Cracking the digital-shopper genome
Companies have more data at their fingertips than
ever, so why do online shoppers remain such a mystery? The solution begins with
bringing all the information together to form a meaningful picture of the
consumer.
An airline sends a regular customer an e-mail about a
special promotion on flights from New York City to exotic Caribbean
destinations. The company had noticed she’d recently browsed vacation sites.
The interested shopper eagerly clicks, landing on a page that shows all the
airline’s flights from all the cities it serves to all its destinations. By the
time our erstwhile traveler has waded through this screen to find flights from
John F. Kennedy International Airport and LaGuardia Airport, she’s no longer
seeing palm trees in her head—she’s seeing red. This is the fault line of the
modern digital-shopping experience, where reality falls far short of the
promise of receiving what you want, when you want it.
It doesn’t have to be this way. The solution
is cracking what we call the shopper genome: converting the vast amount of data
regarding consumer behavior and desires into meaningful insights. Today, the
vast majority of e-commerce companies focus on just one part of a shopper’s
genome, for example, product affinities like urban or designer styles, while
ignoring others, such as need states or emotional-connection points. Others
compartmentalize their efforts by using separate channels, teams, and
processes, resulting in a disjointed customer experience. That’s like
scientists decoding one set of chromosomes while ignoring all others.
Of course, we’re not suggesting that cracking
the shopper genome is easy. Until recently, technology barriers were the main
problem in combining insights across different elements of the organization.
Yet those barriers are starting to fall. As they do, more and more e-commerce
companies and their technology providers have an opportunity to develop a
coherent, comprehensive profile of their digital customers and effectively
engage them across the seven primary digital touchpoints: Internet display
advertising, e-mail, mobile advertising, search, shopping engines, social
media, and video. The rewards are substantial: we’ve found that companies that
are able to assemble larger swaths of the shopper genome to inform the way they
interact with customers can achieve revenue increases of 10 to 20 percent.
Mapping the digital shopper’s DNA
While every e-commerce
company wants a comprehensive view of its customers, few put in place a
disciplined system for collecting and organizing those insights. In the same
way that cracking the human genome requires decoding the DNA packages it
comprises, companies should aspire to create a complete picture
of the customer across a complete set of shopper characteristics (exhibit):
·
Customer
decision journey captures
customer behavioral pathways and attitudes at each stage of a purchase journey.
A customer may initially look for inspiration (ideas on what to buy) and then
information (product descriptions, reviews, informational blogging content)
before seeking the best way to buy a given service or product. Interactions can
be tailored to this process: for example, one technology manufacturer seeks to
identify shoppers on its website who are early in their journey and ensures
they don’t see pricing promotions, which are instead offered to visitors who
are closer to actually buying.
·
Digital-channel
preference highlights how a
shopper prefers to interact with a brand. These insights come from understanding
how customers interact through various digital channels—such as apps, e-mail,
social media, and video—and the ultimate value of that interaction. The most
sophisticated approaches then map these channel preferences to phases of the
customer decision journey to create a clear picture of the customer’s
cross-channel experience.
·
Product
affinity details what
products and product attributes customers prefer across brands and categories.
These insights are based on where customers spend their time while visiting a
website and on their product-purchase history, analyzed for “key preference
indicators” that help to create useful product taxonomies, such as whether the
customer shows a preference for a certain designer or style. In structuring a
taxonomy based on this behavior, retailer The Children’s Place, for example,
uses Demandware’s CQuotient to analyze language in customer reviews and selects
the most relevant words to inform a product’s metadata.
·
Response
to offers details how
customers respond to various offers and what incrementally results from those
interactions. These responses track how coupon offers, discounts, and loyalty
rewards, for instance, affect customer-shopping behavior at a level of detail
that allows an e-commerce company to understand which offers yield the most
cost-effective payoff by customer segment and, eventually, by individual
customer.
·
Life
moments and context looks at
episodes in a customer’s life (such as having a child, getting a new job, or
moving house) and behavior during seasonal events (such as at Christmas or on
vacation). This analysis provides a better understanding of the consumer based
on how much time typically elapses between purchases and whether the customer
is buying consumables or durables (such as furnishing a new home or office).
·
Demographics,
preferences, and needs provide
insights about shoppers based on information beyond interactions with a
specific e-commerce company. In recent years, there’s been impressive growth in
the quantity and quality of data aggregated about customers at the “abstracted
ID” level (that is, information that is not personally identifiable).
