Look-alike targeting increases CTR by 3-5 times and response conversion rate at least twice. Group real users of the service you plan to promote and see how these people differ from the entire subscriber base or from another selected group. Create a portrait of a typical user based on these characteristics, and the system will find similar candidates for the advertising campaign so that each advertising touch gets a 100% response.

The data analyst manages the process independently without involving a DevOps engineer or Hadoop developer: imports and examines data, creates models for calculating new profile parameters using familiar Python, turns the model into production code "in one click", starts and stops the models on the cluster, controls the correctness of calculations in an interactive schedule and, if necessary, exports the results to BI.

In order for quality metrics not to "sink”, it is necessary to train analytical models in time. In MMP, the modeling cycle is four times faster: it takes 1-2 weeks to develop models instead of 1.5 - 2 months. The data analyst independently manages the process without involving a DevOps engineer and a Hadoop developer: imports and examines data, creates models for calculating new profile parameters in the usual Python, “in one click” turns the model into industrial code, starts and stops models on the cluster, monitors the correctness of calculations in an interactive schedule and, if necessary, exports the results to BI.

Terabytes of client data is a platinum asset only if your targeting is on top. The more precisely you select the parameters for segmentation, the higher the conversion rate of responses. With MMP, targeting of any complexity will take a few minutes — without special knowledge of databases and programming and without the involvement of analysts and BI specialists. Cut the base in different planes to create the most accurate segments. Save and copy rules or entire “branches” and don't worry about the same subscriber falling into different groups — contact policies dose the content.

The data analyst manages the process independently without involving a DevOps engineer or Hadoop developer: imports and examines data, creates models for calculating new profile parameters using familiar Python, turns the model into production code "in one click", starts and stops the models on the cluster, controls the correctness of calculations in an interactive schedule and, if necessary, exports the results to BI.

Millions of your subscribers are constantly generating events, each of which is a potential reason to sell services. MMP analyzes the actions and inaction of each subscriber and informs external systems about the best moment for the offer. Use pre-installed trigger processing scripts or create new ones. The platform has a constructor for creating triggers and a flow-based event designer with 50 drag&drop tools and mechanisms.

The functionality of the platform allows you to implement linked and multi-pass campaigns for continuous workflow. Take care of your customers — configure the scenario once, and the system will automatically “transfer” all target customers or those who showed the highest response from one campaign to another, delicately and timely offering the necessary services at the most appropriate moment.

When the campaign flow is created, content is generated, triggers are configured, limits are set, and contact policies are taken into account, it remains to approve the campaign before launching. Develop up-to-date rules for parallel or sequential approval in a separate ARM, assign reviewers to separate blocks and track which elements and by whom are approved, which are not, and which are rejected. Launch of each campaign under 100% control.

The platform supports dozens of communication channels: 4 push channels (SMS, USSD, IVR, Email), 38 pull channels, chatbots and social networks. Build a multi-channel logic of interaction, manage the movement of content across all channels centrally and create a continuous user experience for each subscriber. The system will analyze the volume of delivered content across all channels and determine the priority ones, as well as the type of notifications and relevant advertising content.