E-tailing Click analysis for online shopping profiling. Bernd Drewes SAS EMEA

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1 E-tailing Click analysis for online shopping profiling Bernd Drewes SAS EMEA

2 Agenda E-tailing & Web Mining: Some Background 1-1 Marketing: Hopes and Reality Analyzing for Personalizations Methods/Approaches/Examples

3 E-tailing: A new retail channel Drivers technology globalization lean organization & cost structure Promise of 1-1 marketing/retailing fiction or reality? Needs new analysis: web mining

4 Analyzing Web Data Web/Purchase data preliminaries: clean, identify web sites for each click classical: Determine navigation paths Potential Tasks Identify cycles & dead ends find interests of users compute behavioral data: duration, number of clicks, frequency Purpose of Analysis: buyers vs. surfers??

5 Extract from log files: Preprocessed and ordered cust_id date page section session {0003E2F0-66C8-11d C04FF7927A} home home 1 {0003E2F0-66C8-11d C04FF7927A} home home 1 {0003E2F0-66C8-11d C04FF7927A} fun 1 {0003E2F0-66C8-11d C04FF7927A} links links_partners 1 {0003E2F0-66C8-11d C04FF7927A} home home 1 {0003E2F0-66C8-11d C04FF7927A} links links_partners 1 {0003E2F0-66C8-11d C04FF7927A} profile 1 {0003E2F0-66C8-11d C04FF7927A} links links_confedreen 1 {0003E2F0-66C8-11d C04FF7927A} home home 2 {0003E2F0-66C8-11d C04FF7927A} home home 3

6 Simple Web Mining: Traffic analysis How many hits, page visits, visits, visitors which browsers & OS are most common how many bytes is the server transferring which files and directories are most popular from what sites are visitors coming Helps the drive toward 1-1 marketing??

7 More complex web traffic analysis link log files of entries & sort by time; generates paths of visitors through site group by browser, company, location analysis of common sub-paths: sequence analysis (--> next slide) Helps the drive toward 1-1 marketing?

8 Web Paths: Sequence Analysis

9 So... are these the right questions? How do answers impact profitability? Contribute to customer understanding? Solution 1: Define Business Metrics

10 E-Commerce Metrics Motif Everyone is drowning in data, knows there is value, but cannot make it happen, due to lack of people, expertise, standards, technical issues Everyone s goal: customer conversion Customer life cycle: reach, acquisition, conversion, retention, loyal Can be simultaneous loyal to different products?

11 E-Commerce Metrics We need metrics Acquisition cost= (advertisement and promotional cost)/ (number of click-throughs) Conversion costs=(advertisement and promotional costs)/ (number of sales) Churn rate = (customers lost) / (total customer Base) definitions vary a great deal Stickiness= frequency * duration * total site reach What do we know now?

12 Customer Implications: E-Customer Centric? Online Stores less customer centric passed savings on to customers providing less service focused on web/performance questions Solution 2: Need customer characteristics & profiles

13 Web Mining Questions some questions to ask Characteristics of people who purchased what are the profiles of my customers Characteristics and profiles in terms of what navigation only?? what to do with this information? select other cases/customers or intervene?? Need personal/business information, for -->

14 Personalization Panacea Matching direct or inferred customer needs through product/service personalization: Requires the system to detect who you are and to adapt itself to your needs (vision, partly...) Recommender Systems: A variant use needs of people like you profit from their range of experiences

15 Conclusion (so far) Web site traffic (and statistics) only 1st step for customer conversion need progress toward 1-1 marketing personalization profiling based on meaningful customer information web mining has narrow view of customer behavior obtained by integration: -->

16 Information Integration Complement clickstream data with offline data demographic data archived information on past business interactions purchase history catalog of products and services

17 1-1 Marketing Implications Need to intervene at right time identify seasonal preferences act on customer inactivity improve success of marketing messages reduce defections use additional information, e.g. location: offering product & services at the right time/place

18 Profiling Information See trends emerge in individuals and groups long view of customers -- let trends emerge track strength/breadth of customer interests pinpoint customer behavior changes predict what customer will do next classical segmentation lumps customers provide action points for experts (systems)

19 Conclusion (insight) Examples of analysis need to go beyond classical web mining event-triggered marketing predictive CRM predictions implicate decisions on procurement, production, marketing, sales of products/services shows: E-tailing produces new level of quantity, detail, timeliness

20 Event-Triggered Marketing Different promotional campaigns that will automatically occur when a customer behaves in a certain way Rules for these events must be targeted, customer approved, based on history, measurable, valuable, relationship-based

21 Analysis Needs to produce insights about customers Needs to consider successes AND failures e.g. act on customer inactivity e.g. act on changes from predicted behavior Lots of methods available, for a start predictive modeling, associations/sequences, SVD, Link Analysis, Segmentation

22 Predictive Modeling Describe customer behavior in a model usually by segments decide marketing actions for each segment devise personalization strategy, interact for cross-sell, up-sell in real time

23 Some Sample Analyses Using: predictive modeling, SVD, Link Analysis, Segmentation, Associations, Sequences will be provided at SEUGI time Relatively easy to illustrate these methods More difficult: generate required insights a program, (regrettably) not an achieved feat

24 Conclusion (for good) By 2003 $0.37 return on every dollar spent to promote a web site (Gartner) need to build reliable customer base soon need to meet service expectations 2 out of 3 customers are abandoning the web site after placing items in a shopping cart not failure but learn about interests of customer apply brick&mortar knowledge

BUS 168 Chapter 6 - E-commerce Marketing Concepts: Social, Mobile, Local

BUS 168 Chapter 6 - E-commerce Marketing Concepts: Social, Mobile, Local Consumers Online: The Internet Audience and Consumer Behavior Around 84% of U.S. adults use the Internet in 2015 Intensity and scope of use both increasing Some demographic groups have much higher percentages