Making Online Shopping Smarter with Advanced Analytics

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Making Online Shopping Smarter with Advanced Analytics
• Cognizant 20-20 Insights

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

      Executive Summary                                     of a prospect’s willingness to buy, transactional
                                                            data points such as offline sales are key indicators
      Online shopping has emerged as one of the most
                                                            of actual sales. This paper provides insights on
      popular Internet activities, providing a variety of
                                                            how merging offline and online data can support
      products for consumers and a multiplicity of sales
                                                            customized targeting, resulting in incremental
      challenges for e-commerce players. Research
                                                            increases in e-commerce revenues.
      suggests that online sales has grown 16.1% year-
      over-year from March 2010 through March 2011. 1       Constraints and Opportunities
      Both brick and mortar and pure play online            In most companies, unfortunately, online and
      retailers are competing for the attention of          offline sales are processed in technical silos.
      the online consumer. A real opportunity for           Offline shopping data is very important because
      companies lies in applying advanced digital           many people still want to touch and see products
      analytic techniques that integrate offline and        first-hand before they buy. Conventional wisdom
      online data sets to optimize on-site store inven-     suggests that about 70% of all consumers will
      tories. One example from retail is how Best Buy       make an offline purchase as a result of online
      is leveraging data to become more customer-           marketing. Thus, offline data is extremely
      centric. When Best Buy determined that 7% of          beneficial, and mining it along with clickstream
      its customers were responsible for 43% of its         data provides a valuable resource for improving
      sales, the company segmented its customers            customer satisfaction, product development,
      into several archetypes and redesigned stores         sales forecasting, merchandising and visibility
      to address the buying habits of these particular      into the profit margin for goods and services.
      customer segments, thereby enhancing the
                                                            The ability to identify and reach consumers at the
      in-store experience and increasing same-store
                                                            most crucial point in their buying decision-making
      sales by 8.4%.2
                                                            process, as reinforced by Google’s “Zero Moment
      While optimization and other analytical               of Truth”3 study, demonstrated that in-store or
      techniques like A/B/multivariate testing, visitor     online transactions are heavily influenced by
      engagement, behavioral targeting and audience         insights gained in the moment before a purchase
      segmentation can point toward a high likelihood       decision is made. Thus, steering consumers

      cognizant 20-20 insights | june 2012
Making Online Shopping Smarter with Advanced Analytics
Applying Advanced Analytics

                        Data

                                                                               Association
                                                ETL            Profiling         Rules

                                             Logistic          Variables         Statistical
                                            Regression         Creation        Control Process

                                            Weighted         Segmentation        Statistical
                                            Average                            Control Process

                                            Classification                        Linear
                                                             Time Series        Regression
                                                                                                       Advanced Analytics
                                                                                                       Output

           Figure 1

           toward a particular product or service and then                 Joining geographic, creative, time of day (e.g.,
           capturing them at the point of sale is a very                   morning, afternoon, evening, etc.) data and
           powerful solution.                                              mapping these attributes against time and
                                                                           location-based sales data can highlight hidden
                                  This is why Google’s search
An e-commerce player              marketing is so appealing
                                                                           interactions between online and offline sales
                                                                           activity. This means e-commerce players can
   that can capture the           to marketers; consumers’                 develop more effective multichannel strategies.
  consumer’s attention            desires are instantly tele-              The end result is that real additional sales can
                                  graphed by their actions.
    and activity further                                                   be increased. Of course, e-commerce companies
                                                                           have an option to use advanced Web analytics to
      up the funnel and           Creating a holistic customer
                                                                           create a specific segment for visitors who arrived
                                  picture is not a new idea but
        combine it with           very few companies have
                                                                           online via offline campaigns. Web analytic4 tools
    advanced analytics            achieved it. Constraints
                                                                           provide similar solutions, but they are not by any
                                                                           means a complete set of offerings nor do they
  techniques can begin            have included database
                                                                           create a single view of your customer. Organiza-
                                  and hardware technology
 to assume the mantle             that could not effectively
                                                                           tions need to pull all the data attributes, offline
 of an analytics leader.          support the solution; data
                                                                           and online, into a single database, which would be
                                                                           further refined by advanced analytics techniques,
                                  management issues; Web
                                                                           and use the unified data for precision targeting as
           analytic systems that are not focused on the avail-
                                                                           depicted in Figure 1.
           ability of the underlying data; not getting the right
           people to focus on the data; the division of online             Figure 2 illustrates that by combining Website
           marketing and traditional marketing skill sets; etc.            data (i.e., clickstream information, etc.) with
                                                                           loyalty card insights, sales data and third-party
           Leading the Charge                                              information, e-commerce players can gain critical
           For e-commerce companies, the challenge is                      understanding into customer behavior. To reach
           that customers are often relatively far into the                the forefront of the data-driven digital revolution,
           purchase funnel when they arrive at a particular                e-commerce players need to acquire key tools,
           site. An e-commerce player that can capture the                 analytic prowess and necessary skills. Not only
           consumer’s attention and activity further up the                do e-commerce players need to invest in the right
           funnel and combine it with advanced analytics                   advanced analytics tools, but they must also gain
           techniques can begin to assume the mantle of an                 access to mathematicians and statisticians to
           analytics leader.                                               create models and interpret findings.

