Making Online Shopping Smarter with Advanced AnalyticsMerging offline and online data can help retailers to customize targeting, resulting in incremental increases in e-commerce revenues.

Executive SummaryOnline shopping has emerged as one of the most popular Internet activities, providing a variety of products for consumers and a multiplicity of sales challenges for e-commerce players. Research suggests that online sales has grown 16.1% year-over-year from March 2010 through March 2011. 1

Both brick and mortar and pure play online retailers are competing for the attention of the online consumer. A real opportunity for companies lies in applying advanced digital analytic techniques that integrate offline and online data sets to optimize on-site store inven-tories. One example from retail is how Best Buy is leveraging data to become more customer-centric. When Best Buy determined that 7% of its customers were responsible for 43% of its sales, the company segmented its customers into several archetypes and redesigned stores to address the buying habits of these particular customer segments, thereby enhancing the in-store experience and increasing same-store sales by 8.4%.2

While optimization and other analytical techniques like A/B/multivariate testing, visitor engagement, behavioral targeting and audience segmentation can point toward a high likelihood

of a prospect’s willingness to buy, transactional data points such as offline sales are key indicators of actual sales. This paper provides insights on how merging offline and online data can support customized targeting, resulting in incremental increases in e-commerce revenues.

Constraints and OpportunitiesIn most companies, unfortunately, online and offline sales are processed in technical silos. Offline shopping data is very important because many people still want to touch and see products first-hand before they buy. Conventional wisdom suggests that about 70% of all consumers will make an offline purchase as a result of online marketing. Thus, offline data is extremely beneficial, and mining it along with clickstream data provides a valuable resource for improving customer satisfaction, product development, sales forecasting, merchandising and visibility into the profit margin for goods and services.

The ability to identify and reach consumers at the most crucial point in their buying decision-making process, as reinforced by Google’s “Zero Moment of Truth”3 study, demonstrated that in-store or online transactions are heavily influenced by insights gained in the moment before a purchase decision is made. Thus, steering consumers

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cognizant 20-20 insights | june 2012

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toward a particular product or service and then capturing them at the point of sale is a very powerful solution.

This is why Google’s search marketing is so appealing to marketers; consumers’ desires are instantly tele-graphed by their actions.

Creating a holistic customer picture is not a new idea but very few companies have achieved it. Constraints have included database and hardware technology that could not effectively support the solution; data management issues; Web

analytic systems that are not focused on the avail-ability of the underlying data; not getting the right people to focus on the data; the division of online marketing and traditional marketing skill sets; etc.

Leading the ChargeFor e-commerce companies, the challenge is that customers are often relatively far into the purchase funnel when they arrive at a particular site. An e-commerce player that can capture the consumer’s attention and activity further up the funnel and combine it with advanced analytics techniques can begin to assume the mantle of an analytics leader.

Joining geographic, creative, time of day (e.g., morning, afternoon, evening, etc.) data and mapping these attributes against time and location-based sales data can highlight hidden interactions between online and offline sales activity. This means e-commerce players can develop more effective multichannel strategies. The end result is that real additional sales can be increased. Of course, e-commerce companies have an option to use advanced Web analytics to create a specific segment for visitors who arrived online via offline campaigns. Web analytic4 tools provide similar solutions, but they are not by any means a complete set of offerings nor do they create a single view of your customer. Organiza-tions need to pull all the data attributes, offline and online, into a single database, which would be further refined by advanced analytics techniques, and use the unified data for precision targeting as depicted in Figure 1.

Figure 2 illustrates that by combining Website data (i.e., clickstream information, etc.) with loyalty card insights, sales data and third-party information, e-commerce players can gain critical understanding into customer behavior. To reach the forefront of the data-driven digital revolution, e-commerce players need to acquire key tools, analytic prowess and necessary skills. Not only do e-commerce players need to invest in the right advanced analytics tools, but they must also gain access to mathematicians and statisticians to create models and interpret findings.

Applying Advanced Analytics

Figure 1

Data

Advanced Analytics Output

ETL ProfilingAssociation

Rules

LogisticRegression

VariablesCreation

WeightedAverage

Segmentation

Classification Time SeriesLinear

Regression

StatisticalControl Process

StatisticalControl Process

An e-commerce player that can capture the

consumer’s attention and activity further

up the funnel and combine it with

advanced analytics techniques can begin

to assume the mantle of an analytics leader.

cognizant 20-20 insights 3

Organizations need to apply more complex data calculations to generate a more accurate picture of customer behavior and transactional activity. Measuring offline marketing campaigns statistics is not as easy as many people think. There are numerous different tools that can help organiza-tions improve data accuracy; however, many are third-party solutions that are not integrated into a Web analytics solution.

