eoo How In-Store Retail Analytics Technologies are Driving ...
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eBook
How In-Store Retail Analytics Technologies are Driving Store Results
inReality.com
Retail’s Blind Spot—Moving Beyond Foot Traffic 2-3
In-Store Retail Analytics Solutions Today 4-6
In-Store Shopper Metrics By Solution 7-8
Key Use Cases for Driving Store Performance 9-10
In-Store Retail Analytics Vs. Privacy 11
Getting Started with a Pilot Program 12
Table of Contents
© 2018 InReality, LLC. All Rights Reserved.
© 2018 InReality, LLC. All Rights Reserved. | 2
It’s no secret that technology has transformed retail and reshaped the business for brick-and-mortar.
Amid rising consumer expectations, retail executives have their hands full trying to grow sales from
ever-declining foot traffic numbers. Consequently, optimizing each shopper visit is now becoming
increasingly critical. Traditional tools and data sources have failed to offer a sufficient remedy, but
advances in analytics technologies are transforming brick-and-mortar and driving in-store sales.
For years online players have been continuously
analyzing and optimizing their web stores to create a
more convenient shopping experience—and it shows.
E-commerce is growing almost four times faster than
physical retail.1
In stark contrast, brick-and-mortar has been an analytical
blindspot, limited to time-lagged, unactionable data and
measures of success that rarely consider actual shopper
behavior. Developments in shopper labs have provided a
step forward from expensive one-off research, but still fail
to provide unbiased, real-world insight into the shopping
experience and identify issues affecting sales in real-time.
However, thanks to declining technology costs and
growing capabilities, many brands and retailers are now
turning the page. They are using real-time analytics
to level the playing field and better tap into brick-and-
mortar’s hold on over 90 percent of all U.S. retail sales.2
Retailers and brands should read this ebook if
they would like to:
+ Learn more about in-store analytics and what
business value it brings to the table
+ Understand how to use metrics around shopper
behavior to create and drive an in-store funnel
+ Get exposed to retail analytics solutions available
today, with a breakdown of pros and cons
+ Understand key use cases for in-store retail
analytics
+ Learn how to get started with a retail analytics pilot
1. Business Insider
2. U.S. Census Bureau
Retail’s Blind Spot: Moving Beyond Foot Traffic
© 2018 InReality, LLC. All Rights Reserved. | 3
+ Shopper footfall metrics
+ Shopper traffic patterns/heat maps
+ Shopper demographic metrics
+ Shopper awareness, engagement & dwell metrics
+ Shopper conversion statistics
+ POP/display & brand performance metrics
+ Category/merchandising performance metrics
+ Correlation with third-party sources like: POS, digital shelf labels, and more
It’s clear that retailers and brands in brick-and-mortar recognize the importance of analytics to drive
results in store. In fact, 72% of retail leaders demand fact-based decisions from their organizations,1 and
over the next two years, 376% more will be spent on analytics to improve the customer experience.2
Today many are turning to robust and cost-effective in-store retail analytics solutions. These solutions
can power simple, real-time dashboards and correlate third-party data sources. In-store metrics include:
Retail’s Blind Spot: Moving Beyond Foot Traffic
Introducing the In-Store Funnel
For years door counts and POS data have been the key data sources in-store, but now many are questioning
whether traditional measures of store success are sufficient to meet the challenges of today’s modern landscape.
To optimize declining foot traffic and meet growing shopper expectations, many brands and retailers are taking
a more data-driven approach—building an in-store funnel to better understand and maximize exactly what
happens between the time the shopper enters the store and makes a purchase, i.e the blind spot.
1. Accenture
2. Deloitte
Through this approach brands and retailers
are seeing exactly what works and doesn’t
in attracting and engaging in-store shoppers
to move them down the funnel. As a result,
they are able to constantly adjust and
optimize specific points of influence (i.e.
merchandisers, point-of-purchase displays,
store layouts, etc.) based on shopper
behavior to better predict and drive sales.
TRAFFIC
BLINDSPOT
CONVERSION CONVERSION
ENGAGEMENT
TRAFFIC
AWARENESS
AUDIENCE
VS.
