ANFIMAU | Brand tracking analytical product

Overview

Our customer, a leading market research agency, offers its clients analytical services to provide insights on how their brands are performing in a market considering actual and potential customers.
Currently, customer offers bespoke market research studies with surveys, analytical methods and insights deliverables adopted for specific clients needs and brands to be evaluated. Such studies are usually executed on a per quarter / monthly basis and valued by big clients but at the same time associated with high efforts for preparation, execution and customization thus leading to slow delivery of end results to clients and not often applicable to medium and small clients.
As part of a new business strategy, the customer aims to develop a new offering for its clients - Brand Tracking product leveraging Syndicated Research method as an alternative to existing bespoke market research services. The business goal is to cover needs of small and medium clients, provide supplementary market insights to big clients as well as respond to growing competitive market demand for fast, user-friendly brand tracking products.

Challenge

Strict Time to Market constraints for MVP version of new Brand Tracking Product that includes following high-level functional areas:

Incremental data ingestion from diverse type of data sources like survey execution engine, internal reference data management systems, panel management system via such protocols as APIs, RDBMS, File Storages.
Implementation and integration into data processing flows of new analytical algorithms for brand equity calculation, aggregated metrics preparation, weighting calculations as well as integration of existing custom AI-based services for trends analysis and other analytical use-cases.
Exposure of calculated analytical metrics and captured insights to end-clients using rich-UI web application with fast analytical charts, integrations with internal systems, support of self-service registration and different client subscription models.
Enablement of Data Exploratory capabilities on top of ingested and analyzed data to internal data analysts, scientists and non-power users.

Pursue new revenue growth in post MVP period by integrating new data sources, developing new analytical methods, scaling to new brand categories / countries and expanding the number of customers & users.

Approach

Engagement started with deep analysis of functional and non-functional requirements as well as prioritization of MVP scope considering Time to Market constraints and immediate business value.

Outlined MVP scope and strategic vision were leveraged to prepare phased solution focused on MVP needs as a first step as well as extendable and scalable to support strategic revenue growth by scaling solution to new data sources, analysis methods and markets.

Delivered solution is based on Azure PaaS technologies and leverages:

Data architecture integration

Medallion & Lakehouse architecture patterns for organizing data within analytical platform.

Scalable data processing

Loosely coupled & scalable batch data ingestion and processing components with support of incremental processing.

Integrated AI & Event-driven architecture

Event-based and API-based integration patterns with internal systems and existing AI services.

Efficient data analysis platforms

Cost effective data exploratory environments to run ad-hoc analysis by data analysts or connect from BI tools by non-power users to data stored in Lakehouse.

Microservices-driven web operations

Microservices architecture pattern for web platform that provides operational and visualization capabilities for end-users.

Optimized data visualization

Data charts within web platform use NoSQL storage that stores analysis results extracted from data analytical platform in a read-optimized format to enable better experience for end-users.

Proactive security in SDLC

Secure by design SDLC processes with Shift Left principles supported by Infrastructure Automation and DataOps processes.

Rapid MVP launch and client onboarding

MVP version of the product with first clients onboarded was delivered in a half of year time-frame.

Post-MVP product enhancement and growth

Post-MVP version of the product was successfully extended with new type of data sources and analysis methods to support planned business and clients growth without degrading non functional characteristics of solution.