Analytics Insights Deliver Competitive Differentiation - RIS

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AnALYtics insights DeLiver competitive DifferentiAtion 1

mastering analytics has become vitally important for retail-

ers’ ability to differentiate themselves from their competi-

tors. it’s always been critical for retailers to be able to iden-

tify who their most valuable customers are, but today they

need predictive analytics solutions that provide insights

into the specific engagement tactics that will keep these

valuable customers loyal in the long term. today’s sophis-

ticated analytics also give retail organizations the tools to

handle omnichannel operational challenges, identifying the

optimal location from which to fulfill an order to maximize

both efficiency and profitability. As analytics solutions tap

into newer data sources, such as social media and location-

based information, they will help retailers not only identify

new trends but predict which ones will have a lasting impact

versus those that will simply flash and fade. By providing

insights into all aspects of an increasingly complex purchas-

ing process, analytics solutions let retailers stay in step with

– and even move ahead of – today’s empowered consumer.

Charting the Path That Links Technology and Business Goals

JULY 2013

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Analytics Insights Deliver Competitive Differentiation

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“Knowledge is power,” according to the old adage. retailers today would amend this maxim to read “Knowledge is profitability.”

And the tools to gain that knowledge, and the profits that accompany it, are business analytics solutions.

these are the simple truths behind an increasingly complex set of challenges for individual retailers, and for the industry as a whole, in the business analytics arena. one impossible-to-ignore factor is that there is so much more data available to gather, digest and an-alyze. the citi research 2013 retail technology Deep Dive quotes estimates that 2.5 quintillion bytes of data are created daily: “such an increase means that 90% of the data in the world today has been created in the last two years. the majority of new data is unstruc-tured, Web-based, and outside the company. 70% of unstructured data is stale after only 90 days.”

for retailers specifically, technological changes in the way customers shop have opened up vast new opportunities for gathering data. the rise of online and mobile commerce provided retailers with the abil-ity to trace every click, view, screen swipe and query on the road to a customer’s purchase decision. Brick-and-mortar stores had been lagging their digital coun-terparts, but recent solutions that anonymously track signals from shoppers’ mobile devices, along with increasingly sophisticated video analytics, are rais-ing in-store specificity levels about who shoppers are, what they’re looking for, and how they arrive at a buy/don’t buy decision.

And let’s not forget that data gathering, no matter how granular or sophisticated, is just one element in re-tailers’ ability to use business analytics effectively. they are anxious to use data for solutions such as predictive analytics – potentially the most valuable type of analyt-ics because they can guide key decisions about market-

Source: Citi Research 2013 Retail Technology Deep Dive

of the data in the world today has been created in the last two years. the majority of new data is unstructured, Web-based, and outside the company.

90%

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ing, merchandising, supply chain, direct commerce and store operations, including workforce scheduling.

in fact, predictive analytics was selected by 42.5% of retailers as a “top 10” technology for 2013, tying for third place with mobile pos in the RIS/gartner 2013 retail technology study. (campaign analysis and fore-casting, another key analytics area, was first at 50%.) Yet only 10% of respondents currently have up-to-date predictive analytics solutions in place, although 18% have begun an upgrade in this area and 25% are plan-ning to do so within the next 12 months.

nor is this the only analytics area where retailers are moving quickly to keep up in the knowledge race. on-line retailers are bolstering their staffs in the area of marketing analytics, another key focus area. According to a recent forrester research/shop.org study, 40% of these retailers plan to hire for open positions in market-ing analytics in 2013, reflecting their desire to wrestle down volumes of marketing data to create more effec-tive interactions and an improved customer experience.

following are five mileposts retailers will need to reach on the road to making effective use of business analytics in a complex, data-drenched environment:

Advance from Business intelligence to Business AnalyticsBusiness intelligence (Bi) and business analytics

are often used interchangeably, and even within the strictest definitions there is some overlap between them. According to eKn, Bi refers to tools and soft-ware focused on retrieving, analyzing and reporting data stored in an existing enterprise database, with tools typically focused on querying, reporting, online Analytics processing (oLAp) and alerts. in general, Bi is concerned with understanding what has happened in the past, and delivering that information via regular reports and/or structured queries.

of retailers identified

predictive analytics

as a top 10 technology

for 2013, but only

10% have up-to-date

solutions in place.

