eoo how in-store retail analytics technologies are driving

eBook

How In-Store Retail Analytics Technologies are Driving Store Results

inReality.com

http://www.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


+ 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.


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


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


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


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:

* **

* *

**


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.


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.

USA +1.770.953.1500 | [email protected] +852.3998.3177 | [email protected]

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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.

eoo how in-store retail analytics technologies are driving

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