Financial institutions have been scrambling to develop a data analytics roadmap to drive their digital transformation efforts. Yet senior leaders often fail to prioritize one mission-critical step early enough to accelerate the building of a successful data analytics program. That step: intimately knowing the life needs of consumers and their families, their life triggers and, finally, their priorities.
Once armed with the knowledge of your most engaged, profitable and ideal consumer lifestyle segments, you can then model based on those engaged users to build data-driven personas, and also to target your ideal new prospects for future growth with precision. The final step to improving user experiences with a next-generation data analytics and personalization program, to allow one-to-one marketing, is building the “Last Mile.”
And as every leader ultimately discovers, that last leg is by far the most challenging stage of digital transformation.
Consumer Understanding Comes Before Martech
Typically, financial institutions have focused their efforts on trying to find, select, build or purchase the ideal data warehouse, CRM, marketing automation, or software app to build their digital road map. Yet many fail to recognize that the ultimate success of this innovative digital road map is not about putting the technology, systems or data first.
“Who are your largest, fastest-growing, most-engaged, profitable and satisfied consumers? Market research alone cannot tell you. The answer can only come from deep data analytics.”
— Mark Weber, Strum Agency
Instead, it’s all about putting your members’ or customers’ lives, relationships, and priorities first. That means every internal team data priority, strategic growth plan, digital transformation and growth planning effort must begin with a crystal-clear understanding of this: Who are your largest, fastest-growing, most-engaged, profitable and satisfied consumers?
Market research alone cannot tell you. The answer can only come from deep customer, product, market, competitive, and prospect analytics. And void of such insight into consumers, banks and credit unions ultimately will struggle in their digital transformation efforts to develop rich, personalized, emotionally-connected content and value-added relationships that lead to trust and brand alignment.
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- Customer Segmentation Models In Banking Are Broken
- Data Analytics Performance Gap Ruins CX in Banking
Where Better Data Analytics Can Begin
The first step in any intelligent data analytics journey should be grounded in a lifestyle segmentation growth strategy that is driven by hard data. Armed with this vital strategy, leaders can then focus on applying those data insights into branch optimization strategies; future market and growth planning; prospect targeting; and customer relationship marketing. These lead to rich one-to-one personalization insights, engagement and market share growth.
A smart lifestyle segmentation process requires sophisticated capabilities for data scrubbing, normalizing and integrating core and disparate data, and, ultimately, geo-coding data to be effective. It requires a tailored profitability data model to understand the relationship value of each different segment you have and each account and product you offer.
The process must also include deep market area and prospect analytics, not just looking at your own customer segment relationships for insights.
All this requires the building of a robust data analytics warehouse. It requires the appending of multi-sources of core, disparate and unstructured data. It demands intelligent licensed third-party big data to ensure your data is enhanced with accurate income, gender, lifestyle, financial debt, investing, family status and media usage.
Building on the Data Analytics Foundation
With the input of expert data analysts, lifestyle segmentation experts, brand strategists, growth planning analysts, and real estate strategists, a bank or credit union can turn raw data into actionable data. This unlocks critical foundational answers to strategic growth questions and aids to future market forecasting, like these:
- Which segments have become the fastest-growing and most-engaged relationships in the last three years? How are they shaping current growth trends?
- What makes each segment unique demographically, psychographically, and geographically?
- Which are the most engaged overall users? What products are key for them?
- What factors make a consumer segment more, or less profitable?
- What penetration levels have you generated among your most engaged and profitable segments? How many more of each growth segment are there in each market?
- How different from each other are the market segments you have captured?
- How have each key segment’s relationship engagement, balances, and product usage evolved each year?
- How can you pinpoint and target “ideal opportunity” prospect segments in key markets to accelerate profitable acquisition and growth?
- Which branches are performing best on key performance indicators?
- Which branches are acquiring your ideal growth segments historically, versus the last three years?
No matter what digital transformation or technology platform decisions leaders make, knowing, defining and targeting your ideal customers and future growth-target segment prospects with rich data, psychographic, and lifestyle segmentation will be a requirement of advancing user experiences and intelligent one-to-one personalization.
Armed with a body of sophisticated customer relationship data patterns, including new customer and prospect growth market trends insights and profitability, a financial institution is in a much stronger position to strategize how to build its next-generation targeting and personalization strategy. Key data sets, like regional financial market-share data, product portfolio analytics, branch usage, profitability measures and future target lifestyle growth segments, help drive new levels of decision making, future performance, next-gen onboarding processes, and organization-wide productivity.
Read More:
- Data Analytics Pitfalls Banks and Credit Unions Must Avoid
- Data is Powerful, But Not Banking’s ‘Silver Bullet’
Building Personas from Your Research
Once your final lifestyle growth, retention and secondary segment targets and future prospects have been identified, the next step is to build a data-driven persona strategy. Developing data-driven persona maps can help financial marketers humanize and personalize experiences, improve product design, and tailor smarter relationship development.
While segmentation data is vital for strategic targeting focus and market growth planning, it’s not enough to engage your entire staff to harness personalized lifestyle insights, emerging life issues, and event triggers. These powerful insights help banks and credit unions engage consumers with relevant data and insights, allowing you to deliver helpful content to improve their financial education and decisionmaking. This step is the bedrock of future one-to-one user personalization and tailored onboarding across your enterprise.
“Data-driven personas represent the marriage of data analytics insights and human empathy. They empower marketers to manage, track and build personalized content marketing and tailored user experiences.”
Smart, data-led, journey-mapping exercises for organizations preparing to design a personalization program must leverage data analytics, demographic and lifestyle insights, financial behaviors, product relationship and profit insights if they truly hope to know who their key customers are by life stage, need, product journey, life trigger and event.
Using rich data from psychographic lifestyle segmentation targets and core data, your institution can build data-driven persona maps with factual data that provides the entire organization a deeper understanding of a member’s individual and family life and dynamic financial situation. This is amplified with deeper product relationship knowledge, product and transactional data, market insights, behavioral and predictive triggers and opportunities to build higher empathy.
These persona maps then become the foundation for engaging your entire staff in understanding, leveraging and delivering relevant and personal information.
Data-driven personas represent the marriage of data analytics insights and human empathy. They empower people to manage, track and build personalized content marketing and user experiences tailored to each person’s life events, pain points and unique needs. Personas help inform day-to-day internal decisions and improve interactions by bringing data analytics to life via innovative planning around new product design, savvy staff persona training, and enhanced and simplified operational processes.
Armed with knowledge and insights about these personas (for both existing customers and future prospects) organizations can re-design simpler, smarter and more intuitive user experiences to improve relationship development and retention. Adapting these tools can also humanize institutions’ storytelling and content, and deliver tailored financial education to impact consumers’ lives, transform client relationships, and build a higher level of engagement and trust.