The heartbeat of modern marketing: Data activation and personalization
Technology
has finally advanced to the point where marketers can use real-time data in a
way that is both meaningful to customers and profitable for companies.
We’ve come a long way from “People who bought this, also bought that.”
Consider the experience of a
representative customer we’ll call Jane. An affluent, married mom and
homeowner, Jane shops at a national clothing retailer online, in the store, and
occasionally via the app. When visiting the retailer’s website in search of
yoga pants, she finds style choices based on previous purchases, the purchases
of customers with profiles similar to hers, and the styles of yoga pants most
frequently purchased on weekends. She adds one of the offered yoga pants to her
shopping cart and checks out.
With the exception of a follow-up email,
most interactions with the customer stop there. But here’s what this example
looks like when we activate Jane’s data: Three days after her online purchase,
the retailer sends Jane a health-themed email. Intrigued, she clicks the link
and watches a video about raising healthy kids. One week later, she receives an
iPhone message nudging her to use the store’s mobile app to unlock a 15 percent
one-day discount on workout equipment. Though she has never bought such items
at this retailer, Jane takes advantage of the offer and purchases a new sports
bag. What began as a simple task of buying yoga pants ended up being a much
more engaged experience.
Such data-activated marketing based on a
person’s real-time needs, interests, and behaviors represents an important part
of the new horizon of growth. It can boost total sales by 15 to 20 percent, and
digital sales even more while significantly improving the ROI on marketing
spend across marketing channels: from websites and mobile apps to—in the
not-too-distant future—VR headsets and connected cars.
Customer
data platform: Solving the ongoing challenge of true personalization
Companies regularly experiment with
testing the impact of varied customer experiences, but they do it in isolation.
When they do try to scale, they smack against the challenge of understanding
what to prioritize. Going back to Jane, do marketers target her as a mom, a
yoga enthusiast, or a homeowner? What happens when tests are running against
all three segments? Is she part of a new microsegment that combines attributes
and signals across all three segments?
This is a challenge that has continued to
plague marketers, despite the promise of solutions such as
customer-relationship management (CRM), master-data management (MDM), and
marketing-resource management (MRM). These solutions can help companies
consolidate and streamline data, manage segmentation, organize workflow, and
improve customer relationships. But they don’t take full advantage of digital signals customers provide. Instead,
relying on antiquated “list pulls,” basic segmentation, and campaigns, all lack
the automated decision making, adaptive modeling, and nimble data utilization
to scale personalized interactions.
Enter the Customer Data Platform (CDP)—a
data discovery and “decisioning” (i.e. automated decision making) platform. The
CDP makes it possible for marketers to scale data-driven customer interactions
in real time. And while CDP hasn’t really broken into the Gartner Magic
Quadrant or Forrester Wave, it is gradually becoming an industry-standard
concept, with a small but growing cadre of third-party platforms emerging that
will soon shape the category.
Four
steps to effectively activate your data
Incorporating a CDP into your
organization—whether piggybacking on an existing master data-management or
customer-relationship-management system or starting from scratch—requires
mastery of four areas:
1. Data
foundation: Building a rich view of customer
Many companies have the elements of a
relatively complete view of the customer already. But they reside in discrete
pockets across the company. Just as a recipe does not come together until all
the ingredients are combined, it is only when data is connected that it becomes
ready to use. The CDP takes the data a company already has, combines it to
create a meaningful customer profile, and makes it accessible across the
organization.
“Feeding” the CDP starts by combining as
much data as possible and building on it over time. Creating models that
cluster customer profiles that behave and create value in similar ways requires advanced analytics to process the data and machine learning to refine it. Over time, as
the system “learns,” this approach generates ever-more-granular customer
subsegments. Signals that the consumer leaves behind (e.g., a site visit, a
purchase on an app, interest expressed on social media) can then expand the
data set, enabling the company to respond in real time and think of new ways to
engage yet again. Furthermore, the insights gleaned extend beyond a customer’s
response to a specific campaign, for example by driving more targeted product
development.
A number of companies we’re familiar with,
struggling to truly understand their customers who make infrequent purchases,
combine their own CRM data with Facebook consumer data to build look-alike
models. This helps identify the highest-value prospects most likely to buy in
their category. Increased targeting through display ads on and off Facebook can
yield 50 to100 percent higher returns than from the average Facebook audience.
Mapping third-party data (when it exists) to customer segments via a
data-management platform (DMP) can enhance the experience for both known and
anonymous digital consumers, leading to improvements in engagement and
conversion, measured in net promoter score, acquisition, and lifetime value.
2.