                                   cognizant 20-20 insights                2
Making Online Shopping Smarter with Advanced Analytics
Blending Multiple Sources of Sales Data for Optimal Positioning

                1                                              2                                                       3
                                 Online + Offline                                                                          We Reach — Right Customer at Right
                                                                         Completes the Jigsaw Puzzle
                                Customer Behavior                                                                              Time with Right Products

                         Enable Complete View                  Integrate Promotion, Web Visit, Sales                            Leverage Digital Analytics to
                           of Purchase Cycle                        Transaction, Customer Data                                    Gain Customer Insights

                 ONLINE
                                                                                                                                                             Predict the
                                                                                                               Understand Customer                       “Right Time to Sell”
                                                                                 Privacy
                                                                            k                                    Buying Choices
                                                     Quality        Feedbac

                                                                            g
                                                                        Demo         Addre
                                                                                          ss                                                 Multi
                                                                                                                                            Channel               Predictive
                                                                                                               Segmentation
                                                            Score                                                                                                 Modeling
                                                      Model                                 g                                             Optimization
                                                                                      Catalo
                                                                  y
                                                            egistr       t Accoun
                                                                                 t
                                                      Gift R       Paymen
                         Social                                               tion                             Market Basket                  Factor              Probability
                         Media                                           Promo                 Item              Analytics                   Analysis               to Buy

                                                          Sales                                on
                                                                                         Locati
                                                                                                                Store Locator                                    Probability
                                                                                                                  Analytics                    ROI               to Drop Off
                                                        isit
                                                      bV
                                                    We

                                                                                                                                              Assessing Impact of
                    OFFLINE                                                                                                                   Online Over Offline

Figure 2

Organizations need to apply more complex data                                              Advanced Analytics Framework
calculations to generate a more accurate picture                                           in Action: E-Commerce Gold
of customer behavior and transactional activity.
                                                                                           While e-commerce sales will continue to grow
Measuring offline marketing campaigns statistics
                                                                                           over the next four years, eMarketer5 estimates
is not as easy as many people think. There are
                                                                                           that the rate of growth will decline steadily. This
numerous different tools that can help organiza-
                                                                                           makes it even more vital for e-commerce players
tions improve data accuracy; however, many are
                                                                                           to be strategic about how they use clickstream or
third-party solutions that are not integrated into
                                                                                           Web log data to reach consumers.
a Web analytics solution.

The Art and Science of Connecting Offline and Online Data

            Data Preparation
                                                                                               Segmentation                                  Profiling
                Campaign Data
                                                                                                                                    1.0

               Web Visit Data                                                                                                       0.8

                                                    Raw Analysis                                                                    0.6

             Demographic Data                          Data                                                                         0.4

                                                                                                                                    0.2

                                                                                                                                    0.0
            Sales Transaction Data

                                                                                                                                          Predictive
           Finalize models by taking                   Lift Chart*
                                                                                                              Rank Ordering               Modeling
           inputs from business teams
                                                                                                                                      Data
                                                                                               High Score                                                     Model

                                                                                               Medium Score

                                                                                                                                  Business
                                                                                               Low Score                          Input                      Output

                       Optimize
                       Targeting

Figure 3

                                cognizant 20-20 insights                                       3
Making Online Shopping Smarter with Advanced Analytics
Benefits of Merging Online and Offline Data: Scenario One

           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.

