Accelerated digital transformation is amplifying the need for better data analytics
While digital transformation has been steadily accelerating for the last decade, digital disruption went full-gas starting in early 2020 — and won’t be slowing down. McKinsey estimates that many sectors experienced ten years of growth in digital penetration in just a matter of months. The rapid digitization of customer experiences has also hastened the exponential growth of customer data. Yet, that same McKinsey report notes that all this customer data is not giving the typical company a better understanding of its customers — because most companies lack the data modeling and analytics tools necessary to untangle and make use of customer data.
Mortgage lenders working to use their customer data
Mortgage lenders know they’re collecting vastly more customer data than ever. In fact, the financial services industry as a whole generates more data than any other sector, accounting for nearly a third of all business data. And the amount of data generated keeps growing at a near-exponential rate, with estimates of 700% growth in 2021 alone.
Mortgage lenders struggle with two main data challenges: First, it’s not always clear where all that data is going. It often ends up in siloed, hard-to-access repositories. That’s why it’s estimated that the typical company only analyzes 12% of the data they have. Second, once you have the data, how do you use it? Because it turns out that the data itself doesn’t just scream answers and insights. According to the Harvard Business Review, just 3% of business leaders say they can truly act on the customer data they collect.
Turning customer data into customer-centric business decisions
Customer experience is king. The majority of businesses today, in every sector, are competing primarily on the basis of customer experience. In the mortgage lending world, borrowers are prioritizing customer experience above rates as they evaluate lenders. Delivering a stand-out customer experience is absolutely critical. But it depends on understanding (and increasingly anticipating) what customers want — and their Amazon Primed expectations are higher than ever and evolving quickly. Customer-centricity is the name of the game in the business world: aiming to make every business decision centered on what the customer wants, expects, needs and prefers. Customer data provides an immense trove of valuable insights to drive customer-centric business decisions. The customer data you collect is your customers telling you exactly what they want and need — through what they do.
So, lenders are collecting more and more customer data — and they know it’s valuable. The key, then, is figuring out how to harness that data — and unlock the insights buried within — to drive customer-centric business decisions.
The challenge: Too much (siloed) data
The most basic challenge every mortgage lender is facing is the sheer quantity of customer data pouring into the organization every day. We’re long past the relatively quaint notion of “Big Data” — the business world generates and manages the majority of the 160+ zettabytes of data in the world today. A zettabyte is too large to wrap your brain around, but the underlying reality isn’t: It’s impossible for the human mind to derive meaningful insights from just looking at the customer data flowing into a mortgage lender. But most mortgage lenders couldn’t just look at all their customer data — even if they wanted to. In the typical lending organization, the data is splintered into many silos — across systems, technologies and apps, as well as departments and teams. Trying to extract meaningful insight from just one of these silos is like trying to make a judgment about your customer by looking at only their elbows — which is to say, not particularly accurate or useful. Forrester says that the lack of a unified, 360° view of customer data is the top challenge for enterprise customer experience teams — across all segments. But it’s a particularly big problem for mortgage lenders. The mortgage origination and closing process is complex and collaborative. In addition to the multiple internal systems lenders use, they’re also working with income, employment and asset verification providers, insurance and fraud protection providers, appraisal and title partners — the list goes on. Critical data about borrowers is commonly stuck within the silos of these service providers’ systems.
of companies say they have no single source of truth for their data
1 in 3
say the lack of centralized data is a barrier to effective analytics
How do you extract customer insights from customer data?
There are three key steps for mortgage lenders aiming to build a customer data analytics program that effectively extracts actionable insights from your customer data:
- Build a centralized customer data platform
A lack of centralized data is the barrier holding back 1 in 3 companies’ analytics programs. The foundation for any customer data analytics program is a centralized customer data platform — integrating all customer data, across all channels, systems and touchpoints — into a single, central source of truth on all things CX, from which analytics and automation can draw. Leading customer data platforms don’t just integrate data form the components within the customer experience platform. They come with pre-built integrations to other best-of-breed technologies — from loan origination systems, to product and pricing engines, to point-of-sale systems — and offer robust APIs that enable custom integrations. This ensures that all relevant customer data streams are feeding into a single, centralized source of actionable truth.
