Customer database segmentation escalates innovation opportunities.
© MJV Technology & Innovation
70%
The Outcomes:
AI-Driven Intelligence
Automated CRM Segmentation for a Leading Fintech
of consumers expect companies to take an active stance on social, environmental, and political issues
According to a survey by "Social Sprout"
Design-Driven Data Science
A Latam fintech unicorn grew its user base by around 400% in a single year due to a clear value proposition and fluid user/customer experience.
But this accelerated growth brought about some questions: who were these new customers and how do we serve them?
To answer these questions, MJV’s team used data science techniques to statistically scale and validate findings from the research & discovery process within the CRM database.
Industry: Finance | Client: One of the biggest fintechs in Latin America
Industry: Finance
Client: One of the biggest fintechs in Latin America
The Challenge
• Segmentation of the local banked population according to their main unmet needs.
• Automation of the process of identifying segments in the customer database.
• And the discovery of opportunities for innovation in the upscale market.
The Challenge
Market Research and Customer Profiles
When dealing with large sets of data, MJV believes that a qualitative angle is a necessity, especially when the data represent living, breathing individuals - a mix we like to call Design-Driven Data Science.
Our solution was to use market research alongside qualitative user interviews in order to generate the customer profiles and benchmarks needed for CRM segmentation while keeping the team and deliveries adherent to user realities, needs, and pains.
Our Solution
Implementing a profitable strategy in a different market
A High-Fidelity O2O Prototype
Our Solution
When dealing with large sets of data, MJV believes that a qualitative angle is a necessity, specially when the data represents living, breathing individuals, a mix we like to call Design Driven Data Science.
Our solution was to use market research alongside qualitative user interviews in order to generate the customer profiles and benchmarks needed for CRM segmentation while keeping the team and deliveries adherent to user realities, needs, and pains.
Fly or Die:
Validating a New Revenue Model Through Testing-as-a-Service
A solution is only as good as what you can prove, and MJV did this through a testing-as-a-service framework.
While our client ran the stores (both digital & physical), we took it upon ourselves to craft, test, and validate the strategy to determine if it should be implemented nationwide.
Case Study
How We Got There:
The Process
Ethnographic research with clients and non-clients, mapping behavioral drivers and needs.
Customers
01
Benchmarks, market research, and references from other brands/companies.
Market
02
Analysis and generation of hypotheses, automation for viewing profiles within the database.
Data Science
03
Use of the blue ocean strategy to rethink value delivery for a prioritized segment.
Innovation Opportunities
05
Research with stratified sampling and a high level of reliability.
Quantitative Research
04
All phases of the project had important results and outcomes that contributed to the final delivery. Below is a list of what we handed over to our client at project’s end.
The Results
127
Features created that provide insights into customer profiles and financial behavior.
In this project, techniques such as data science, machine learning, and cluster analysis were crucial to statistically confirm and scale the knowledge obtained in the research stage within the CRM database, in addition to automating the classification process.
Take a look at some of the deliveries supported by our Design-Driven Data Science methodology:
13
Different product usage contexts.
117
Variable filters generated through CRM behavioral segmentation.
51
Innovation opportunities identified within the new customer base profiles, according to our findings about their needs.
Doing research into your own customer base in order to better understand and segment them can be a daunting task (especially when your user base suddenly grows seemingly overnight), but you don’t have to go it alone.
If you’re looking to automate your customer segmentation, discover new blue oceans, or explore innovation opportunities within your industry, reach out to one of our consultants.
Our Conclusions:
Final Thoughts
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Design Driven Data Science:
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Qualitative Segmentation
Two different hypotheses for segmenting the banked population based on qualitative research.
Segmentation Drivers
Definition of relevant factors for differentiating the behavior of bank customers.
Market Deck
Research and information from secondary sources with financial market data, banked population, and upscale segment benchmarks.
Segmentation of the banked population
Creation of factors and method of categorization of macro and micro-drivers, extracting 8 financial segments and 20 micro-segments according to its variables of interest, from a stratified, complex sampling model.
Quantitative scoring of the customer base
Creation of a lead scoring matching the customer profiles with financial products, according to purchase propensity.
Identification, segmentation & scoring automation
Development of a streamlined, automated, more efficient, and documented process/code for segmentation and base scoring iterations.
The extensive scope resulted in five different work fronts. To base each of our work fronts, we decided to ground the individual segments of the process with specific methodologies.
The five work fronts were: