Analytical Customer Relationship Management

Analytical Customer Relationship Management Jaideep Srivastava University of Minnesota srivasta@cs.umn.edu http://www.cs.umn.edu/faculty/srivasta.html 11/2/2003 1 Presenter Background • Oct 1988 – Sept 1999 – • Oct 1999 – April 2000 – • Director of Data Analytics, Yodlee – e-finance experience May 2001 – August 2001 – • Chief Data Mining Architect, Amazon.com – e-commerce experience May 2000 – April 2001 – • Professor, University of Minnesota – academic experience Chief Technology Officer, Chingari – entrepreneurship experience September 2001 – – 11/2/2003 Professor, University of Minnesota Technical advisor to two Venture Capital firms in the Silicon Valley 2 Outline • Technology trends • Shift in marketing approach • Amazon.com case study: personalized • • • • consumer marketing Yodlee case study: web business intelligence Analytics behind e-marketing Privacy issues Concluding remarks 11/2/2003 3 Technology Trends • Internet growth – Faster than any other infrastructure • Data collection – Rapid drop in storage costs – Dramatic improvement in resolution and rate of data collection ‘probes’ • Data analytics – Increasing deployment of warehouses – Major leap forward in data mining technologies and tools Becoming possible to really understand what your customers want – even at the individual level!! 11/2/2003 4 Infrastructure Adoption in the US 120 Millions of users 60 Radio TV Cable Internet 0 1922 11/2/2003 1950 1980 1995 2000 5 Marketing – 75 years ago • Production – a la Adam Smith • You can have any color as long as it’s black – Ford Motor Co. 11/2/2003 6 Marketing - today Add the spice of flexibility, courtesy of robotics, computers … 5 11/2/2003 7 New approach to marketing TO: Finding products that are right for each customer TURN the process through 90 degrees FROM: Finding customers that are right for each product To achieve this we need to align around Products: 1 2 3 4 5 ….. 11/2/2003 •Organization and culture •Business processes and skill •Measurement and incentives •Information management •Technology 8 “Mass Customization” – • Mass production – Cheap to produce – Efficient to produce – Uniform features/quality – ‘one size fits all’ approach – Optimize production cost • B. Joseph Pine Customization – Expensive to produce – Inefficient to produce – Customized features – ‘tailor made’ approach – Optimize customer satisfaction • Mass customization Cheap & efficient to produce – Customized features – ‘tailor made’ approach – Optimize production cost & customer satisfaction – 11/2/2003 9 We have indeed come a long way … 11/2/2003 10 CRM Functions - 1 • Customer care & support functionality Incident assignment/escalation/tracking/reporting – Problem management/resolution – Order management/promise fulfillment – Warranty/contract management – • Marketing functionality – – – – – 11/2/2003 Campaign management Opportunity management Web-based encyclopedia, configurator Market segmentation Lead generation/enhancement/tracking 11 CRM Functions - 2 • Executive information functionality – Extensive & easy-to-use reporting • ERP integration functionality Legacy systems – Web data sources – 3rd party information – data overlays – • Excellent data synchronization functionality Mobile synchronization with multiple field devices – Enterprise synchronization with multiple database/application servers – 11/2/2003 12 CRM Functions - 3 • Sales functionality Contact management profiles and history – Account management including activities – Order entry – Proposal generation – • Sales management functionality Pipeline analysis, e.g. forecasting – Sales cycle analysis – Territory alignment – Roll-up and drill-down reporting – 11/2/2003 13 CRM Functions - 4 • Telemarketing/telesales functionality – – – – • Time management functionality – – • Single user and group calendar/scheduling E-mail Field service support functionality – – 11/2/2003 Call list assembly Auto dialing Scripting Order taking Work orders, dispatching Real time information transfer to field personnel via mobile technologies 14 Traditional Growth of CRM Functions in an Organization THE PRESENT MULTIPLE CHANNELS & DATA STORES / IMPERSONAL SERVICE 3rd Party Resellers Kiosk ATM Branch Outbound Call Centre Data Impact! • IMPERSONAL • LOW QUALITY • UNINFORMED • INCONSISTENT Data Data Data WEB Fax Email WAP Inbound Call Centre l In Confidence 11/2/2003 15 Vision for Customer Driven CRM THE NEAR FUTURE MULTIPLE CHANNELS & DATA STORES / PERSONALISED SERVICE Impact! 