Source: RIS/Gartner 2013 Tech Trends Study

42.5% 42.5%

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Business analytics consists of a broader set of tools and solution sets, which can be used for functions such as playing out “what-if” scenarios that can pre-test and predict the impact of specific actions. Busi-ness analytics can also provide the ability to detect previously undiscovered patterns within data, for ex-ample identifying areas where costs or manpower are higher than the norm, or where sales/margins are low-er. such discoveries can point retailers to issues that need attention or uncover new opportunities they had been unaware of.

the EKN State of the Industry 2013 Future of Re-tail Analytics report defines Business Analytics as the “art, science and philosophy of utilizing insights to improve decision-making in the context of a par-ticular business function or process. it is focused on a continuous, ongoing, and iterative exploration of past business or business process performance to gain insight, drive business planning, and deliver a particular business outcome.”

the difference is more than just technical and se-mantic. eKn identifies the ways today’s leading re-tailers, including Best Buy, cvs, Amazon, target and Walmart, are leveraging analytics to: •Gainadeeperunderstandingoftheircustomers’ behaviors, needs and preferences to build a more personal relationship

•Improvemarketingeffectivenessthroughmicro-targeting, personalization and delivery of context and channel sensitive promotions and offers that increase the likelihood of purchase•Optimizethesupplychaintoensurethemostprofitable outcome in terms of demand fulfillment balanced against cost of carrying excess inventory•Determinepricing,includingbundleandbasketpricing, based on the value customers attach to their needs at any given time

the rise of online and mobile commerce provided retailers with

the ability to trace every click, view, screen swipe and query on the road

to a customer’s purchase decision.

http://www.eknresearch.com/2013/05/24/the-future-of-retail-analytics/

I N D U S T R Y I N S I G H T S

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iInside, a WirelessWERX company, is a market-leading provider of highly detailed indoor shopper analytics and insights to brick-and-mortar retailers and high-traffic venues. through patented, low-cost and easy-to-install technolo-gies, the company offers privacy-compliant statistical data and solutions that aggregate consumer traffic behavior in order to help cli-ents increase revenue and improve operational efficiency. With more than 24 global patents and over 30 years of unrivaled expertise, iinside is the best-in-class choice for consumer analytics and indoor positioning solutions.

Q: What do in-store customer traffic solutions add to retailers’ analytics efforts?JON ROSeN: The ability to add traffic data to other performance measures addresses a very retail-specific challenge and opportunity. Precise retail traffic data paints a path-to-purchase picture previously unavailable to the retailer. Retailers now can learn exact-ly where people go in their stores, for how long and in what order. That data translates into the ability to manage the business: Operations, Merchandising and Marketing, in order to increase shopper time, increase visit frequency, sequentially-merchandise and decrease wait times at the registers. The effect of any action, from store layout changes to promotions, can be measured in traffic and at the register. Advanced solutions also include location-enabling mobile applications to communicate with shoppers in the store, based on location. We can also now connect online and offline purchase behavior. It’s a true sea-change in retail data resources, and as we grow, we’re focused on aggre-gated data and other privacy protections.

Q: What kinds of questions does this allow a retailer to get answers to?ROSeN: There are at least two segments of unique, valuable data: performance re-porting and cause-and-effect. With performance reports, retailers can measure each de-partment’s performance across several factors including conversion, draw, dwell-time, repeat visits and more. Various individuals from store GMs to merchandisers and others can drill down across the chain to evaluate performance by store and by department. As a result, resources to increase departmental conversion and other performance metrics have never been more powerful.

Q: How can retailers make the best use of this data?ROSeN: Retailers will want to look at which departments are doing better than oth-ers using key measures like conversion. Let’s take two different stores with TV depart-ments that have similar revenues. But with traffic data we learn that the TV department at store 61 converts at 4%, while store 62 converts at 9%. Armed with that data we are now aware of the need to find reasons for sub-optimal conversion and resolve them. Are the departments merchandised the same way? Is one location better within the store? Is the employee training better in one store versus another? The store manager can see how well they’re performing, by department, against regional and national averages, and these better performers can influence the retailer on what constitutes best practices.

For staffing, because traffic solutions measure how many and how long shoppers are in each department, they can help establish correlations between staffing levels and their impact on merchandising and marketing. Are shoppers in the store for 15 minutes, or for an hour? That’s a big staffing question, especially in a store where associates are assigned to a particular department.

A retailer dealing with showrooming might discover exactly which departments were being “showroomed,” i.e. where people were visiting but not buying and at which times throughout the day or week. Perhaps shoppers weren’t just looking for a lower price. Perhaps they couldn’t make a choice of product, or maybe there was no one avail-able to help. If traffic data tells us where and when the behavior is occurring we have another key resource to increase sales and enhance the customer experience.

“Precise retail traffic data paints a path-to-purchase picture previously unavailable to the retailer. Retail-ers now can learn exactly where people go in their stores, for how long and in what order.”