                                                                                             Visit                               % of                       Visits per
                                                                                                          Seasonality
                                                                                             freq.                             Visitors                      Visitor                           1. Profiling helped client
                                                                                             High           Regular              15.98%                                   44
                                                                                                           Seasonal               1.04%                                   26
                                                                                                                                                                                                 in recognizing the
                                                                                 Buyer
                                                                                             Low            Regular               0.10%                                    5                     potential buyers and
     Online Data
                               Visitor Profiling                                             High
                                                                                                           Seasonal
                                                                                                            Regular
                                                                                                                                  4.59%
                                                                                                                                  1.71%
                                                                                                                                                                           4
                                                                                                                                                                          38
                                                                                                                                                                                                 up sell seasonal buyers.
    Web Visit                                                                     Non                      Seasonal               0.11%                                   26
                                                                                 Buyer       Low            Regular               1.45%                                   10
    Web                                                                                                    Seasonal               1.03%                                    8

    Registration                                                                                                              Channel Preference                                               2. Channel preference
                                                                                            % Customer
                                                                                                                      eCom             Retail                              Both                  helped client in
    Online
                                                                                           Large Frequent Buyer        5.0%            2.0%                                2.7%
    Transactions                                                                                                                                                                                 reaching out to
                            Channel Preference                                             Large Buyer                11.7%            0.8%                                0.6%
                                                                                                                                                                                                 customers with their

                                                                                Purchase
                                                                                Behavior
                                                                                           Frequent Buyer              5.4%            3.5%                                6.5%
    Email                                                                                                                                                                                        preferred channel.
                                                                                           Medium Buyer               30.7%            2.0%                                3.0%
    Campaign
                                                                                           Small Frequent Buyer        0.5%            0.4%                                0.8%
    Banner Ads                                                                             Small Buyer                21.0%            1.5%                                1.9%

                                                                                      Design Preference                   % of                                                                 3. Taste of a buyer
                                                                                                                                                           % of Sales
     Offine Data                 Purchasing                                               Segment                      Customers
                                                                                                                                                                                                  helped client in
                                 Preference                                     Modern                                    40.82%                                        43.75%
                                                                                                                                                                                                  reaching them with
    Sales                                                                       Utilitarian                               17.55%                                        11.10%
    Transaction                                                                 Organic                                   10.76%                                         5.19%                    targeted offers.
                                                                                Classic                                    8.37%                                        11.04%
    Demography
                                                                                                   % Identified Sessions          % of Identified visitors
                                                                                                                                                                                               4. Visitor and session
    Store Location               Visitor and                                     Concept           Dec-10        Jan-11            Dec-10                                  Jan-11
                                                                                                                                                                                                 recognization was
                                   Session                                        Brand A                16%          15%                        8%                                 6%
                                                                                                                                                                                                 improved by 30%
    Address
                                Recognization                                     Brand B                22%          19%                  11%                                      9%
                                                                                                                                                                                                 to 50%.
                                                                                 Brand C                  17%           13%                  8%                                     6%
                                                                                All Brands                16%           14%                7.8%                                   6.3%

Figure 4

Figure 3 illustrates a typical process/methodology                                          In the near future, organizations will likely deploy
followed by a digital analytics center of excellence                                        more sophisticated and integrated Web/digital
(CoE) to merge offline and online data in order to                                          analytics tools to more easily combine online
build predictive models that optimize customer                                              campaign data with offline insights. This will
targeting.                                                                                  include:

We have applied this methodology at several of                                              •    Getting the Web log or clickstream data from
our clients resulting in the scenarios presented in                                              Web analytics tools.
Figures 4 and 5.                                                                            •    Merging it with point-of-sale data.

Benefits of Studying Online Customer Behavior: Scenario Two

           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.
                                                                                                                                                                                            Purchased with web Visit
                                                                                              30% people are using Express Checkout
                                                                                                                                                                        100%
                                                                                                                                                % of Unique Customers

                                                                                60
                                                                                                                         18                                                                                                70%
                                                                    Thousands

                                                                                                                                                                                                          71%

           Online              Express Checkout                                 50                                                                                      80%              62%
                                                                                                                                                                                                                     65%

           Web Data
                                                                                                                                                                                                    61%
                                                                                                   14
                                    Analysis
                                                                                                                                                                                  48%
                                                                                40                                                                                      60%

       Browser
       Web Visit                                                                30
                                                                                                                         42                                             40%