11/2/2003 • PERSONALISED • HIGH QUALITY • INFORMED • CONSISTENT DATA 16 Where Does CRM Fit? C R M S u p p or t S ys t e m s C R M S ys te m D a t a b a s D e a s t a M in in g & D a t a W ar e h o u si n g Customer Data Database Companies Products & Services For Example Oracle Analysis Data mining Companies For Example NCR Segmentation MIS Customer Profile Market Information Organisation Strategy Treatment Strategy Customer Care Product Maintenance Business Acquisition Customer Interaction Channels TRUE CRM SPACE Utilising CRM Support systems Customers 11/2/2003 17 CRM Success Factors • • • • • • • • • • 11/2/2003 Determine functions to automate Automate what needs automating Gain top management support and commitment Employ technology smartly Secure user ownership Prototype the system Train users Motivate personnel Administrate the system Keep management committed 18 Analytical CRM 11/2/2003 19 Analytical CRM - Outline • Definition • The Analytical CRM loop • Customer segmentation & analysis • Customer targeting • Customer loyalty & its impact • Customer retention 11/2/2003 20 Analytical CRM Definition The CRM Equation: Customer Relationship Management = Customer Understanding + Relationship Management Customer Understanding: Analysis of customer data to gain deep understanding down to the level of individual customer Relationship Management: Interaction with the customer through various channels for various purposes Analytical CRM: Use customer understanding to perform effective relationship management 11/2/2003 21 CRM Analytics Loop Hypothesis generation Results Analysis Action 11/2/2003 22 Amazon.com’s Case Study: Personalized Consumer Marketing 11/2/2003 23 The continuing relationship … Amazon.com “Loyalty” model Need Creation anticipate/stimulate Information search provide /assist Evaluate alternatives assist / negate Purchase transaction optimise /reward Post purchase experience 11/2/2003 add value 24 Need Creation (attract to website) Need Creation 11/2/2003 anticipate/stimulate 25 Further Need Creation (upon reaching website) 11/2/2003 26 Information Search Information search 11/2/2003 provide /assist 27 Evaluation of Alternatives Evaluate alternatives 11/2/2003 assist / negate 28 Purchase Optimisation/Reward Purchase transaction optimise /reward •1-click purchase •‘slippery check out counter’ vs. ‘sticky aisles’ 11/2/2003 29 Post-purchase experience Post purchase experience 11/2/2003 add value 30 Account Management 11/2/2003 31 Why is loyalty important • Amazon’s ‘customer lifetime value’ model (for book buyers – – – – Average $50 for first time purchase Average $40 per visit thereafter Average of one visit per 2 months Assume customer will be active for 10 years – not validated yet ☺ • ‘4 buys and you are hooked’ empirical law • Use Alexa data to bring back ‘prodigal sons’ (and daughters) 11/2/2003 32 Build more loyalty faster “Loyalty” LTV Time 11/2/2003 33 The ‘Virtuous Cycle’ Purchase response Buying decision/process Customer knowledge 11/2/2003 34 Internet Marketing Insight – Jeff Bezos • Role of Advertisement – get customer to the store – Customer experience – get customer to buy – • Brick & mortar stores Getting customer to store is the hard part – Shopping cart abandonment is not common, since the overhead of going to another store is very high – especially in Minnesota winters! – • Marketing expenses – 80% for advertisement; 20% for customer experience The 80-20 rule is reversed for on-line stores – Jeff Bezos 11/2/2003 35 Remarks on Amazon.com • A very innovative company – the poster child for e-commerce • Is pushing the envelope in personalization • Customers love it • Will it make money – we’re all waiting to see A company of the future, with a product of the past, in a market of the present 11/2/2003 36 Yodlee.com Case Study: Web Business Intelligence 11/2/2003 37 Current Situation: Consumer Confusion “It takes me two hours to get to all my accounts” “I can’t look at my assets across accounts” “I can’t remember all my user IDs and passwords” “I want the web to work for me, not the other way around” “This is overwhelming……I need some help” 11/2/2003 “Make it easier for me!” 38 Solution – Personal Information Aggregation 11/2/2003 39 Aggregation Service Model Communication Site (content partner) Finance Site Travel Site Capabilities Content Acquisition Aggregation, Analysis, Personalization Aggregation Service Provider AOL Connected User 11/2/2003 AOLfinance MyCiti Citibank Mobile User Applications Presentation & Interaction 40 Business Intelligence Benefits to Corporation • ‘Tip-of-the-iceberg’ analysis for a brokerage house • Lifestyle preference analysis of banking customers for a survey • ‘True-wallet-share’ analysis for a credit card organization • Dynamic