Sophisticated data aggregators such as Acxiom and Nielsen’s eXelate are able to
append not only demographic data such as age, gender, or zip code–based income
level but also preferences and intent deciphered from browsing behavior across
networks of hundreds of websites. For this to work over time, e-commerce
companies need to adhere to strict standards about keeping data abstracted and respecting
consumer privacy.
Pulling these insights
together to crack the shopper genome is both a technology andpeople challenge.
Each requires specific capabilities: the two main elements of the ever-evolving
technology landscape that must be mastered are data assembly and data-driven
action, while organizationally, companies need to not only find the right
people but also use them the right way.
Technology
Technology is emerging
to help provide a complete and dynamic picture of the shopper. At the heart of
customer intelligence is a customer-relationship-management (CRM) capability
that goes way beyond a tool to capture customer records or call-center
interactions and is able to integrate insights about customers in one place.
Advanced CRM capabilities facilitate data assembly by
providing a comprehensive view of “first party” customer data such as purchase
history, service interaction, website-navigation behaviors, and social-media
conversations. Additional insight into what customers do when they are not engaging
with the brand can be gained through third-party data. Data-management
platforms help companies aggregate customer data they already have in their
systems. Third-party “megadata” aggregators offer wide sets of ready-made
integrations with demand-side platforms.
When it comes to data-driven
action, recent innovations have enabled more dynamic ways of delivering
personalized experiences. For example, site-personalization providers such as
Certona, MyBuys, and RichRelevance dynamically alter the product selection
provided to different site visitors based on their shopping profiles, while
companies such as 360pi and Profitero allow e-commerce players to monitor
competitors, define business logic, and adjust prices accordingly.
This gets to one of the big advantages that
digital stores have over traditional physical locations: in theory, they can
render differently to every visitor on each visit. Retailer Kenneth Cole
reconfigures various elements on its website based on a visitor’s
click-behavior history—some see more product reviews, for example, while others
see more videos, images, or special offers. The algorithm the company uses
constantly learns which content works best depending on the customer and
renders the site accordingly. Similarly, some B2B sites present content
tailored to customers based on the industry they come from (perhaps identified
through Internet Protocol address lookups), while other e-commerce companies
tailor ads to users by delivering multiple forms of a purchased ad, customized
to any element of an offer (for example, product, price, promotion, or
presentation of a product).
People
E-commerce companies tend to have separate
teams, processes, and budgets for various functions. For instance,
responsibility for driving repeat buys and loyalty often rests with the e-mail
team, while reengaging visitors who did not buy during a visit is commonly
assigned to the retargeting team. Media buying is focused on traffic
acquisition and is separated from e-commerce, which is focused on conversion.
Such separation torpedoes the ability to deliver a great user experience. A
media buy may highlight a specific experience or offer, for example, but if the
e-commerce team has not created content to deliver on it, the user is left
confused and frustrated—just like our Caribbean vacation shopper. Technology
providers further complicate this challenge. E-mail service providers generally
don’t integrate with social-media ad systems, for instance, and on-site search
providers don’t integrate with CRM systems.
In our experience, companies can begin to
address these organizational issues by creating teams that “own” a customer
segment across the full journey. Central responsibility for this resides with
customer-segment—or “cohort”—owners, who oversee every phase of engagement from
strategy to execution and have broad cross-functional authority and decision
rights. These teams create a customer-segment profit-and-loss overlay on top of
existing channel- or product-centric ones. They also focus the annual marketing
planning and execution cycles on these specific segments. Ultimately, cohort
managers make sure that all customer communications are orchestrated,
harmonized, and synchronized across every touchpoint.
A deep understanding of the customer
experience across channels is at the core of this capability. Cohort leaders
put in place advanced-analytics teams that create a unified, 360-degree view of
the customer that constantly updates insights into the shopper genome. These
insights form the basis of an ongoing set of rapid test-and-learn initiatives
to continuously optimize customer interactions. To execute on the strategy,
cohort managers rely on marketing-channel managers who implement the campaign
in their respective channels, such as e-mail and website. And since technology
is the foundation of this capability, these teams have dedicated support from
the company’s marketing technologists to help navigate complex build-or-buy
technology decisions and find the most effective solutions.
Any comprehensive customer-engagement program
needs to start with a clear vision of what a complete digital experience looks
like. Making that a reality requires cracking the shopper genome—and building
the technology and organizational capabilities that deliver on the customer’s
increasingly high expectations.
byGadi BenMark and Maher
Masri
www.mckinsey.com/insights/marketing_sales/Cracking_the_digital-shopper_genome?cid=digital-eml-alt-mip-mck-oth-1508
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