While discussing the value of Big Data in helping to shape a customer-centric brand in my last Intel IT Center article, CHRISTOPHER PETERS commented succinctly the need for a shift from macro (market) trend analysis to micro (or individual) trend analysis.
Engaging with customers, partners and each other as individuals has fundamentally changed and the power of decisions is the individual. Before the consumer revolution and expansion of social media platforms and communities, we did not have the ability to connect and analyze at this level.
Personal relationships with brands and products existed, but were not visible to others - as is now possible with the digital economy and social media. While it is possible today to extract value from the opportunity it is critical to have both a business strategy (CMO) and big data analytic solutions (CIO) working together to support growing the business - one person at a time.
Banks have been leaders in analytics for decades, yet they have not fully realized the benefits – until now. What’s different now?
Banks cannot afford to focus solely on products anymore
Customers are expecting a more personalized service across all industries, and banks are not immune. Regulators are instituting more intrusive and granular requirements while the world’s data output from perpetually connected consumers are creating major challenges in satisfying customers and regulators. All these factors are making it increasingly more difficult for banks to stay relevant and turn a profit
Financial Services Industry (banking) CIOs and CMOs are under incredible pressures by business leaders wanting more consistency in information and by regulator’s references of data integrity with each new requirement. Unlocking the contextual insights in the data to better understand customers represents a significant opportunity to gain a competitive advantage, and fundamentally change the way decisions are made for commercial gain.
Predicting how customers and competitors’ customers will behave and how those behaviors will change is critical to tailoring fees for timely and relevant consumer offers. Big Data should be about changing the way you do business to harness the real value in your data, re-shape the interaction with the market, and increase the relationship value with your customers. Therefore, which data is required to achieve these objectives, who needs it, and how often, are key big data decisions to consider, especially when multiple data sources, coupled with geo-spatial data, social media, emails, call center transcripts, and other unstructured data all play a part in knowing the customer today, and tomorrow.
Both internal and external data, structured and unstructured should enable financial services firms to personalize their products to each customer and tie in enhancements needed across the organization to support better customer-centric performance.
In resources planning and alignment, big data solutions for financial services is not only about customer segmentation of one, but also about leveraging existing assets in such a way to reduce costs of infrastructure deployment.
These can either be shared or optimized better to align people’s skills around the right initiatives that align with what will add the most value to the bottom-line at any given moment in time.
Big Data cannot be solely about the technology
Financial services companies are using big data today to focus on operational issues – risk, efficiency, compliance, security and better decision making, however there is a growing need to identify how big data is going to be used for innovative profit growth. The value of big data is the ability to triangulate all these disparate activities into a more holistic approach to managing customer relationships.
If you have a set of customers that you've missed communicating proper disclosures to, you have a possible Dodd-Frank compliance issue, right? But, you could also have a relationship issue with that customer who’s discovered outside your walls, either from your competitors or other consumers that they have an alternative service they could change into.
Offering this consumer a new product, might not be such a good idea until you resolve the compliance, or expectations issue.
Conversely you may have customers who are on your A + list for compliance. These customers tend to be very supportive of companies they do business with because they tweet about them and mention them on Facebook. They are champions for brands they love, yet if you don’t know this you may be missing the opportunity to increase revenues from your loyal supporters.
The problem isn't not knowing. Banks are great at postmortem and analytics – they've been doing it for years. The problem is what to do when the touch point happens and having a real time view into the customer dossier at that moment in time to determine the timing of the up-sell or damage control, keeping in mind that the dossier will tell a different story each time – it’s never static.
This is exactly what gets banks into trouble - assuming consumer values, behaviors, and needs are static – they no longer are.
They can change several times in a single day, which means your 9:30am call to your call center will produce a certain contextual insight about your customer, which is different than the branch visit at 2:45pm on the same day – each touch point is a unique opportunity and needs to be treated as such.
The inability to connect data across organizational and department silos has been a business intelligence challenge for years, especially in banks where mergers and acquisitions have created countless and costly silos of data. Sourcing and analyzing internal data isn’t difficult, discovering value still locked away in these internal systems is another story. Financial services firms lag behind their cross-industry peers in using more varied data types within their big data pilots and implementations The reason for such a lack of focus on the unstructured data is due to the ongoing struggle to integrate the organization’s structured data. The real problem is that banks are looking at it mostly from a technology’s perspective – it’s a business application that needs to be introduced, not just the tech.