targeting for banner advertisements, e-mail campaigns, etc. 11/2/2003 41 ‘Tip-of-the-Iceberg’ Analysis for a Brokerage House 11/2/2003 Asset Based Tiers Number of Users < $20K 7579 $20K - $100K 2539 $100K - $500K 1994 $500K - $1M 525 $1M - $5M 547 $5M - $25M 106 > $25M 9 • This brokerage house treated customers with net worth > $1M as ‘high net worth’ (HNW) customers with specialized services • Almost none of the customers in the green region had > $1M with this brokerage 42 Household Lifestyle Preference Analysis for a Survey Financial Preferences - 53% have at least one online banking account - 51% have an online credit card account -- higher than Yodlee users as a whole - 31% also have an E*Trade account, and 11% also have a Schwab account - Have a preference for FirstUSA over Citibank, the opposite preference for users as a whole Lifestyle Preferences 25% make travel reservations online -fewer than users as a whole - Expedia is more popular as an online travel site than Travelocity - 49% have a frequent flier account -higher than users as a whole -The favorite frequent flier programs are United, Delta, American, in that order - Half as many of co-brand users shop on Ebay than users as a whole - The most popular credit card is American Express 11/2/2003 43 ‘True-Wallet-Share’ Analysis for a Credit Card Organization Range < $100 Total Users 462 Discover 4.13 American Express -467.40 (152) Mastercard 0 Visa -29.76 (87) Other -60.29 (272) Average Total -190.74 $100 - $200 232 -12.61 (73)(39) 120.17 (66) 0 89.95 167.10 (156) 149.44 $200 - $500 $500 - $1000 $1000 $2000 $2000 $5000 $5000 $10000 $10000+ 643 968 1386 36.97 (107) 75.57 (182) 174.55 (292) 253.77 (207) 571.09 (378) 988.97 (540) 0 0 837.25 263.27 (432) (1) 957.69 1732 620.80 (354) 1696 1332.48 (452) 2156.30 (1099) 4091.64 (814) 10111.75 (1010) 272.42 (421) 623.36 (593) 1078.01 (866) 2358.22 (1579) 4966.61 (1200) 14649.52 (1341) 342.99 893.47 1471.38 2422 (40)(135) 218.93 597.83 (217) 1018.50 (323) 2087.75 (601) 3976.93 (483) 8934.39 (642) (1) 3648.40 (3) 1921.16 3297.58 7100.20 22329.56 (9) Analysis of credit card balance habits of user base • There are1386 people, each of which carries a total balance between $1000 and $2000 on all credit cards that (s)he owns • 292 of these 1386 people own discover cards, and carry an average balance of $174.55 • 540 of these 1386 people own AmEx cards, with an average balance of $988.97 • 323 of these 1386 people carry one or more Visa, with an average Visa network balance of $1018.50 11/2/2003 44 Business Implications of True Wallet Share Analysis A credit card offeror knows exactly how much money customers holding its cards spend (every month) on its card vs. that on the competition’s cards • Offeror can target users falling within various segments for specific customer acquisition, retention, etc. purposes • Detailed profile and history information of these users can be used for precision targeting and customer messaging through various channels including ad serving, e-mail campaigns, promotions, etc. • If transaction level detail information of these users is analyzed, it can be determined exactly which credit cards are being used by aggregation users as a whole for what kind of lifestyle activity, e.g. travel, entertainment, shopping, groceries, etc; this can help partner decide which market segments to focus on • 11/2/2003 45 Business Implications (contd.) • • • The analysis above, if carried out at an individual user level detail, can be used to target individual customers with specific promotions, etc. Transaction level detail can be classified into charges to specific organizations, department stores, airlines, etc. This will identify the top organizations that aggregation users spend money at, either on the partner’s card or on a competing network. This would be useful in determining which organizations to partner with for customer retention, and acquisition, respectively All of these analyses if performed periodically, and tracked over time, can provide valuable insight into the evolving credit balance distribution and usage behavior at the user population or individual user level 11/2/2003 46 Targeted Ad Serving 11/2/2003 47 Targeted Ad Serving (contd.) 