JON ROSeN, execUTIVe

VIce PReSIDeNT, IINSIDe,

A WIReleSSWeRx cOMPANY

Customer Traffic Provides Keys to Conversion Analytics

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•Spotflashtrends,suchasacelebritysightingina particular pair of jeans, that have an impact on demand to be able to turn them into revenue capturing opportunities.

this is a list that any retailer, large or small, would rate as highly valuable.

gain insight by mixing Data from multiple DomainsAnalytics solutions, and the insights they produce,

have often focused on the needs of an individual de-partment. this data “silo-ization” has often been an

there’s a major mismatch between how

retailers believe their analytics resources

should be structured versus the current

organizational reality.

shared services format for analytics

each department responsible for own analytics

resources

it primarily responsible for

analyticscurrently structured

should be structured

Source: EKN 2013 State of the Industry Research: The Future of Retail Analytics

Organizational Structure of Retailer Analytics Resources

71%18% 58% 53%5% 5%

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requirements for

Business AnalyticsStrategy•Mission:LeverageBusinessAnalyticsinsightstohelpaccomplishkey business goals (e.g. growth, customer engagement, profitability)•MovefromBusinessIntelligencefocusonrear-viewanalysistoforward-looking, ‘what-if?’ predictive analytics•Gain360-degreeviewofcustomeractivityacrossallchannels/touchpoints•Optimizedeliveryofinsightstothetimewhentheirconsumptionwill be most valuable

Technology•Deploysolutionstomonitorsocialmediaandidentifyemergingnetworks relevant to customer base•Uselocation-basedtechnologytoimproveunderstandingofin-store customer purchase journeys•Establishconsistentinternaldatamodel•InvestinBigDatatoolstohandleacceleratinggrowthofdatasetsizes•Deploycontextual,visual,mobile-friendlyvehiclesfordeliveringanalytics insights to front-line associates

Process•EstablishC-levelorseniorVP-levelexecutiveinchargeofanalytics•Movefromadepartment-basedorIT-centricanalyticsorganizationto a shared services model•Investinspecialistskillsetssuchasstatisticians,actuaries,datascientists and economists•Decreasetimefromdataacquisitiontoanalysisandinsightpro-duction•Incorporateavailablethird-partyorgovernmentdatatoenrichanalytics insights•UseA/Btestingtoconstantlymeasureimpactofactions•Comparetestresultsamongvariouscustomersegmentstoassessvariances

Requirements every major business initiative requires a detailed assessment that examines the proj-ect’s impact on internal processes, technologies, personnel, strategic alignment and costs. one goal of the assessment is to identify granular and high-level requirements that are essential elements in the project’s game plan. managing and addressing these requirements is critical to success.


40%

issue in retail, with different departments unable to speak the same language in terms of data, resulting in different descriptions of products or definitions of what its price actually is.

in some ways, the spread of new analytics technol-ogy has made the problem worse. “even recently, marketing’s adoption of Web and social media analyt-ics was initially driven more from a channel perfor-mance measurement and improvement perspective, rather than a need to understand consumer behavior and preferences better,” writes eKn research Director gaurav pant in the future of retail Analytics report.

retailers are moving aggressively to make greater use of social media data: eKn projects that retailer in-tegration of social data will go from 30% in 2013 to ap-proximately 90% in 2015; mobile data will see a similar growth in integration, albeit from an even smaller base.

the demands of omnichannel retail and the custom-er-centric nature of these kinds of social media data are creating new opportunities for retailers to gain in-sights that will benefit multiple departments and func-tions. “new insight can be drawn by mashing up data across domains,” writes greg girard in the iDc retail insights september 2012 report titled Big Data and An-alytics in retail: Unlocking hidden opportunities. “for example, contextual analysis of contact center call logs, tweets, ratings and reviews, lot tracking of ship-ment records, product design information, and loyalty data taken together could spot product defects from customer feedback, identify root causes from product design information, identify stores holding any defec-tive merchandise, and find loyal customers who pur-chased the item.”

Address organizational structure of Analytics resourcesthis type of productive mixing of data and ana-

lytics insights doesn’t occur without organizational

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retailer integration of social data will go from 30% in 2013 to approximately 90% in 2015

Source: EKN 2013 State of the Industry Research: The Future

of Retail Analytics

30%

50%

60%

70%

80%

90%

2013

2015


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change within the retail enterprise. current depart-mental structures tend to discourage sharing: 71% of retailers surveyed by eKn have individual departments responsible for their own analytics, and just over half (53%) primarily rely on it for analytics support. how-ever, only 5% of respondents say these structures are their desired/optimal state.

in contrast, 18% of respondents use a shared ser-vices format for analytics, but 58% say this is the way their organization should be structured.