Advanced Analytics Framework in Action: E-Commerce Gold While e-commerce sales will continue to grow over the next four years, eMarketer5 estimates that the rate of growth will decline steadily. This makes it even more vital for e-commerce players to be strategic about how they use clickstream or Web log data to reach consumers.

Blending Multiple Sources of Sales Data for Optimal Positioning

Figure 2

ONLINE

OFFLINE

1 2 3

Quality

Model Score

Gift Registry

Demog

Payment Account

Address

Catalog

Item

Location

Promotion

Sales

Web Visit

FeedbackPrivacy

Completes the Jigsaw Puzzle

Assessing Impact of Online Over Offline

Understand Customer Buying Choices

Predict the “Right Time to Sell”

We Reach — Right Customer at Right Time with Right Products

Online + Offline Customer Behavior

Enable Complete View of Purchase Cycle

Integrate Promotion, Web Visit, Sales Transaction, Customer Data

Leverage Digital Analytics to Gain Customer Insights

Market Basket Analytics

Segmentation

ROIProbability to Drop Off

Probability to Buy

Predictive Modeling

Multi Channel

Optimization

Factor Analysis

Store Locator Analytics

Social Media

The Art and Science of Connecting Offline and Online Data

Figure 3

Campaign Data

Web Visit Data

Demographic Data

Sales Transaction Data

Data PreparationProfilingSegmentation

Rank OrderingLift Chart*

High Score

Medium Score

Low Score

Model

Data

Business Input Output

Finalize models by taking inputs from business teams

Optimize Targeting

Predictive Modeling

0.0

0.2

0.4

0.6

0.8

1.0

Raw Analysis Data

cognizant 20-20 insights 4

Figure 3 illustrates a typical process/methodology followed by a digital analytics center of excellence (CoE) to merge offline and online data in order to build predictive models that optimize customer targeting.

We have applied this methodology at several of our clients resulting in the scenarios presented in Figures 4 and 5.

In the near future, organizations will likely deploy more sophisticated and integrated Web/digital analytics tools to more easily combine online campaign data with offline insights. This will include:

• Getting the Web log or clickstream data from Web analytics tools.

• Merging it with point-of-sale data.

Benefits of Merging Online and Offline Data: Scenario One

Figure 4

Brand A 16% 15% 8% 6%

Situation: Client has huge online and offline data which needed integration for actionable analysis. Solution : Profile customers based on their online purchasing/browsing behavior with offline transactions.

Online Data

Web Visit

Web Registration

Online Transactions

Email Campaign

Banner Ads

Offine Data

Sales Transaction

Demography

Store Location

Address

Visitor Profiling

Channel Preference

Purchasing Preference

Concept% Identified SessionsDec-10 Jan-11

All Brands 16% 14%

% of Identified visitorsDec-10 Jan-11

7.8% 6.3%

Design Preference Segment

% of Customers % of Sales

Visit freq. Seasonality % of

VisitorsVisits per

Visitor

% CustomerChannel Preference

eCom Retail Both

Purc

hase

Be

havi

or

Large Frequent Buyer 5.0% 2.0% 2.7%Large Buyer 11.7% 0.8% 0.6%Frequent Buyer 5.4% 3.5% 6.5%Medium Buyer 30.7% 2.0% 3.0%Small Frequent Buyer 0.5% 0.4% 0.8%Small Buyer 21.0% 1.5% 1.9%

Brand B 22% 19%Brand C 17% 13%

11% 9%8% 6%

Modern 40.82% 43.75%Utilitarian 17.55% 11.10%Organic 10.76% 5.19%Classic 8.37% 11.04%

BuyerHigh Regular 15.98% 44

Seasonal 1.04% 26 Low Regular 0.10% 5

Seasonal 4.59% 4

Non Buyer

High Regular 1.71% 38 Seasonal 0.11% 26

Low Regular 1.45% 10 Seasonal 1.03% 8

1. Profiling helped client in recognizing the potential buyers and up sell seasonal buyers.

Visitor and Session

Recognization

2. Channel preference helped client in reaching out to customers with their preferred channel.

3. Taste of a buyer helped client in reaching them with targeted offers.

4. Visitor and session recognization was improved by 30% to 50%.

Benefits of Studying Online Customer Behavior: Scenario Two

Figure 5

Situation: : Unstructured huge clickstream data with not much knowledge of what to do with it.Solution : Web path analysis revealed browsersʼ intent and needs which helped to customize offerings.