© 2018 InReality, LLC. All Rights Reserved. | 4
In-Store Retail AnalyticsSolutions Today
Video Analytics
1 2Video Analytics
Today, there are four main solutions for uncovering the in-store metrics discussed previously. Here is a
breakdown of each solution with pros and cons, listed from most to least comprehensive. Keep in mind,
some vendors offer combinations of these options, and some brands and retailers have combined on their own.
Beacon
3
1 | Video Analytics via Facial Detection Small sensors embedded in shelves, displays, signage, kiosks, and more to capture more granular
shopper-level insights. Ceiling-level traffic insights can also be combined for a more comprehensive view.
(Facial Detection) (Ceiling Sensors) AnalyticsWiFi
4
Analytics
Best For:
Retailers and brands looking to understand very specific points within a store—analyzing down to a display or area of a planogram; captures all the in-store metrics outlined earlier
Pros:
+ Only solution that measures specific points of influence with detailed shopper behavior metrics
+ Wide flexibility in terms of applications/use cases
+ Some vendors eliminate privacy concerns by using completely anonymized technology where no video or personal shopper data is ever stored
+ Provides best shopper sample
Cons:
Requires more upfront effort—determining sensor placement and installation
© 2018 InReality, LLC. All Rights Reserved. | 5
3 | Beacon Analytics
Beacons are small, wireless Bluetooth devices. When paired with a mobile app, beacons can track Bluetooth-
enabled smartphones within a certain proximity. Tracking is not limited to just smartphones, smartwatches or
bluetooth sensors attached to things like shopping carts can be used additionally or as an alternative.
In-Store Retail Analytics Solutions Today
2 | Video Analytics via Ceiling Sensors
Small sensors placed in store ceilings.
33% of U.S.adults do not own a smartphone.
1 Pew Research Center
33%
Best For:Retailers and brands seeking limited insight into shopper footfall and traffic patterns on known/opt-in shoppers
Pros: + Fairly inexpensive
+ Easy to implement
Cons: + Requires shoppers download a mobile app
+ Poor sample—only tracks within a certain proximity and known shoppers, i.e. those who have opted-in, with Bluetooth turned on and the required mobile app downloaded (Additionally, keep in mind that 33% of U.S. adults do not currently own a smartphone.)
+ Privacy concerns around tracking the shopper’s smartphone
Best For:
Retailers seeking more precise shopper footfall and traffic analytics of all store shoppers
Pros:
+ Does not depend on the shopper’s smartphone, signal strength or a shopper having either bluetooth
or WiFi turned on like the two following solutions
+ Sometimes existing surveillance cameras can be used
Cons:
Can require more upfront effort if existing cameras are not in place
© 2018 InReality, LLC. All Rights Reserved. | 6
4 | WiFi Analytics
Shoppers are tracked while in-store via their smartphone, whenever they turn on WiFi.
In-Store Retail Analytics Solutions Today
4 | Other
In addition to WiFi, beacon and video analytics, there are a few other options out there, such as GPS tracking
and mining video. However, these options haven’t had much application in the field yet or don’t offer any
additional value or cost benefits that made them worth exploring.
Pros:
+ Inexpensive
+ Easiest to deploy of all solutions
+ WiFi usage is becoming widely accepted by shoppers
Cons:
+ Poor sample—only captures analytics on the subset of shoppers with smartphones who also
have WiFi turned on
+ Unreliable—WiFi signals are easily lost or interrupted
+ Not precise enough for shopper behavioral, demographic and conversion insights—only
provides approximations within a few feet of where shoppers are in-store, even with
improvements like triangulation
+ Privacy concerns around tracking the shopper’s smartphone
+ May become obsolete—companies like Apple, AVG Labs and Blackphone are all working
on solutions to protect shoppers’ anonymity and reduce mobile tracking accuracy
Best For:
Retailers seeking limited insight into shopper footfall and traffic patterns
© 2018 InReality, LLC. All Rights Reserved. | 7
Metric Application WiFi Beacon Video(Ceiling)
Video(Facial)
Shopper Traffic
Total TrafficTotal # of shoppers that enter the store
AudienceOf total shoppers, the number that walk by a specificcategory/merchandising/product
Unique VisitsCounts of new versus returning shoppers within a specfic timeframe and/or area
Demographics Shopper age and gender counts
In-Store ShopperMetrics By Solution
Retail analytics is no longer just for retailers and is much more than just footpath mapping and traffic
analytics. Today retail analytics offers both brands and retailers the insights they need to orchestrate
shopper behavior and drive sales in-store. The benefits of retail analytics also affects many functions
within the organization, including marketing, merchandising and store operations.
