analyze this: web style analytics...

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Analyze This: Web Style Analytics Enters The Retail Storeby Adam Silverman, April 16, 2014

For: eBusiness & Channel Strategy Professionals

Key TaKeaways

Retail store analytics Is Gaining a FootholdWith new technologies such as Wi-Fi, Bluetooth, and advanced video analytics entering the market, retailers are beginning to install and leverage these platforms to gain a deeper understanding of customer behavior in the store. These retail store analytics platforms provide insight that previously could only be realized in the eCommerce channel.

Deployments Benefit Operations, Merchandising, and MarketingThe benefits of retail store analytics can affect many groups within the organization, including loss prevention, marketing, merchandising, store operations, and eBusiness. Partnering with other internal departments can yield a common set of requirements that power many different goals, creating scale and efficiency for the organization.

significant effort Is Required To Deploy Retail store analyticsThe development and deployment of retail store analytics is riddled with challenges including lack of clearly defined use cases, the inability to marry store analytics data with other enterprise data, and customers’ concerns about privacy. Those retailers that break through these barriers will gain an advantage over their competitors.

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For eBusiness & Channel strategy ProFessionals

why ReaD ThIs RepORT

eBusiness leaders have a lot to offer retail store executives as they embark on injecting digital commerce technology into the physical store. Retail stores have been living in the analytical “dark ages” in comparison to digital channels, relegated to measurement technics that rarely take into account customer behavior. However, new advances in location-based analytics technologies are transforming how retailers win, serve, and retain customers within the physical store. These new insights will drive increased engagement with customers and greater efficiency in retail store operations. eBusiness executives have been leading teams responsible for detailed web analytics for many years and are well positioned to lead the evolution of retail store analytics. This report will shed light on new key metrics that retail store analytics will unleash and on how retailers should leverage these metrics to transform their physical stores.

table of Contents

Robust Digital store analytics Technology Is Rapidly evolving

Retail store analytics Is The Cornerstone Of Modern Retail

adoption Of store analytics Requires significant effort To execute

reCommendations

To Reap The Rewards Of Retail analytics, heavy Lifting Is Required

supplemental Material

notes & resources

Forrester interviewed 19 vendor and retailer companies, including alex and ani, american apparel, digby, euclid, motorola solutions, and retailnext.

related research documents

the emergence of Beacons in retailmarch 12, 2014

you are here: location analytics and the rebirth of Customer experienceFebruary 11, 2014

the retail eCommerce metrics that matteroctober 22, 2013

analyze This: web style analytics enters The Retail storePrivacy remains a significant Barrier For adoptionby adam silvermanwith tony Costa, Carrie Johnson, martin gill, Brendan Witcher, lily Varon, and rebecca Katz

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analyze this: Web style analytics Enters the retail store 2

© 2014, Forrester Research, Inc. Reproduction Prohibited April 16, 2014

ROBusT DIGITaL sTORe anaLyTICs TeChnOLOGy Is RapIDLy evOLvInG

No longer are retailers relying on antiquated technologies to measure the performance of their most expensive asset — their stores. Legacy methodologies of optimizing store performance based on optical traffic counters, ad hoc shopper studies, or merchandise analysis without browse behavior have given way to a new set of technologies. These new solutions allow modern retailers to add a new dimension of customer behavior to insights and allow them to operate in real time (see Figure 1). The rapid adoption of mobile devices by customers as well as advancements in Wi-Fi, Bluetooth, and video surveillance software have allowed retail store analytics to gain a foothold among digital retail businesses, although the depth of these analytics tools is lacking and immature (see Figure 2). The most common data collection technologies deployed in stores today include:

■ Wi-Fi. When a customer’s smartphone has Wi-Fi enabled, it is consistently seeking a Wi-Fi network to join. In doing so, the smartphone is sending out a small amount of information that contains a media access control (MAC) address that uniquely identifies the mobile device.1 Retail store analytics providers embed Wi-Fi “sniffers” in various locations in the store looking for these unique Wi-Fi MAC addresses in order to generate data on how the customer is interacting with the store.

