, on which algorithms can be trained effectively. Analytical tools offer a clear way to unlock the data, which you have probably been storing for years and apply its wisdom directly to your day-to-day operations – in conjunction with the knowledge and experience that your team already has.

Data analytics for IoT

IoT generates a large amount of data – sensor data, behaviour data, location data, etc. While this data is usually stored for the long run, it is in its proper use that its value can be realized. And its uses can be various depending on your IoT project and the key issues you need to solve. One such issue could be to address overall operational planning (e.g. do predictive maintenance). In this case more traditional analytical models could be applied. The issue at hand could be however that you need to make almost real time decisions based on your sensor information (e.g. automated vehicles). In this case the value of your information has an extremely short lifespan and you need to employ proper ML algorithms to make that information used in that very short time. Of course training of the models should be based on a very large dataset, but your final model needs to be able to do split second decisions and monitor their implementation on the fly. Whatever your IoT project is, if you have large volumes of data generated, analytical tools can be extremely helpful in turning that data into actions.

Custom data analytics

Do you have a problem that you are struggling to solve in the traditional ways (human knowledge + experience)? Do you have enough volume of reliable historical data for the problem? If so, we may be able to help you by building a completely custom analytical model for your specific case, so that you can turn data into decisions.