Who’s shopping where? The power of geospatial analytics in
omnichannel retail
Using
advanced geospatial analytics, retailers can now quantify the true economic
value of each of their stores across channels—and they’re uncovering surprising
insights.
The wave of store closures across the US retail sector continues. In 2017
alone, more than 7,000 stores went dark, unable to withstand consumers’ rapid
migration to e-commerce, the explosive growth of direct-to-consumer brands, and
the glut of retail square footage in the heavily overstored US market. Retail
space per capita in the United States is 15 to 20 times that of other major
developed markets. Customer traffic at malls has been steadily decreasing.
Margins are declining in almost every retail category. Given these trends, it’s
becoming harder to justify keeping expensive brick-and-mortar stores open if they don’t meet sales expectations.
Already, in the first few months of 2018, retailers have announced plans to
shutter an additional 3,800-plus US stores.
Unfortunately,
retailers often make the wrong decisions about which stores to close, thus
inadvertently hurting their business further. They also overlook valuable
opportunities to expand their market presence and unlock growth. The main
reason is that they’re using outdated metrics: many retailers continue to use a
combination of trend analysis and “four-wall economics” to assess store
performance—that is, they’re still primarily taking into account the sales and
profits that the store generates within its four walls, without considering its
impact on other channels. This assessment then affects other decisions,
including the store’s payroll, labor coverage, and sometimes
inventory selection. However, consumers today shop across channels: they might
visit stores to look at products and then eventually buy them online, or they
might research a product online and then buy it in a store. In this
environment, the traditional four-wall metrics are, at best, incomplete
indicators of a store’s potential.
The most sophisticated
retailers are now closely examining the interplay between offline and online customer decision journeys. They’re taking an omnichannel
view of store performance—allowing each store to “get credit” for all the sales
in which it played a role, whether those sales happened offline or online. In
doing so, retailers are getting a more accurate picture of each store’s total
economic value and making better decisions about their omnichannel presence.
Their secret weapon? Advanced geospatial analytics.
Outside the four walls
Physical stores aren’t
going away. We estimate that in-store sales will still make up 75 to 85 percent
of retail sales by 2025. That said, the physical store is no longer just a
place to buy products. A store now plays several possible roles: it might serve
as an experiential showroom for products, a fulfillment center for online
orders (or a convenient place for returning or exchanging online purchases), a
hangout where groups of friends can try things on and take selfies that they
then post on social media, or
a destination for those seeking ideas and inspiration. It’s entirely possible
for a store to have weak sales and profits within its four walls while being a
strong contributor to the retailer’s overall performance.
Advances in data and
analytics can help a retailer quantify both a store’s halo effect (positive)
and its cannibalization effect (negative)—in other words, how a store’s
existence influences the performance of the retailer’s other sales channels.
Retailers have long recognized that a store can have a halo effect, but it has
traditionally been thought of in marketing terms—that is, a store can raise
awareness of the retailer’s brand, just like a billboard or a TV commercial.
Viewed as such, the halo effect has been difficult to measure. However, in an
omnichannel world, a store can do more than just raise awareness; it can drive
sales through other channels, and vice versa. McKinsey research suggests that a
store’s e-commerce halo can account for 20 to 40 percent of its total economic
value.
A new era in data and analytics
For decades, retailers
have been mining a variety of data sets—point-of-sale information,
demographics, market trends, and so on—to learn about customers and serve them
better. Today, thanks to the availability of new types and sources of data,
it’s possible for retailers to gain a much deeper understanding of consumers
and markets. Retailers have access to more consumer-behavior data than
they’ve ever had before, in the form of opt-in e-receipt programs and
anonymized mobile-phone location data.
The aggregated data can
shed light on not just the quantity but also the quality of customer traffic.
This information allows retailers to get a detailed picture of how people move
and interact within a market, as well as how they behave across both offline
and online channels.
And it’s not just that
there’s more data. Companies now also have access to increased
analytical horsepower in machine-learning models. These models can
mine big data assets and help generate granular, actionable insights at the
micromarket level.