■ Bluetooth. Similar to Wi-Fi, smartphones omit a unique Bluetooth identifier in order to seek out other Bluetooth devices to pair with. This unique signal can be identified by Bluetooth sniffers and used for retail store analytics. In addition, the emergence of Bluetooth low energy (BLE) allows for a new connection to be made with the smartphone, one where the store leverages beacons to transmit signals that can be interpreted by smartphones to deliver a message to the customer or to deliver location information to an analytics server.2

■ Video. The sophistication of video analytics is rapidly increasing, with new technologies that detect facial expressions and gender. Also, video analytics can even identify unique visitors across various locations, allowing for detailed path analysis of customers across the chain. An added bonus is that retailers can leverage existing video hardware that is often in place for asset protection reasons.

■ Global positioning system (GPS). Combining behavioral data generated outside the store and captured via a GPS with in-store behaviors can yield important insights to retailers. For instance, they can see the locations a customer visited prior to arriving at their store or send a targeted message to a customer when he enters their stores. In addition, GPS-based analytics does not require any new in-store hardware, making it a solution that can be rapidly deployed for outside-the-store analytics.




Figure 1 Retail Store Analytics Enables Real-Time, Customer-Centric Action

Source: Forrester Research, Inc.115390

Bre

adth

of

acti

vity

mea

sure

d

Reaction speed to measured activitySlow Fast

Lim

ited

Exp

ansi

ve

Legacy store analytics:• Traffic counters• Merchandise productivity• Labor management• Reactive loss prevention• Shopper studies

Modern store analytics:• Layout optimization• Merchandise productivity

coupled with customer behavior• Labor management coupled

with customer behavior• Proactive loss prevention• Marketing attribution• Store marketing performance




Figure 2 Customer Location Analysis Is Showing Value And Rapid Adoption


Creation

Negative

Low

Medium

High

Survival Growth Equilibrium Decline

Bu

sin

ess

valu

e-ad

d,

adju

sted

fo

r u

nce

rtai

nty

Ecosystem phase

Time to reach next phase:Trajectory:

< 1 year

> 10 years

1 to 3 years 3 to 5 years

5 to 10 years

Significant successModerate successMinimal success

Behavioral customer segmentation

Cross-sell and upsell analysis

Churn and attrition analysis

Customer device usage analysis

Customer engagementanalysis

Customersatisfaction

analysis

Customer journey and path analysis

Customer lifetime value analysis

Customer locationanalysis

Customer look-alike targeting

Customer propensity analysis

Next-best-action analysis

Product affinity andrecommendationanalysis

Sentimentanalysis

Social network analysis




ReTaIL sTORe anaLyTICs Is The CORneRsTOne OF MODeRn ReTaIL

Along with streamlining the operations of the physical store, retail store analytics also offers retailers the opportunity to personalize a customer’s experience. In many ways, the operations of a retail store analytics platform closely resemble that of traditional eCommerce analytics systems (see Figure 3). Retail store analytics drive:

■ More efficient and effective store operations. By understanding traffic patterns, conversion triggers, and customer behavior at a more granular level, retailers are finding tremendous benefit by aligning their in-store labor to meet customer demand. This analysis leverages historical sales, seasonal performance, and real-time data such as weather, promotional calendars, and in-store browse behavior to optimize the staff needed to maximize productivity. In addition, retailers are able to better measure and understand the effectiveness of sales associates by gaining insight on when, where, and for how long they are interacting with customers. Finally, loss prevention is improved, as retailers are able to use retail store analytics to augment video surveillance and identify new patterns of fraudulent activity. For example, American Apparel has improved the efficiency and productivity of its stores by implementing retail store analytics, yielding a 30% lift in sales and a 20% reduction in theft.3

■ Optimized store layout and merchandise performance. Retail store analytics provides retailers with the opportunity to analyze merchandise and real-estate performance in a similar way as their eCommerce channel. Conducting A/B testing of store layouts, understanding conversion rates by department, and employing shelf-specific measurement strategies allows retailers to assess and implement changes that drive increased conversions. A retailer working with RetailNext, an in-store analytics company, realized a 19% increase in sales after testing the removal of floor displays that prevented customers from effortlessly browsing merchandise in the store. In addition, Tesco implemented the “Broccoli Cam,” a shelf-specific analytics tool that measures the inventory of produce. By enabling insights in real time, Tesco is able to understand when a shelf is empty and immediately take action to restock.4