Decisioning: Mine the data to act on the signals
The decisioning function enables marketers
to decide what is the best content to send to a given customer for a given time
and channel. Customers are scored based on their potential value. A set of
business rules and regression models (increasingly done through machine
learning) then matches specific messages, offers, and experiences to those
customer scores, and prioritizes what gets delivered and when. This allows
companies to make major improvements in how they engage with their customers by
developing more relevant, personalized engagement, within a single channel or
across channels, based on a customer’s behavioral cues. Those signals can be
basic, such as “cart abandoned” or “browsed but didn’t buy,” or more nuanced,
such as activity by segment and time of day, gleaned from mining customer data.
In effect, these signals become triggers that invoke an action. A decisioning
engine develops a set of triggers and outcomes based on signals and actions the
company takes in response.
For example, one multichannel retailer
discovered that many consumers made a purchase on the website just once per
year. Further analysis revealed those same customers tended to return to browse
the site a few days after purchase. The company now takes advantage of this
window of opportunity to send tailored, targeted messaging, rather than risk
losing the customer for another year. This approach doubled the open rate of
its emails—from 10 to15 percent for generic targeted communications to 25 to 35
percent for real-time, “trigger based” communications acting on consumer
signals.
More sophisticated companies build up a
decisioning model that works across all distribution channels. That requires
advanced modeling and analytics techniques to identify the impact of one
channel on another as a customer proceeds along his/her decision journey. A
travel company took this approach recently and saw coordinating messages across
channels drive a 10 to 20 percent incremental boost in conversion rates and
customer lifetime value.
Effective decisioning is based on repeated
testing that validates and refines hypotheses and outcomes. Over time, these
can become increasingly sophisticated as models and algorithms build on each
other. One telecommunications company has been testing different offers to different
groups: millennials, customers in specific cities, previous owners of a
specific device, groups of relatives, and people who viewed a specific web page
in the last three days. As complex as this may seem, a semi-automated
decisioning engine prioritizes the offers and experiences proven to have the
highest rate of return. This allows the telco to scale the results of dozens of
tests without fear of inconsistent customer experiences or conflicting offers.
3.
Design: Crafting the right offers, messages, and experiences at speed
Understanding your customers and how to
engage them counts for little without the content to actually deliver to them.
Designing great offers, however, is hampered by the fact that functions and
departments within companies tend to operate as mini fiefdoms. The owners of
each channel test and engage consumers exclusively within their own channel.
Real benefits can only occur when companies shift to “war rooms” of people from
relevant functions (marketing, digital, legal, merchandising, and IT/DevOps)
who focus on specific consumer segments or journeys.
These teams have clear ownership of
consumer priorities and responsibility for delivering on them. The
cross-functional team continually develops new ideas, designs hypotheses for
how to engage customers, devises experiments, and creates offers and assets.
Analytics help size opportunities, test impact, and derive insights from tests.
That content is then tagged, so that it can be associated with a trigger and be
ready to go when needed. Just three months after launching its war room, one
large multichannel retailer saw its testing speed go from 15 to 20 weeks to two
to three weeks, and testing volume increase from four to six per month to 20 to
30 per month.
4.
Distribution: Delivering experiences across platforms
Distribution systems are simple “pipes”
that send the ad or message that fed into them. Often they can be quite manual
and just blast out communications to wide segments of people with little
tailoring. But connect the CDP engine, with its predetermined triggers and
tagged content, to that distribution system and a formerly blunt marketing
instrument becomes a far more directed one sending specific messages to
distinct customer subsegments across all addressable channels.
That distribution system is often a
platform itself that lives in the cloud. Other “point” solutions (marketing
technology solutions for a specific task) can be connected into the CDP as
well. Sophisticated businesses have developed a library of APIs to help tie the
CDP into the “martech stack”—the marketing technologies that support the
function. The best distribution platforms create a feedback loop that sends
customer response, engagement, and conversion data back into the CDP. That
mechanism allows the CDP to grow and evolve (e.g., by responding to changing
business rules or customer propensity scores), refining successful outcomes and
eliminating unsuccessful ones. Remember Jane? If she received more than a
specified number of touches over a period of a week, the business rules would
suppress additional messages to protect her experience and sentiment toward the
brand.
Implementing
the data-activation framework
Not all data-activation efforts are
created equal. We recommend using a case-driven approach, maintaining a backlog
of tests ranked by opportunity, quantifying the impact of each potential use
case, and balancing it with the level of effort required to implement it.
Unlike a wholesale IT transformation,
deploying a CDP isn’t a replacement of current customer-data systems, but
rather an operational solution that can piggyback on existing systems. In our
experience, many marketers already have a large part of the
marketing-technology equation in house; they’re just not using it properly.
The promise of data-activated, one-to-one
marketing is not only possible but is now increasingly expected by today’s
customers. It is now the key to transforming simple customer transactions into
enduring relationships.
By Julien Boudet, Brian Gregg, Jason Heller, and Caroline Tufft
http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-heartbeat-of-modern-marketing?cid=other-eml-alt-mip-mck-oth-1703
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