Capturing value from your customer data
Companies
can put their information to work by teasing out novel patterns, driving
productivity, and creating new solutions.
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.1Customer 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?
By Brad Brown, Kumar Kanagasabai, Prashant
Pant, and Gonçalo Serpa Pinto
http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/capturing-value-from-your-customer-data?cid=other-eml-alt-mip-mck-oth-1703
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