In an increasingly customer-centric world, the ability to capture and use customer insights to shape products, solutions, and the buying experience as a whole is critically important. Research tells us that organizations that leverage customer behavioral insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin.1 Customer data must be seen as strategic.
Yet most companies are using only a fraction of the data in their possession. Sprawling legacy systems, siloed databases, and sporadic automation are common obstacles. Models and dashboards may be forced to rely on stale data, and core processes may require considerable manual intervention. Often, too, organizations may not have a clear understanding of the specific outcomes they’re looking to achieve through data optimization. All that is leaving significant value on the table.
How much, you ask? A McKinsey survey of more than 700 organizations worldwide found that spending on analytics to gain competitive intelligence on future market conditions, to target customers more successfully, and to optimize operations and supply chains generated operating-profit increases in the 6 percent range.
Our work suggests that these returns don’t have to be confined to a handful of top players. Rather, when it comes to generating measurable value from their data, most organizations have plenty of low-hanging fruit they have yet to harvest.
Here are three of the most promising avenues available now to most organizations.
Tease out critical patterns
Information on what customers purchase, how many times they contact customer service, and how long they linger on a given website can create an insightful narrative about buying habits and preferences. Most organizations capture much of this information, but often in isolated packets. Too few marry it all together. A bank, for instance, can minimize churn, fraud, and default risk by pooling customer data and applying advanced analytics to understand the needs and possible next actions of key segments. Those patterns can be used across the business. Credit-risk teams will want to know if a customer whose bank balance falls into the red more than once a quarter could be at higher risk for defaulting on a mortgage. Marketing could use the data to pitch financial-planning and overdraft-protection services. Such customer data can also be packaged, sanitized, and sold to relevant third parties, such as credit bureaus and payments companies—allowing the initial investment of analytics time and modeling to yield multiple dividends.
In addition, pattern data can be used to direct spending. An industrial-parts manufacturer, for instance, studied customer-buying histories, behavioral data, and surveys to understand the typical purchasing path for their highest-value segments. The data revealed that buyers were far more likely to rely on distributors for product recommendations and much less likely to be influenced by trade-show demonstrations and collateral. Marketers were able to reallocate budgets accordingly.
Others, led especially by consumer companies, are taking things further and using customer data to personalize outreach. By pulling together rich customer profiles and rigorously tracking response rates, marketers can know precisely what types of content over what channel and format are likely to have the greatest impact on key segments and microsegments. A decade ago, the tools weren’t available to do this. Now they are. And nearly all companies can benefit. An automotive insurer, for instance, learned that the customer journey to buy car-insurance policies typically starts 60 days before customers receive their first quote and usually involves an average of 15 signals. They can use that information to tailor the tone and timing of their outreach. Such personalization can deliver five to eight times the return on investment on marketing expenditure, and can lift sales by 10 percent or more.
Exponentially improve productivity
While frontline monetization opportunities tend to get the headlines, often the biggest, near-term gains are operational in nature. Data optimization helps reduce inefficiency. Many B2B companies, for instance, can find it hard to enforce pricing discipline given their large and distributed field networks. Exceptions, all too often, can be the rule. But league tables, reporting dashboards, next-best-action analytics, and other solutions can have a profound impact, allowing managers to compare performance and see what pricing, discounts, and bundles are working at other, similar clients.
Data-enabled processes can also help businesses scale scarce institutional expertise by making specialist knowledge more readily available. A financial institution, for instance, found that its transaction specialists were being inundated with foreign-trade questions from regional offices. That was frustrating the head of transactions, who had hired the team to create a suite of new services. The team solved the problem by implementing a system based on artificial intelligence (AI) capable of capturing and interpreting reams of data to surface answers to the most commonly asked questions.
Similarly, better data integration across a range of internal and external sources can cut down on search times and help analysts, auditors, and others spend less time tracking down information and more time applying the results. Professionals can run the numbers on much bigger sets of data, do better vetting, and do it all faster, allowing specialists to apply their skills in other ways. While AI and machine-learning tools do require a more significant investment of time and resources, many other capabilities can be created using tools and systems that most organizations have in place today, and then refined from there.
Forge breakthrough solutions and services
Upstart Network is a lending company whose specialized algorithms and nontraditional measures allow it to use a range of customer-background data to offer market-leading rates. Ginger.io similarly relies on customer data from smartphones and fitness wearables, such as sleep, mobility, and communication patterns, to improve clinical assessments and diagnose when patients with mental illness may be becoming symptomatic. Customer data is also enabling the creation of online marketplaces and bold new business models, such as Airbnb’s. They join a fast-growing list of companies that are using data to innovate breakthrough data applications and business models.
Such breakthroughs don’t have to be the preserve of digital pure plays, however. Many incumbent organizations have the advantage of long-standing client relationships, deep pockets of expertise, and scale. By prioritizing a handful of specific customer outcomes, such as reduced churn or improved cross-sell, and setting up small, dedicated cross-functional teams to experiment, refine, and release new approaches, established players can generate significant returns.
Making it happen
Organizations are at different data-maturity levels. But regardless of how far along a company is, virtually every organization has valuable customer data assets that could be put to better and more active use. Although the basic requirements of any strategic initiative still apply—articulating a strong and cohesive digital strategy, securing strong leadership backing and the right resources, and prioritizing one or two high-impact pilots—companies don’t need to wait until they have the “perfect” systems or technologies in place. These two foundational steps alone can open up a wellspring of opportunity.
Enrich customer data. Customer data should be enriched to incorporate digital profiles, life events, community information, transaction-based insights, customer preferences, sentiment scoring, and so forth in order to get a full picture of the customer. Organizations can capture digital profiles and digital activity by linking web, mobile, and social-presence data. Marketing or customer teams can start by attaching activities to customer profiles. Those activities might include customer-sentiment-behavior scores, insights derived from purchasing transactions, call-center queries, and online behavior. A property-and-casualty insurer, for instance, linked customer-footprint data from an online real-estate site to identify customers who might be considering moving. Agents could see that information on the customer’s profile and send potential customers a prepackaged quote for the relevant zip codes.
Make that data shareable and accessible. Using “two speed” IT, where specialist business and IT teams fast-track digital development, businesses can get a jump on high-value customer initiatives even as they build out their longer-term transformation. Software overlays can link data silos among different lines of business, and semantic layers can funnel information into a user-friendly interface. Integrating pertinent customer data and making it accessible across the business not only cuts down on duplicate information gathering and manual data entry but can also lead to offering customers lower prices, greater convenience, and improved experiences.
When a customer calls into a contact center to raise a concern, some organizations are able to update that interaction in real time, so that relevant parties across all the organizations can get a 360-degree view of the customer and better respond to her needs. Likewise, rather than holding up clinicians who wanted fast access to their patients’ complete medical histories, a dedicated digital-services team created a patient portal that allowed doctors to log on, search for a patient’s name, and receive an at-a-glance complete patient report including links to X-rays and other images. The portal masked the complexity of the underlying data environment and helped improve service, outcomes, pricing, and risk management. The team then worked with those managing the hospital’s larger digital transformation to migrate the portal over to the new environment once it was ready.
Companies getting started might consider a few key questions:
- What customer data can we turn into unique data products, and where does it reside?
- What external data could we acquire, and what third parties should we collaborate with to create data-driven value?
- Is there an opportunity to use customer data to create a marketplace to bypass or reshape an existing industry?
- What skill sets and capabilities will we need, and where can we find or develop them?