2. Focus on good data hygiene
Pulling all the data together in one place doesn’t mean all that data is usable. That’s because customer data from difference sources — CRM and LOS systems, contact centers and social media, etc. — comes in different formats. In fact, as much as 90% of the data we generate today is unstructured — meaning it doesn’t fit a single, neat format like an account number. This unstructured data needs to be managed to ensure good data quality or data hygiene — ensuring it’s usable for analytics engines. It’s estimated that poor data quality leaves about 30% of revenue on the table for the typical business — and costs U.S. businesses more than $3 trillion every year. Again, a quality customer data platform or customer experience platform will come with pre-built integrations and APIs that simplify the data hygiene challenge — and best-in-class solutions will come backed by expert support to help mortgage lenders tackle the data quality issue.
3. Leverage customer data analytics tools
Once you’ve got your centralized trove of customer data, it’s time to go to work. But any single piece of customer data has limited value on its own — the value lies in the connections and contextual relationship between the data. The human brain can juggle around five pieces of information — in the best of conditions. Modern analytics engines, powered by sophisticated artificial intelligence (AI), have limitless ability to manage multiple variables and pull out the trends, patterns and connections in real time that the human mind couldn’t identify in a lifetime. This is why businesses continue to increase investments in AI and analytics. Yet, while most lenders are using some form of analytics, a 2021 Ellie Mae survey showed that roughly half of all mortgage lenders do not have a clearly defined strategy or program for customer data analytics. Fortunately, modern analytics tools are becoming more accessible, more practical and easier to use than ever.
What to look for in a customer data analytics tool
- Cloud-powered analytics
The most innovative customer data analytics tools are all in the cloud. Yet, more than half of enterprise analytics platforms are still using on-premises delivery models that limit capabilities and cost-efficiencies. The underlying computing power that drives the sophisticated capabilities of modern customer data analytics is enabled by the cloud. Cloud computing offers incredible computing power to organizations of any size, in a cost-effective SaaS delivery model that eliminates the enormous barrier of capital investment that previously limited analytics to the largest enterprises.
2. Intuitive user experience (UX)
Until fairly recently, using data analytics tools came with a prerequisite of a data science degree. This not only put them out of reach for small to mid-size mortgage lenders — it greatly limited their potential use within an organization. Today’s leading customer data analytics tools are built to be easy and intuitive for anyone to use. They come with the same kinds of user-friendly interfaces that power the rest of our digital world of work. This makes customer data analytics practical for any mortgage lender — no matter how tech-savvy their staff — and means that the analytics tools can be directly used across the organization, rather than being limited by the “gatekeeper effect” of needing help from IT.
3. Highly visual, shareable outputs
Closely related to usability of the tool is the usability (or understandability) of the outputs. Leading customer data analytics platforms deliver highly visual reports and show trends and insights in simple dashboard-style views. These intuitive outputs make it easy for anyone in the organization to understand the meaning — and easily share these insights to begin putting them to work.
4. Seamlessly embedded within workflows
To maximize the real-world utility of an analytics tool, it can’t be an add-on piece of software with a separate user interface. Leading customer data platforms and customer experience platforms feature sophisticated analytics capabilities that are built right into everyday workflows for things like customer interactions, marketing campaign management and more. These seamlessly embedded analytics capabilities are the essential “short-cut” to turning raw customer data into data-driven action.
5. Insight-triggered actions
The most advanced customer experience platforms go beyond embedding analytics tools into everyday workflows. These platforms use the outputs from embedded analytics tools to automatically trigger actions within customer journeys. This intelligent automation gives mortgage lenders a nearly plug-and-play ability to deploy data-driven customer experience and marketing strategies in real time across their organizations.
How do you get truly actionable analytics insights?