Online Data

Web Visit

Web Registration

Online Transactions

Email Campaign

Banner Ads

Offine Data

Sales Transaction

Demography

Store Location

Address

Express Checkout Analysis

Online Purchasing After E-mail Click-through

Browser Store Distance Analysis

Onsite Search Effectivenesse

Web Browser

Log

Web Path

Web Page View

Web Registration

Item View

Onsite search

0%

20%

40%

60%

80%

100%

Retail Catalog eCommerce

48%61%

65%62%71% 70%

% o

f U

niq

ue

Cu

stom

ers

Transaction Channel

Purchased with web Visit

30 Days prior 90 Days prior

0

10

20

30

40

50

60

Category 1 Category 2

33 42

14

18

Th

ou

san

ds

CHECKOUT SIGN INEXPRESS CHECKOUT SIGN IN

LocationName

% Customers with in 50 Miles

% Order Value with in 50 Miles

Grove 96% 88%

Westchester 96% 82%

Whitman 95% 79%

Store Area Distance from browsers

30% people are using Express Checkout

Activities # of Visits% Activities after onsite

searchTotal Product Search 700,000Exit after Search 150,000 22.5%NULL Search 7,000 1.0%Product Search 260,000 39.3%Using Search Again 160,000 24.1%Top or Left Navigations 50,000 7.1%

Homepage 20,000 2.4%3. Browser behavior around a store helped client in providing customized offer on specific stores.

4. Onsite search effectiveness helped client in customizing product information for better search results.

1. Checkout analysis helped client in estimating the return on new development on products Web site.

2. Customer behavior of browsing before buying helped client in focusing more on information on their website about products.

cognizant 20-20 insights 5

Generating Collective Intelligence

Analytical ToolsOmniture, Webtrends, SAS, SQL,

Coremetrics, etc.

Group of ExpertsLeaders, Scientists

and Experience Facilitators

Validation and Application

Highly Specialized Information Silos

Collective IntelligenceNew Technologies, New Knowledge,

New PhilosophyInsight Builder Tool

Synt

hesi

sFiltering

Marketplace (Online + Offline)Sales, Web, Demography, Store Location, E-mail,

Banner Mobile, Social Media, Data, etc.

Figure 6

• Studying online and offline customer behavior.

• Refining that data with the use of advanced analytic techniques, set methodology and algorithms, applied by the right domain experts.

• Then, validating/refining the data for pinpoint targeting.

The end result: Enabling companies to reach the right customer with the right product offerings at the right time and place. With this in mind, we

have created a unique solution framework (see Figure 6).

ConclusionThe solution framework shown in Figure 6 can be customized to meet the unique requirements of each company. Because it is technology agnostic, it can seamlessly integrate with any ERP system, the Web, social media, DW/BI, CRM and POS systems. Also, it takes data in whatever form is given and then it works toward synthesizing each of the data sets in a format that can be further sliced and diced using our proprietary algorithms and models. It thereby brings offline and online data together to enable business users to make insightful decisions to move their businesses forward.

For example, an organization selling laptops or mobile devices that captures the correct 1% of its prospects can generate an additional 3% in increased revenues. It can do so by applying a tried and true process/methodology (as illus-trated in Figure 3), followed by a digital analytics center of excellence to merge offline and online data and build predictive models to optimize customer targeting.

Thus, identifying the most valuable customers using online and offline data allows companies to more accurately identify those who are transac-tion minded and can help boost their revenues. A data-driven optimized strategy is the key, and that is possible only with offline and online data integration.

Footnotes 1 http://www.ipaydna.biz/Online-sales-witness-16.1-percent-growth-year-over-year-n-43.htm2 http://www.fico.com/en/FIResourcesLibrary/Best_Buy_Success_2271CS_EN.pdf3 http://www.thinkwithgoogle.com/insights/library/studies/the-zero-moment-of-truth-macro-study/4 http://en.wikipedia.org/wiki/Web_analytics5 http://eMarketer

About the AuthorAshish Saxena is the Leader of the Digital Analytics CoE within Cognizant’s Enterprise Analytics Practice. He has over 15 years of experience in e-commerce, multichannel retail, digital and advanced analytics space. He also holds several patents in the e-commerce/mobile space. Ashish can be reached at Ashish.Saxena3@cognizant.com.

About Cognizant

Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out-sourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50 delivery centers worldwide and approximately 140,500 employees as of March 31, 2012, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.

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About Cognizant’s Enterprise Analytics PracticeCognizant’s Enterprise Analytics Practice (EAP) combines business consulting, in-depth domain expertise, predictive analytics and technology services to help clients gain actionable and measurable insights and make smarter decisions that future-proof their businesses. The Practice offers compre-hensive solutions and services in the areas of sales operations and management, product management and market research. EAP’s expertise spans sales force and marketing effectiveness, incentives management, forecasting, segmentation, multichannel marketing and promotion, alignment, managed markets and digital analytics. With its highly experienced group of consultants, statisticians and industry specialists, EAP prepares companies for the future of analytics through its innovative “Plan, Build and Operate” model and a mature “Global Partnership” model. The result: solutions that are delivered in a flexible, responsive and cost-effective manner www.cognizant.com/enterpriseanalytics.