Information on the organization's IT activities

Full name of the organization: DATA WAVE Limited Liability Company

TIN (INN): 7708368316

About Datawave

Datawave is a high-tech tool for boosting sales and service efficiency. The platform aggregates and analyzes various types of customer data from multiple sources, which the system then uses to build a single customer profile.

Datawave is listed in the Russian Software Registry

Datawave is listed in the Russian Software Registry under registry entry No. 16068 dated December 23, 2022. The entry was made pursuant to the instruction of the Ministry of Digital Development, Communications and Mass Media of the Russian Federation dated December 23, 2022, based on the minutes of the expert council meeting dated December 19, 2022, No. 2088pr.

Datawave is listed in the registry of IT companies

Datawave is listed in the registry of IT companies under the decision
on granting state accreditation to an organization operating in the field of information technology, dated February 1, 2024, No. AO-20240131-16393862932-3

Software description

This software is program code whose purpose is the collection and aggregation of user information, including communication with the user.

Objectives:

• Communication
• Analysis of user actions after communication
• Communication planning
• Provision of components

Purpose:

• Communicate with users via chatbots, social networks, messaging apps and push messages.
• Create recommendations for products, services and rate plans on the website, in the mobile app and in partner integrations.
• Analyze behavior and predict customer needs based on ML models.
• Create your own personalized loyalty programs, promotions, bonus systems and gamification of the interaction process.
• Build offers personalized for each customer: product, price, timing and bonus
• Enrich data on customers and their needs, including through third-party DMP providers.

Key software functions

Main functions

• Receiving audience data from multiple sources.
• Managing customer identity data.
• Supporting real-time customer segmentation.
• Providing customer data to other systems.

Data ingestion. CDPs extract customer data from multiple sources (for example, transactional systems, online behavior, call center, demographic data or POS).

Identity management. CDPs resolve customer identities across multiple channels, most of them using deterministic matching and profile stitching with identifiers provided in the incoming data. A CDP creates and maintains a persistent customer identifier.

Segmentation. CDPs enable marketers to create and maintain universal omnichannel audience segmentation using unified data in the CDP.

Data delivery / activation. CDPs facilitate the activation of information and of the single customer profile created in the CDP by providing connectors and APIs for other marketing technologies.

Technologies and programming languages used to implement the Software/Services

Since the principles of how customer data technologies work are by now well established in the market, we have identified the system principles that matter to the client:

•Technology popularity: demand among other companies

•Technology prevalence: the number of professionals on the labor market skilled in using it

•Language development and support: long-term support.

The project is built on a microservice architecture, which was chosen to enable continuous improvement of the system's modules:

•Security and ease of testing

•Ability to improve the system without changing its core structure

•Ability to use various technologies: flexibility of support.

The following technologies and programming languages are used to implement the Software/Services:

1. ClickHouse: a system for building analytics. ClickHouse responds very quickly to analytical queries on data updated in real time, so it can be used in interactive applications. The system can scale to tens of trillions of records and petabytes of data. Using ClickHouse opens up possibilities that were previously hard even to imagine: it is possible to store the entire data stream without pre-aggregation and quickly get reports broken down by any dimension.
2. PostgreSQL: a powerful open-source object-relational database system with more than 35 years of active development that has earned a strong reputation for reliability, feature robustness and performance.
3. Redis: a server-side data structure store. It is used as a cache in front of the “real” PostgreSQL database. A Redis-based cache helps improve performance. It makes efficient use of the speed of in-memory data access and eases the data-processing load on the application's central database.
4. MongoDB: in MongoDB, data is stored in unstructured form, as special documents. It is used for working with non-relational databases.
5. React: a system for simplifying the process of building large applications. React is geared toward a component-based approach to development. React addresses application performance, making the application faster and less resource-intensive through the use of a virtual DOM.
6. Python: used for back-end development. It is in high demand among company employees and is a popular language among the target audience, as well as among analysts in companies' R&D departments

Detailed description of platform modules and their characteristics

Module: customers

Module objective:

Searching for and selecting users for communication

Features:

1. Searching for customers in the database
2. Creating selections from the customer database
3. Viewing customer data
4. Displaying data on an individual customer
5. Displaying available technical data on customers
6. Creating customer selections:
   1. Dynamic selection: a customer selection whose membership is updated over time
   1. Static selection: a fixed selection

Module: visitors

Module objective:

Searching for and selecting users who are not logged in to the systems (mobile app, website) for communication

Features:

1. Displaying information on unregistered customers
2. Displaying customer session data

Module: customer segments

Module objective:

Storing user lists for communications

Features:

1. Viewing saved segments
2. Editing saved segments
3. Deleting saved segments

Module: A/B tests

Module objective:

Conducting various test studies

Features:

1. Testing interfaces
2. Offers
3. Messages in communication channels

Module: surveys

Module objective:

Conducting research and collecting data among users

• Qualitative
• Quantitative

Features:

1. Conducting surveys
2. Collecting qualitative and quantitative data

Module: campaigns

Module objective:

A visual builder for executing tasks according to an algorithm predefined by the user, taking into account trigger events generated by users or the system

Features:

1. A visual builder for creating triggered mailing campaigns
2. Viewing statistical data

Module: DMP

Module objective:

Receiving data from third-party data providers

Features:

1. Receiving data from data providers

Module: DSP

Module objective:

Transferring a list of users to advertising networks to engage with them

Features:

1. Uploading customer data to advertising systems

Module: visual message builders

Module objective:

Create a visual builder for preparing message templates in advance

Features:

1. Creating templates for communication channels:
   1. Email messages
   1. Push messages
   1. SMS messages

Module: metrics

Module objective:

A module for visualizing data from the system database

Features:

1. A visual editor that displays system metrics according to rules set by the system administrator
2. Ability to create, edit and delete panels

Standalone modules:

Module objective:

A general set of components that support the operation of the preceding modules and organize the role-based access model at login

Features:

• Secrets: a section needed by technical specialists when obtaining tokens from advertising systems
• Agencies: a section for creating role-based access models under the agency scheme (functionality is determined by the system administrator)
• Users: a section for creating role-based access models under the agency scheme (functionality is determined by the system administrator)
• Brands: a section for creating role-based access models under the agency scheme (functionality is determined by the system administrator)
• Settings: a section for data entry by the administrator (the system developer) according to expert rules

Technical support

Questions arising while working with the suite should be sent to the support team

at info@datawave.ru

All requests are reviewed during business hours (Europe GMT+3); responses and

support are normally provided no later than 48 hours after the request is received.