Financial services further lags behind in core capabilities of text analytic in its natural language state, such as the transcripts of call center conversations. These analytics include the ability to interpret and understand nuances such as sentiment, and intentions, and can often be used to understand behavior and references to improve the overall customer experience.
To compete in a consumer empowered economy it is increasingly clear that financial services firms must leverage their information assets to gain a comprehensive understanding of markets, customers, channels, products, regulations, competitors, supplies, employees and investors. Realize value by effectively managing and analyzing the rapidly increasing volume, velocity, and variety of new and existing data, and putting the right skills and tools in place to better understand their operations, customers, and the marketplace as a whole.
Customers interact from a number of touch points within the financial services walls, or outside on the social media sphere. All connection points currently lead to a data repository, be it a social media monitoring effort that is feeding into a CRM platform, a customer service center call recording and transcript, an online transaction, an ATM deposit, or an in person transaction; all these touch points currently collect data in silos.
Solutions such as what SegOne Inc. is developing, apply semantic indexing algorithms to make all the data sources more insightful in order to present to the customer in real time, the right service/product offerings that meet the customer’s needs at that particular moment in time. Contextual analytics is the soul of your data – without a soul you are a corpse or a robot – not able to add any value at all.
“Banks need to go a step further in their big data efforts by tracking perpetually connected consumers online to identify interests, and behavior patterns in order to extract insights that can lead to predictive marketing, and positioning opportunities, but it also needs to tie into compliance risk management, in order to effectively meet the complex challenges they face today”.
With real time micro-contextual analytics, when the touch point with the customer is happening, the bank will be able to provide:
- The right credit card offer
- The right mortgage
- The right checking account
- The right investment
- The right insurance product
- The right loan
- The right mix of bundled offerings to enhance the customer-centric experience and profitability
At the same time provide feedback to R&D, Product, Marketing, IT, and the business line executives on:
- Market Trends
- Gaps in offerings (competitive nemesis data)
- Resource Planning Demands
- Infrastructure needs, based on market specific growth, or lack thereof
- And so on...
When you take millions of touch-points daily, constant regulatory changes, fierce competition, unstable, volatile markets worldwide, perpetually connected consumer data which reveals behavioral, and interest patterns, it becomes really challenging for banks to have an effective big data initiative.
Furthermore the need to keep the customer facing staff up to date on everything, in order to make the best recommendation to customers, based on what the customer interests are, and making sure they communicate within compliance requirements, is almost impossible.
Add keeping up with the assets requirements across the enterprise to keep up with all the movements, come up with a targeted marketing campaign to attract, retain, and expand relationships, then take the time it takes to develop, deploy, and prove out a big data initiative, add seven figure consulting engagements to help you triangulate it all, add the cost of transforming to a centralized data warehouse, add the time it takes to put a solution that is useful in place --- and you know exactly why banks are lagging behind in big data adoption.
Banks need big data business applications to help them solve many of the issues they are facing.
Micro-segmentation is key
Developing a big data strategy for banking should not be a technology-focused exercise.
Fundamentally, banks need to be able to transform their business models into customer-centric models and transform their environments into customer-advocate centric environments based on all the regulatory requirements. Realistically banks need to become enablers of consumer commerce.
“A bank’s true nature can evolve to be a commerce partner to a consumer. At the end of the day each consumer is looking for an outcome; a commerce partner to help them create experiences they value”
Banks need to know who the customers are in terms of what they value, how they behave, how they interact with competitors, other companies, and other consumers. In other words, know who needs to be handled with kid gloves from day one, and who is a brand champion.
Banks should not assume that a quarterly, or even monthly segmentation exercise will cut it, or that they can segment all customers into a few manageable buckets – that does not work anymore! For a long time segmentation of one, was considered impossible, not scalable, and unmanageable – for financial services it’s a fundamental “must have” in order to manage risk across 100% of its customer base, and in order to drive fees and transaction based revenues from 100% of its customer base.
If your big data initiative does not create a way to automate managing each customer, individually, while turning compliance requirements into strategic advantages, it’s not a big data initiative you should bother with. It’s simply another version of the same old, same old – that’s perhaps faster.
A solution that enables banks to have micro-contextual analytics for each customer will empower banks to get ahead of risk, and proactively drive profits from the most valuable customers.
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