■ Improved customer experience through personalization and marketing effectiveness. Retailers and consumer packaged goods (CPG) companies can now leverage retail store analytics to better understand the performance of their marketing tactics in the store and can combine these insights with eCommerce behavior to create personalized marketing campaigns. By identifying unique customers in a store through opt-in programs like guest Wi-Fi, retail store analytics can improve the effectiveness of marketing by quantifying and attributing marketing spend to a specific customer and transaction. Kiddicare, a UK-based omnichannel retailer, leverages guest Wi-Fi to gather a unique customer identifier such as an email address and uses retail store analytics to track and analyze the behavior of that specific customer within the store.5 This data can be used in real time to present an offer via a mobile ad or can be sent to an email service provider (ESP) to trigger a personalized email based on in-store browse behavior.




Figure 3 Retail Store Analytics Has Evolved And Now Resembles Web Analytics


Key metrics What it is Equivalent web metrics

Awareness and adoption

The percent of chain/ brand visitors to store

The penetration level of acompany’s total customerbase in the offline channel

The percent of chain/brand visitors to site

The percent of customers who research websitepurchases in the store

The penetration of storebrowsers who plan to shoponline (this is often called“showrooming”)

The percent of customers who research store purchases on the site

Year-over-year storetraffic changes

Data in traffic changesin-store

Year-over-year site trafficchanges

Why it’s important

Provides insight into thetotal number of cross-channel shoppers over time

Sheds light on the level ofpre-shopping in eachchannel

Provides insight intovolume of inbound leads


Engagement

Traffic The number of visitors intothe store

Sessions

“Lease line”conversion rate

The percent of visitorsengaging with externalwindow displays who enterthe store

Marketing click-throughrates

Trial rate The percent of visitors whotry on items such asapparel or engage within-store productconfigurators or kiosks,compared with all visitors

Product detail conversionrate


Determines theeffectiveness of marketingprograms to drive trafficinto the store

Assesses the productivityof window displays

Identifies the effectivenessof product trials in the store

Trial abandonmentrate

The percent of visitors whotry on items such asapparel or engage within-store productconfigurators or kiosks anddo not buy

Cart abandonment

Repeat store visitors/new store visitors

The ratio between new andrepeat visitors in a store

Repeat site visitors/newsite visitors

Stops per store The number of dwell eventsduring a visit

Total page views

Illuminates the efficacy ofstaff and tools to driveconversion when a producttrial occurs

Highlights the mix of newversus repeat shoppers todetermine the effectivenessof both acquisition andretention programs

Shows the engagementlevel of a visitor




Figure 3 Retail Store Analytics Has Evolved And Now Resembles Web Analytics (Cont.)



Engagement (cont.)

Store dwell time Time spent in the store perperson

Time on site

Store conversion rate Total orders divided byvisitors

Site conversion

Department dwelltime

Total time spent in aspecific department pervisitor

Department page views


Assesses engagement withthe store and intent to buy

High-level performancescore for the store overall

Assesses engagement withthe department, themerchandise within thedepartment, and intent tobuy

Departmentconversion rate

Total orders divided byvisitors who have dwelledin a particular area

Department conversionrate

Determines the conversionrate for engaged customersby department who areconsidering a purchase

One-stop visitors Number of visitors who visitone area of the store andleave

Bounce rate Provides insight intoproductivity around thestore entrance andcustomer engagement

Path analysis How shoppers flow througha store experience

Path analysis Helps to surface the mostvisited store areas

Traffic to associate Number of visitors whohave engaged with a salesassociate

N/A Shows the visibility of anassociate to shoppers

Store associateproductivity

Sales influenced byassociates

N/A

Shopping cartperformance

Highlights how effective thestore associate is atconverting sales

Queue length The amount of time it takesa visitor to check out

Identifies any bottlenecksin the checkout process

N/AAssociate to shopperbehavior

Includes the number ofinteractions and the lengthof those interactionsbetween an associate anda visitor

Helps determine the impactassociate interactions haveon conversions andcustomer satisfaction




Figure 3 Retail Store Analytics Has Evolved And Now Resembles Web Analytics (Cont.)