Align Delivery of Analytics insights to User needsAnother of the enormous challenges in this area

involves getting the insights that analytics solutions produce to those who can make the best use of them. the classic example would be a predictive analytics solution identifying the customer a sales associate is currently helping as someone who is highly likely to purchase a second pair of shoes if she is offered even a small percentage discount. getting that crucial piece of “intel” to the salesperson in time to affect the out-come of their interaction, however, requires high lev-els of coordination among various systems, databases and communication devices.

in these and other time-sensitive situations, deliver-ing the equivalent of a spreadsheet with the relevant data buried somewhere in it will make the entire ef-fort an exercise in frustration. that’s why eKn predicts that retailers “will invest in contextual, visual and mo-bile-friendly delivery of insights to combat the biggest challenge that prevents them from leveraging analyt-ics strategically – delivery of insights to the right re-source at the right time.”

even as they explore the new frontiers of analytics, retailers can’t afford to lose sight of the basics. “half

In time-sensitive situations, delivering

the equivalent of a spreadsheet

with the relevant data buried somewhere

in it makes the entire effort an exercise

in frustration.

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the time, retailers are better off when they know ex-actly which problems they’re trying to solve, because it helps them focus on the right questions to ask,” ac-cording to nikki Baird and steve rowen, writing in the october 2012 rsr research report titled retail Busi-ness intelligence: A Work in progress. “retailers need to stay focused on the ‘small bites’ – on the common problems that need to be solved, and on how they can pre-analyze to get end users, particularly front line end users, as close to the action needed as possible.”

Apply Analytics insights Across channelsthe value of analytics-produced insights can be

multiplied when they are applied across a retailer’s sales channels. retailers capable of connecting shop-pers’ online activities, i.e. when they are researching a potential purchase, to their store visit, are much more likely to be able to complete the sale when they arm store associates with this knowledge.

in some cases the insights move from the store to the digital channel. Brooks Brothers sought to repli-cate online the complex process of building a dress shirt, which generally takes place in the store with the assistance of a sales associate. multiple parameters include picking one of four fit types as well as differ-ent color and fabric choices. the retailer used testing to communicate to the customer “at the right time and using the right message,” according to Brooks Broth-ers’ director of analytics cindy Lincks.

she added that the retailer’s testing involved look-ing at the responses of new versus existing custom-ers, and noting that “we didn’t expect them to re-spond the way they did. A lot of what we do involves slicing and dicing customer segments, because it’s not just about what appeals to the masses but what appeals to the individual customer’s needs as they go through your site.”

BestPractice When Brooks Brothers sought to replicate the detailed in-store process of building a dress shirt online, it used customer segmentation data comparing new and existing customers to test and refine processes and communications


Conclusionforward-thinking retailers realize how vitally impor-tant analytics are to their ability to differentiate them-selves. for example, it’s always been critical for retail-ers to identify who their most valuable customers are, but predictive analytics gives retailers insights into what products, promotions and engagement vehicles are most likely to keep these valuable customers loyal in the long term. Analytics also offers retailers guid-ance about increasing share of wallet, building mid-value shoppers into high-value ones.

sophisticated analytics can also help create an or-ganization capable of handling the challenges of om-nichannel retailing. for example, as fulfillment be-comes more complex and spreads to the store level, cost analysis can inform business rules that tell the re-

tailer the optimal location from which to fulfill an order so as to maximize both efficiency and

profitability. Analytics can also help retail-ers determine optimal pricing based on the value customers place on the prod-uct they’re buying at any given time.

Analytics solutions that can tap into newer data sources, such as the wealth of

social media data and location-based infor-mation from tracking of mobile devices, give

retailers powerful tools to identify new trends – and also to test which of these trends will have real staying power versus those that flash and then fade. in addition, retailers can use analytics solutions to answer questions affecting their long-term success, such as which new geographic markets they should expand into because they will be the best “fit” for the retailer’s brand.

By providing insights into all aspects of an increas-ingly complex purchasing process, analytics solutions let retailers stay in step with – and even move ahead of–today’sempoweredconsumer.•

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Charting the Path that Links Technology and Business GoalsR O A D M A P

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Business Analytics Mileposts

• Advance from Business Intelligence to Business Analytics• Gain insight by mixing data from multiple domains• Address organizational structure of analytics resources• Align delivery of analytics insights to user needs• Apply analytics insights across channels

Roadmap KPIs Infographic

90%of the data in the world today has been created in the last two years

Source: Citi Research, January 2013 Source: RIS/Gartner 2013 Tech Trends Study

onLY

of retailers have up-to-date predictive analytics solutions in place.

retailer integration of social data will rise from 30% in 2013 to 90% by 2015

90%

“in time-sensitive situations, deliver-ing the equivalent of a spreadsheet with the relevant data buried some-where in it makes the entire effort an exercise in frustration.”

Source: EKN 2013 Future of Retail Analytics

retailers use a shared services format for analytics, but nearly

60% believe this is the way their organization should be structured

>1 in 5

Source: EKN 2013 Future of Retail Analytics