Sample of Available In-Store Metrics by Solution:
* **
* *
**
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Metric Application WiFi Beacon Video(Ceiling)
Video(Facial)
AwarenessTime
Shopper time spent looking at a specific category/merchandiser/display/product
Dwell Time
Shopper time spent around a specific category/merchandiser/display/product
EngagementTime
Shopper time spent interacting with a specific display or product
Stopping Power
Statistics on audience, awareness, engagement and dwell times around a specific category/merchandiser/display/product
ConversionPower
Category/merchandiser/display/product performance statistics relative to other store locations or regions
Heat Maps/Path Analyses
Traffic patterns around the store, showing where shoppers travel and dwell
In-Store Shopper Metrics by Solution
Store/Category/Merchandising/Display Performance
* *
* *
*
*
*Based on assumption that shopper smartphone and connection requirements are met for WiFi and
Beacon (reference pages 5-7 for more on these requirements). Platform capabilities vary by vendor; make
sure to ask for details. This summary is based on best-in-class platforms across each solution category.
*
*
| 9© 2018 InReality. All Rights Reserved.
Key Use Cases for Driving Store Performance
More data is the last thing retailers and brands need. However, in-store retail analytics metrics can be
used for a wide array of applications, and because the data is real-time, simple to read and often setup
around the organization’s unique KPIs, brands and retailers are hitting the ground running, putting
learnings into action. Following are some key general use cases for brands and retailers:
Use Cases for Brands:
1 | Optimize In-Store Marketing Spend & Sales
By using real-time A/B testing and comparing sales data to awareness, enagement and dwell times, along
with traffic and audience analytics, brands can identify which displays or other in-store investments are
most effective, with possible comparisons from store-to-store and region-to-region or even by gender
and age. This information can identify “winning” product displays and best practices, while simultaneously
directing spending to maximize ROI in-store.
2 | Instantly Tailor In-Store Promotions & Experiences to Shoppers
Using analytics data in real-time, messaging and promotions based on personas and the shopper’s journey
can be triggered while shoppers are actively engaged to drive more relevant experiences. This can be done
using digital screens (kioks, displays, monitors, etc.) based on traffic, shopper demographics, proximity, and
more. All campaigns can be run remotely, without ever needing to step inside the store.
3 | Solve the Omnichannel Puzzle
With an understanding of shopper traffic and behavior in-store, brands can also get a better picture of their
shoppers’ journeys and gain invaluable insights around how to best influence and improve the shopping
experience. Some platforms will actually allow the brand to segment this data by location/region, date/time/
season or even demographics (age/gender) to identify further trends. Additionally, by comparing traffic
benchmarks against traffic during the run of specific out-of-store campaigns, brands can also gain some
perspective on how multi-channel efforts affect or drive in-store performance.
© 2018 InReality, LLC. All Rights Reserved. | 10
1 | Drive More Revenue Per Square Foot
Identifying the right levers to pull are essential to in-store success. Having a real-time understanding of what
attracts, engages and converts shoppers can be crucial to determining why a particular store, product
category, or brand is underperforming, before the numbers come in and weeks have passed with no sales.
Moreover, it can provide valuable insight into shopper interests, key indicators of purchasing behavior,
and high-performing versus low-performing points of influence in-store to help optimize product mix and
maximize ROI on investments throughout the store.