Experience

Overall satisfaction Customer’s likelihood ofachieving her goal

Overall satisfaction

Net Promoter Score Data on advocates anddetractors

Net Promoter Score

Store associatesatisfaction

Customer satisfaction withstore associateengagements

N/A


Provides a gauge for happyshoppers

Correlates to word ofmouth, loyalty, and,ultimately, sales

Helps determine the qualityof the associate experience


Impact

Year-over-year saleschanges

Data in sales acrossvarious devices

Year-over-year saleschanges

Cost per order Total spend on an activitydivided by ordersattributable to that activity;most relevant for webmarketing

Cost per order


Provides an overallperformance score

Enables ranking of variousinitiatives by investmentlevels

aDOpTIOn OF sTORe anaLyTICs RequIRes sIGnIFICanT eFFORT TO exeCuTe

Retail store analytics holds the promise of meaningful improvement in operations and revenue growth. However, there are massive obstacles for retailers who embark on this journey. They face a world where new competitors are leveraging heaps of customer behavioral insights to transform the shopping experience, both online and in stores, and those retailers who balk at the opportunity to deploy retail store analytics will be flying blind compared with their more agile competitors. When investigating the common barriers preventing retailers from achieving success with retail store analytics, Forrester found that retailers must:

■ Address privacy concerns or deployments will fail. As retailers experiment with retail store analytics, they are met with resistance from shoppers who are wary of providing personal location information and unsure of how retailers would use this information. In fact, 44% of US online adults stated they are likely to stop shopping at a retailer that tracks their location in-store via their cell phone.6 However, customers are more willing to share their location information today than they were a few years ago. IBM reports that the willingness of customers




to share GPS location information nearly doubled, from 19% in 2011 to 36% in 2013.7 Even when this data is used to improve the customer experience, a lack of transparency can cause customer concern if they feel their personal information is being captured without their knowledge or approval. Nordstrom experienced this first-hand in 2013, when its retail store analytics deployment caused a good number of customers to voice their privacy concerns. Ultimately Nordstrom disabled its analytics implementation in response.8

■ Define business cases upfront. Without predefined use cases for retail store analytics, deployments by retailers are destined to underwhelm. Since retail store analytics offers new insights, retailers will need to respond by augmenting existing use cases. For instance, rather than just measuring traffic into the store, retailers can now measure the conversion rates of customers who enter after viewing a store window. This data can lead to beneficial and actionable insight around window display effectiveness and its relationship to store traffic.

■ Involve many teams to reap the most benefit. Opportunities to gain broader insights and benefits disappear when retail professionals work in silos. When different teams such as store operations, merchandising, asset protection, marketing, and eBusiness use retail store analytics, it creates scale and greater value for retail organizations.

■ Create insights from data. One omnichannel retailer told us that the heat maps within the retail store analytical tools were fantastic at pointing out where all of the doors and escalators were located. Clearly this is not actionable insight. In order for retail store analytics to transform the operations of the physical store, retailers must marry retail store analytics data with traditional retail data such as inventory position, staffing levels, customer lifetime value, online browse behavior, and external information such as weather or combine it with other historical behavior that may live within an enterprise data warehouse (EDW).

■ Distribute insight to the field through robust tools and training. Insights can originate at the headquarters in the form of marketing campaigns aimed directly at the customer or by augmenting the merchandise strategy to drive improvements in product effectiveness. In addition, this technology will allow store managers and associates to take action at a local level. To achieve this vision, retailers need to empower associates, local store managers, and regional managers with insight that can provide real-time guidance, as well as robust training on how to leverage these insights to drive results (see Figure 4).




Figure 4 iInside Creates Web-Style Dashboards Showing Associate And Customer Behavior In-Store


Source: iInside

R e c o m m e n d at i o n s

TO Reap The RewaRDs OF ReTaIL anaLyTICs, heavy LIFTInG Is RequIReD

Organizations must overcome significant barriers in order to yield benefits from retail store analytics. There is a real risk that implementations lacking actionable and meaningful insights will be deemed unsuccessful. eBusiness leaders have been at the forefront of leveraging analytics to improve eCommerce performance and are well equipped to mitigate risk and lead retail store analytics integrations. eBusiness leaders in conjunction with their store operations and business intelligence partners should:

■ Define and publicly state the value that customers will receive by tracking their behavior. It’s clear that customers balk at the notion of retailers tracking their individualized behavior in-store without providing value and indicating how they will use the data. Retail analytics leaders must provide real value to customers in exchange for viewing their browse behavior in-store. This may include offering free Wi-Fi, personalized offers in-aisle, or enhanced service from a personal shopper. In addition, retail organizations should clearly communicate whether they are capturing customers’ personally identifiable information or only collecting aggregated data that cannot be traced back to a specific person.