Any data can be actionable — if you know what to look for. Analytics tools just dramatically simplify that hunt. Still, plenty of analytics insights aren’t truly actionable on their own. Traditionally, analytics tools have still required time and expertise to interpret exactly what the insights are telling you — and determine what to do next. But the next generation of analytics tools are tackling this problem head-on by focusing on delivering predictive and prescriptive insights. In other words, they’re not just telling you what’s already happened. They’re predicting what’s coming next — so you can plan and get ahead. And they’re even prescribing the next-best action, based on that predictive insight. This is the fully realized potential of actionable analytics: a tool that cuts right to the point of what you should do next, based on what your customers are telling you right now.
Where are mortgage lenders on their analytics journey?
Today, most companies across all sectors are lagging well behind this fully realized predictive and prescriptive analytics potential. Just 7% of marketers in all segments say they’re able to deliver data-driven marketing engagements, in real time, for their customers. And that number is closer to 3% in the mortgage lending world:
Where are Mortgage Lenders in the Analytics Journey?
Descriptive Phase: 37%
Lenders who can see simple facts about business performance.
Analytical Phase: 36%
Lenders who can see what happened and why it happened.
Predictive Phase: 24%
Lenders who can begin to identify patterns and meaningful business trends.
Prescriptive Phase: 3%
Lenders who can inform future decisions based on real-time data insights.
How mortgage lenders are turning customer data into real-world action
While most of the mortgage industry is just beginning to move toward actionable predictive and prescriptive analytics capabilities, the most innovative and forward-thinking lenders are already leveraging analytics to turn their raw customer data into real-world action across a variety of use cases. Here’s just a quick look at a few:
Great marketing campaigns can produce a downstream headache: Tedious, repetitive lead qualification. Whoever the task falls to, manually reviewing each lead not only adds up to higher labor costs, but it slows down the sales cycle — and can let valuable leads get away before they’re qualified and contacted.
Leading sales teams are now using embedded analytics and intelligent automation to rapidly analyze and qualify leads generated through marketing efforts. Analytics-powered lead qualification is not only exponentially faster — allowing sales to act on qualified leads in near-real time — but can also produce higher-quality leads. Today’s analytics engines can analyze a much broader set of data — comparing an individual lead to past precedents and other benchmarks — to more accurately determine the hottest leads for sales to target.
Accelerating & enhancing customer experience
Consumer expectations are the chief driver of digital transformation in the mortgage lending industry, and customer data analytics has tremendous potential to help lenders meet rising expectations across the borrowing journey. As borrowers evaluate options and consider lenders, embedded analytics can provide personalized recommendations and support, 24/7. As they move into the application and pre-closing processes, analytics-enabled automation can accelerate and simplify income, identity and asset verification, as well as the validation of other documents and customer information — while providing automated status updates to keep customers informed. Analytics can significantly speed data validation and other core processes in loan underwriting and loan closing. Together, these analytics-powered advantages add up to profound acceleration in the loan application, approval and closing process. In a white-hot market, empowering borrowers to move faster — and keeping them informed on their progress — directly boosts customer satisfaction and creates powerful competitive advantage.
Improving customer engagement & retention
Marketing, sales and loan officers are naturally focused on customer acquisition and the high-touch needs of newly acquired customers. Meanwhile, the ongoing customer communication that’s critical for retention and building lifelong relationships can often get less attention. With typical customer retention rates in mortgage lending at all-time lows, most lenders know they could be doing more to deepen loyalty and build more value from their existing customers. They just don’t have the time. Lenders have been using marketing automation to drive drip campaigns for years now. But today’s analytics-powered marketing automation technologies enable a new level of hyper-personalization — with even less manual intervention. Analytics-powered marketing automation can intelligently trigger next-best messages and sales actions based on customer behaviors and actions — or based on real-time analysis of integrated customer data. Leading lenders are building customer engagement, cross-selling and retention strategies based entirely on these intelligently triggered customer journeys. This analytics use case can not only unlock significant unrealized value from your most receptive audience, but also free your marketing and sales teams to focus on customer acquisition.