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Making Online Shopping Smarter with Advanced Analytics

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Making Online Shopping Smarter with Advanced Analytics

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Advanced digital analytics techniques that blend offline and online data enabe e-commerce players to best target consumers. Weblog data and clickstream are powerful web analytics tools, but they must ...

Advanced digital analytics techniques that blend offline and online data enabe e-commerce players to best target consumers. Weblog data and clickstream are powerful web analytics tools, but they must be merged with offline data analysis - such as loyalty cards, demographics, third-party information and sales data - to deliver maximum power for e-marketers.

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  • 1. • Cognizant 20-20 InsightsMaking Online Shopping Smarterwith Advanced AnalyticsMerging 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
  • 2. 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 searchAn 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
  • 3. 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 OfflineFigure 2Organizations need to apply more complex data Advanced Analytics Frameworkcalculations to generate a more accurate picture in Action: E-Commerce Goldof customer behavior and transactional activity. While e-commerce sales will continue to growMeasuring offline marketing campaigns statistics over the next four years, eMarketer5 estimatesis not as easy as many people think. There are that the rate of growth will decline steadily. Thisnumerous different tools that can help organiza- makes it even more vital for e-commerce playerstions improve data accuracy; however, many are to be strategic about how they use clickstream orthird-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 TargetingFigure 3 cognizant 20-20 insights 3
  • 4. 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 4Figure 3 illustrates a typical process/methodology In the near future, organizations will likely deployfollowed 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 onlinebuild predictive models that optimize customer campaign data with offline insights. This willtargeting. include:We have applied this methodology at several of • Getting the Web log or clickstream data fromour 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 browsers 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 Data Express Checkout 50 80% 62% 61% 65% Web Analysis 40 14 60% 48% 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 Location Onsite 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
  • 5. 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 aFigure 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, andThe end result: Enabling companies to reach the that is possible only with offline and online dataright customer with the right product offerings integration.at the right time and place. With this in mind, weFootnotes1 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://eMarketerAbout the AuthorAshish Saxena is the Leader of the Digital Analytics CoE within Cognizant’s Enterprise AnalyticsPractice. He has over 15 years of experience in e-commerce, multichannel retail, digital and advancedanalytics space. He also holds several patents in the e-commerce/mobile space. Ashish can be reached atAshish.Saxena3@cognizant.com. cognizant 20-20 insights 5
  • 6. About Cognizant’s Enterprise Analytics PracticeCognizant’s Enterprise Analytics Practice (EAP) combines business consulting, in-depth domainexpertise, predictive analytics and technology services to help clients gain actionable and measurableinsights 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 managementand market research. EAP’s expertise spans sales force and marketing effectiveness, incentivesmanagement, forecasting, segmentation, multichannel marketing and promotion, alignment, managedmarkets and digital analytics. With its highly experienced group of consultants, statisticians and industryspecialists, EAP prepares companies for the future of analytics through its innovative “Plan, Build andOperate” model and a mature “Global Partnership” model. The result: solutions that are delivered in aflexible, responsive and cost-effective manner www.cognizant.com/enterpriseanalytics.About CognizantCognizant (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 inTeaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industryand business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50delivery centers worldwide and approximately 140,500 employees as of March 31, 2012, Cognizant is a member of theNASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performingand fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant. World Headquarters European Headquarters India Operations Headquarters 500 Frank W. Burr Blvd. 1 Kingdom Street #5/535, Old Mahabalipuram Road Teaneck, NJ 07666 USA Paddington Central Okkiyam Pettai, Thoraipakkam Phone: +1 201 801 0233 London W2 6BD Chennai, 600 096 India Fax: +1 201 801 0243 Phone: +44 (0) 20 7297 7600 Phone: +91 (0) 44 4209 6000 Toll Free: +1 888 937 3277 Fax: +44 (0) 20 7121 0102 Fax: +91 (0) 44 4209 6060 Email: inquiry@cognizant.com Email: infouk@cognizant.com Email: inquiryindia@cognizant.com©­­ Copyright 2012, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by anymeans, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein issubject to change without notice. All other trademarks mentioned herein are the property of their respective owners.