         Log
       Web
                                                                                20
                                                                                                   33
                                                                                                                                                                        20%
                                                                                10
        Registration                                                                                                                                                     0%
                                                                                 0
       Web Path                                                                                                                                                                    Retail           Catalog        eCommerce
        Online                 Online Purchasing
                                                                                             Category 1             Category 2
                                                                                                                                                                                               Transaction Channel
                                                                                           CHECKOUT SIGN IN
        Transactions             After E-mail                                              EXPRESS CHECKOUT SIGN IN                                                                  30 Days prior            90 Days prior
       Web Page
        Email                    Click-through
         View
        Campaign                                                                      1. Checkout analysis helped client                                                   2. Customer behavior of browsing
                                                                                         in estimating the return on new                                                      before buying helped client in
        Banner Ads                                                                       development on products Web site.                                                    focusing more on information
         Web                                                                                                                                                                  on their website about products.
                                 Browser Store
      Registration
         Offine Data               Distance                                          Store Area Distance from browsers                                                                                             % Activities
                                                                                                                                                                               Activities          # of Visits     after onsite
        Sales                       Analysis                                                                                                                                                                          search
                                                                                      Location      % Customers with % Order Value
        Transaction                                                                    Name            in 50 Miles  with in 50 Miles                                    Total Product Search        700,000
       Item View                                                                                                                                                        Exit after Search           150,000           22.5%
        Demography                                                                                                                                                      NULL Search                  7,000             1.0%
                                                                                       Grove               96%                   88%
                                                                                                                                                                        Product Search              260,000           39.3%
        Store
        OnsiteLocation           Onsite Search                                       Westchester           96%                   82%
                                                                                                                                                                        Using Search Again          160,000           24.1%
         search
        Address
                           e     Effectiveness                                        Whitman              95%                   79%                                    Top or Left
                                                                                                                                                                                                    50,000             7.1%
                                                                                                                                                                        Navigations
                                                                                                                                                                        Homepage                    20,000             2.4%
                                                                                     3. Browser behavior around a
                                                                                       store helped client in providing                                                    4. Onsite search effectiveness helped
                                                                                       customized offer on specific                                                          client in customizing product
                                                                                       stores.                                                                               information for better search results.

Figure 5

                               cognizant 20-20 insights                                      4
Generating Collective Intelligence                                   have created a unique solution framework (see
                                                                     Figure 6).

                    Marketplace (Online + Offline)
                                                                     Conclusion
             Sales, Web, Demography, Store Location, E-mail,
                  Banner Mobile, Social Media, Data, etc.
                                                                     The solution framework shown in Figure 6 can be
                                                                     customized to meet the unique requirements of
                            Analytical Tools
                                                                     each company. Because it is technology agnostic,
                      Omniture, Webtrends, SAS, SQL,                 it can seamlessly integrate with any ERP system,
                            Coremetrics, etc.

                                                                is
                                                                     the Web, social media, DW/BI, CRM and POS
        Filtering

                                                         Synthes
                            Group of Experts
                           Leaders, Scientists                       systems. Also, it takes data in whatever form is
                        and Experience Facilitators                  given and then it works toward synthesizing each
                             Validation and
                                                                     of the data sets in a format that can be further
                              Application                            sliced and diced using our proprietary algorithms
                                                                     and models. It thereby brings offline and online
                                                                     data together to enable business users to make
                            Highly Specialized
                             Information Silos
                                                                     insightful decisions to move their businesses
                                                                     forward.
                         Collective Intelligence
                    New Technologies, New Knowledge,                 For example, an organization selling laptops or
                             New Philosophy
                           Insight Builder Tool                      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
Figure 6
                                                                     tried and true process/methodology (as illus-
                                                                     trated in Figure 3), followed by a digital analytics
•   Studying online and offline customer behavior.                   center of excellence to merge offline and online
•   Refining that data with the use of advanced                      data and build predictive models to optimize
    analytic techniques, set methodology and                         customer targeting.
    algorithms, applied by the right domain
                                                                     Thus, identifying the most valuable customers
    experts.
                                                                     using online and offline data allows companies to
•   Then, validating/refining the data for pinpoint                  more accurately identify those who are transac-
    targeting.                                                       tion minded and can help boost their revenues.
                                                                     A data-driven optimized strategy is the key, and
The end result: Enabling companies to reach the
                                                                     that is possible only with offline and online data
right customer with the right product offerings
                                                                     integration.
at the right time and place. With this in mind, we

Footnotes
1
    http://www.ipaydna.biz/Online-sales-witness-16.1-percent-growth-year-over-year-n-43.htm
2
    http://www.fico.com/en/FIResourcesLibrary/Best_Buy_Success_2271CS_EN.pdf
3
    http://www.thinkwithgoogle.com/insights/library/studies/the-zero-moment-of-truth-macro-study/
4
    http://en.wikipedia.org/wiki/Web_analytics
5
    http://eMarketer

About the Author
Ashish 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.

                                  cognizant 20-20 insights           5
About Cognizant’s Enterprise Analytics Practice
Cognizant’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-
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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.

About Cognizant
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