At the most
sophisticated retailers, geospatial data and analytics are often owned by a
strategic advanced-analytics group. The group, which can be centralized or
reside within a specific function, drives the use of advanced analytics across
silos. It delivers cross-cutting insights that bring together the priorities of
various functions, including marketing, sales, finance, and real estate.
The combination of
advanced geospatial techniques and machine learning,
applied to cutting-edge data on consumer behavior, is unleashing powerful new
insights for retailers. In particular, it’s helping retailers make better
decisions about expanding or contracting their store networks. It’s also helping
them develop store-level action plans to improve performance. In addition, some
retailers are using these insights to mobilize their sales force and prioritize
their investments.
Geospatial analytics in action: A case example
Consider the case of a
global specialty retailer that sells its products through its own
brick-and-mortar stores, an online store, and wholesale accounts. The
retailer’s sales were declining in the face of strong competition. For insights
into how to reverse its sales trend across the network, the company turned to
geospatial machine learning.
A team of data
scientists built an analytical model customized for the brand, leveraging both
internal and external data. Testing hundreds of variables, the team used
geospatial machine learning to identify the factors that have the greatest
positive or negative effect on a zip code’s total sales.
Based on these drivers,
the team was able to predict the retailer’s potential sales in each zip code
and each store, and to compare potential sales with actual sales. Then, using
geospatial simulation, it estimated each store’s impact on wholesale and online
sales.
The team was also able
to isolate the unique factors that contribute to a strong e-commerce halo. It
found that, in general, a store has a strong e-commerce halo if it is a larger
store located in an area with a high proportion of young and urban
professionals. Other, more surprising factors that contribute to a strong
e-commerce halo: being far from other same-brand stores, being in a high-traffic
retail environment such as a high-quality mall or a power shopping center, and
having low tourist spend (which means most of the store’s customers live or
work nearby).
The retailer used these
insights to identify which stores weren’t living up to their sales and profit
potential and which micromarkets contained untapped growth opportunities.
Further analysis revealed that the retailer could optimize the omnichannel
value of its store network and achieve a 20 percent gain in EBITDA by closing,
relocating, or reformatting stores (for instance, turning a fullpriced store
into a digital showroom).
The retailer then
created market-level “battle plans” for its store network: which stores to
reformat or close, where to increase its presence either via new stores or
deeper wholesale penetration, and what the sequence and scope of its
investments would be.
Getting started
In kicking off a
geospatial analytics effort, every retailer will have a different starting point.
We recommend that companies first conduct an internal inventory of data
availability and advanced-analytics capabilities.
Some retailers have
limited data (for example, low visibility into wholesale accounts), siloed
business units, and only a handful of data scientists and analysts, if any.
These retailers should build their minimum data requirements and consider
partnering with external providers or acquiring analytics capabilities
outright.
On the other end of the
spectrum, some retailers already have extensive external data partnerships,
consistent and reliable data-sharing processes with their wholesale accounts,
and senior management focused on omnichannel success. Such retailers can opt to
build a strong data-science team with experience in geospatial analytics. That
team would be tasked not just with performing the analyses, but also with
generating useful insights that can be easily integrated into real-time
business processes and decision making.
Regardless of their
“build, buy, or partner” decision, retailers must constantly strive to break
down business silos. If the heads of the retail, e-commerce, wholesale,
marketing, real-estate, and finance functions all operate independently of each
other and have few or no cross-cutting goals or initiatives, the company as a
whole won’t be able to make the best omnichannel decisions.
In our experience,
retailers can quantify performance gaps, uncover growth opportunities in their
go-to-market strategy, and reap early wins from advanced geospatial analytics
within 6 to 12 months—particularly when an empowered, cross-functional team is
leading the charge. Successful pilots in one or two markets can quickly build
buy-in for global rollout. By harnessing the power of geospatial analytics,
retailers can capture the omnichannel customer—which, in the near future, could
very well be the only kind of customer there is.
https://www.mckinsey.com/industries/retail/our-insights/whos-shopping-where-the-power-of-geospatial-analytics-in-omnichannel-retail?cid=other-eml-alt-mip-mck-oth-1808&hlkid=4261a6a6f5f9470f9ffcc1970a0b3e3f&hctky=1627601&hdpid=e257e96e-8ca5-401d-b75a-d4616e647b73
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