2 | Improve Operational Efficiency & Empower Sales Staff
Relevant data can also be delivered to sales staff in real-time. For example, using phones or tablets to
ping them when a consumer has spent a long time around a specific area or product and so it might be a
good time to approach them. With details about that specific product already at their fingertips, the sales
associate would already be prepared to offer that consumer a more personalized shopping experience.
Additionally, with shopper traffic data and heat maps, retailers can optimize staffing allocation in different
store categories/departments by time/date to better service shoppers. Retailers can also easily identify
and remedy things like long wait times, low-traffic or congested areas, and metrics such as average visit
duration compared against sales data can be a good indicator of overall experience measurement and help
compare performance across store locations/regions.
3 | Improve Department/Category/Merchandising Performance
By understanding how shoppers navigate their store(s), how they behave, how much time they
spend in specific areas, what attracts their attention, and their demographic profiles, retailers can optimize
their store footprint, product placement and cost per shelf. For example, they’ll have the know-how to place
key merchandise in high-traffic routes to drive conversion, improve sell-through and promote merchandise.
They’ll also be able to identify the most cross-shopped categories: an opportunity to not only improve the
shopping experience, but to also help drive revenues by relocating these departments closer together or
strategically placing key SKUs from these highly cross-shopped categories/departments.
Use Cases for Retailers:
Key Use Cases for Driving Store Performance
| 11© 2018 InReality, LLC. All Rights Reserved.
33% 67%
of shoppers expect the companies they engage
with to know more about them.1
are willing to share personal data, but only
in exchange for some perceived value.2
Shopper Views On Privacy:
The good news is that shoppers are no longer surprised by companies’ interest in their lives. In fact,
shoppers will exchange their information for some perceived value.
1 Accenture
In-Store Retail Analytics vs. Privacy
Shoppers’ willingness to give up personal data will continue to improve as they continue to see the
benefits of receiving better, more targeted experiences and offers, just like they’ve had for years online.
However, to further protect their shoppers, retailers and brands do also have options available (see
pages 5-6) to keep shopper data completely anonymized.
2Accenture
| 12© 2018 InReality, LLC. All Rights Reserved.
Will Privacy Concerns Hinder Brick-and-Mortar’s Intelligent Evolution?
Getting Started with a Pilot Program
In-store retail analytics offers a great opportunity for marketing, merchandising and operational
improvement and revenue growth. However, for newcomers, the big question is: how to get started?
A pilot program is a great way for brands and retailers to dip their toes in the water and quickly get a
taste of what the solution could offer their particular organization within a relatively low cost.
General Overview of How a Pilot Program Works:
Step 1: Technology setup
This first step varies based on solution—video will likely require site surveys to determine ideal
sensor placement and execute installation; beacons may also require site surveys and bluetooth
sensors will have to be placed in-store; and WiFi will require simply establishing a WiFi network.
Step 2: Determine KPIs
Defined KPIs to be proven are mapped against analytics capabilities.
Step 3: Baseline data, insights & learnings
Data are accessed through a real-time dashboard, then insights and learnings are extracted.
Step 4: Test period
Using a small number of test stores and a control group of stores, changes are made per the
learnings and tested for impact against set KPIs; this happens within hours/days, in real-time.
Step 5: Adjustment and iteration
From the changes made, a “winner” is identified. This process should be repeated for continued
optimization in real time and overall performance is measured.
Moving forward, as shopper behavior continues to change, keeping a learning environment going
in-store will be essential to optimizing brick and mortar.
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© 2018 InReality, LLC. All Rights Reserved.inReality.com
About InRealityInReality provides a SaaS platform paired with IoT sensors to help brands and retailers
anonymously track and direct in-store shopper behavior, optimizing conversion of products,
displays, categories, departments or full stores.
With its retail analytics and responsive technology software and insights-to-action services,
InReality provides real-time, hyper-relevant experiences and works with clients like The
Home Depot, Anheuser-Busch and Tempur-Sealy.
Headquartered in Cincinnati with domestic offices in Atlanta and internationally in Hong
Kong, InReality is concentrated on the retail market in the beverage, furniture and bedding,
electronics, health and beauty, convenience, and home improvement industries.