■ Leverage vendor goodwill and run a trial in a single store. A full deployment across an entire retail store chain is laborious and expensive, while a small deployment will provide insights with minimal cost. These insights can then be leveraged to build a business case and to onboard other departments. In some cases, retail store analytics vendors can leverage existing Wi-Fi or video infrastructure to build a pilot. Start small and test the results of retail store analytics platforms before pulling the trigger on a chainwide deployment.

■ Build use cases upfront before implementing an analytics solution. Since use cases will vary based on the type of retailer, the size of the retailer, and the goals of its stores, eBusiness leaders should work with their store operations, marketing, loss prevention, and merchandising leaders to define goals and success measurements for each constituent before choosing a technology solution.

■ Develop actionable KPIs that measure performance based on the defined use cases. Key performance indicators (KPIs) need to be available to leaders as well as store associates and must provide actionable insight that will directly improve the efficiency of store operations or the experience of customers within the store.

■ Seek a solution that meets the needs of all stakeholders. By leveraging a solution that can meet the needs of various teams, the expense of implementing and operating a retail analytics platform can be absorbed by many budgets, making the solution cost-effective for all. Be wary of introducing new barriers as more teams become decision-makers in the project. Start out with clear guardrails on how decisions will be made, including assigning an executive sponsor who can remove roadblocks as they appear.

suppLeMenTaL MaTeRIaL

Companies Interviewed For This Report

3VR

Alex and Ani

American Apparel

ByteLight

Digby

Euclid

Fractal Analytics

IBM

iInside

Locately

Midsize omnichannel retailer

Motorola Solutions

Nomi

Placed




enDnOTes1 A media access control (MAC) address is a unique identifier assigned to network interfaces such as

smartphones for communication on a network. MAC addresses are most often assigned by the smartphone manufacturer and are stored in its hardware, such as the card’s read-only memory or some other firmware mechanism. This information is unique to the device and allows for store analytics systems to identify specific devices within the network. Source: Cisco (http://www.cisco.com/c/en/us/td/docs/net_mgmt/cisco_secure_access_control_server_for_windows/4-2/configuration/guide/acs42_config_guide/noagent.html#wp1017547 ) and IP Location (http://www.iplocation.net/tools/mac-address.php).

2 To learn more about beacons and location technologies in the store, please see the March 12, 2014, “The Emergence Of Beacons In Retail” report.

3 Source: American Apparel (http://www.slideshare.net/fullscreen/G3Com/store-operations-superstar-awards-2012/16).

4 Source: Tescco’s YouTube page (http://www.youtube.com/watch?v=noa4SmYhjTA).

5 To learn more about Kiddicare’s deployment of store analytics, please read the case study. Source: “Kiddicare Uses RetailNext Products to Improve Multichannel Shopping Experience,” RetailNext case study (http://retailnext.net/wp-content/uploads/2014/01/RetailNext-Case-Study-Kiddicare.pdf).

6 Source: North American Technographics® Media And Advertising Online Benchmark Recontact Survey, 2013.

7 Source: IBM (http://www-935.ibm.com/services/us/gbs/thoughtleadership/greaterexpectations/).

8 Although Nordstrom was leveraging location analytics from Euclid in 2013, it halted the practice of tracking customers in the summer of 2013. To read more about location analytics, privacy, and Nordstrom’s decision to remove location analytics, read the following article. Source: Stephanie Clifford and Quentin Hardy, “Attention, Shoppers: Store Is Tracking Your Cell,” The New York Times, July 14, 2013 (http://www.nytimes.com/2013/07/15/business/attention-shopper-stores-are-tracking-your-cell.html).

RetailNext

Shopperception

Siteworx

Swarm Solutions

Symphony Analytics

Yellowfin



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