11/2/2003 48 The Analytics Behind e-CRM 11/2/2003 49 Web Logs – Record of consumer behavior looney.cs.umn.edu han - [09/Aug/1996:09:53:52 -0500] "GET mobasher/courses/cs5106/cs5106l1.html HTTP/1.0" 200 mega.cs.umn.edu njain - [09/Aug/1996:09:53:52 -0500] "GET / HTTP/1.0" 200 3291 mega.cs.umn.edu njain - [09/Aug/1996:09:53:53 -0500] "GET /images/backgnds/paper.gif HTTP/1.0" 200 3014 mega.cs.umn.edu njain - [09/Aug/1996:09:54:12 -0500] "GET /cgi-bin/Count.cgi?df=CS home.dat\&dd=C\&ft=1 HTTP mega.cs.umn.edu njain - [09/Aug/1996:09:54:18 -0500] "GET advisor HTTP/1.0" 302 mega.cs.umn.edu njain - [09/Aug/1996:09:54:19 -0500] "GET advisor/ HTTP/1.0" 200 487 looney.cs.umn.edu han - [09/Aug/1996:09:54:28 -0500] "GET mobasher/courses/cs5106/cs5106l2.html HTTP/1.0" 200 ... ... ... Access Log Format IP address userid time method url protocol status size mega.cs.umn.edu njain 09/Aug/1996:09:54:31 advisor/csci-faq.html Other Server Logs: referrer logs, agent logs Application server logs: business event logging 11/2/2003 50 Shopping Pipeline Analysis ‘sticky’ states Overall goal: •Maximize probability of reaching final state •Maximize expected sales from each visit Browse catalog Complete purchase Enter store Select items cross-sell promotions • • • • ‘slippery’ state, i.e. 1-click buy up-sell promotions Shopping pipeline modeled as state transition diagram Sensitivity analysis of state transition probabilities Promotion opportunities identified E-metrics and ROI used to measure effectiveness 11/2/2003 51 Original Amazon Model for Customer Segmentation 1500 dollars spent in 1000 past quarter H M 500 1 2 3 4 5 6 7 number of purchases in past quarter Light buyers Medium buyers Heavy buyers Customer M - medium Customer H - heavy Super heavy buyers 11/2/2003 52 Data Driven Customer Segmentation Model frequency monetary tenure recency • modeled customers in a 4-dim space • used PCA to determine relative weights of each dimension • Composite Score = w1*recency + w2*frequency + w3*monetary + w4*tenure 11/2/2003 53 Customer Score Interpretation Recency Cust M Cust H 11/2/2003 Frequency Monetary Tenure Composite Score … … … … … 10 days 4 times $480 3 months 80% … … … … … 30 days 2 times $900 10 months 72% … … … … … … … … … … • Cust M => frequent visitor but low spender => potential for acquiring higher wallet share => focus on improving relationship • Cust H => infrequent visitor but heavy spender => focus on sustaining relationship 54 Customer Segmentation & Segment Analysis 11/2/2003 55 Customer segmentation • • Purpose of segmentation is to identify groups of customers with similar needs and behavior patterns, so that they be offered more tightly focused – Products – Services – Communications Segments should be Identifiable – Quantifiable – Addressable – Of sufficient size to be worth addressing – • Two approaches to segmentation cluster common characteristics, and then map out behavior patterns – Separate out behavior patterns, then identify segment characteristics – 11/2/2003 56 Imagine if customer base were segmented as follows Potential business High Low Develop Observe & Incentivize Low Retain Care & Maintenance Actual business High Targeted communication to each segment 11/2/2003 57 Express profits as deciles, and ask questions 1200 1000 800 600 Profit 400 200 0 -200 -400 -600 -800 -1000 -1200 11/2/2003 Should the focus be on retaining wallet share from segments 8 – 10? Or, on gaining from segments 1 – 4? Who are these customers; what do they look like? Deciles Are these worth keeping? Can we service them with a lower cost channel? What can we do to make this segment profitable? Middle 60%, either side of break even. What can we do about these? 58 Dig deeper – larger product catalog may not necessarily mean more profit! 12 11 10 9 Products 8 Offered 7 6 5 4 3 2 1 0 Profit Deciles 11/2/2003 59 Privacy Issues 11/2/2003 60 let’s begin with some real examples … 11/2/2003 61 Problem: Shopping for spouse’s anniversary – too much clutter 11/2/2003 62 Solution: Focused and relevant advertisement 11/2/2003 63 Problem: Tired of mistreatment by financial institutions … • You have tons of money in your investment portfolio • But you are over-worked and slipped a couple of credit card payment deadlines – after all you are busy managing your investment portfolio ☺ • Credit card institution treats you like a deadbeat 11/2/2003 64 Solution • Why not let the credit card institution know what your investment portfolio balance is? Impress them ☺ • Perhaps even authorize credit card company to transfer funds from your investment account to cover the payment? Or maybe not ☺ 11/2/2003 65 So, what’s the catch… • Shopping example Allow the vendor to collect detailed information about you and build an accurate profile – Junk mail is only a nuisance for the receiver, but an expense for the sender! – the sender wants to avoid it more than the receiver!! – • Credit card example – Allow the credit card company and investment company to share your information • Multiple online accounts example Hand over your account names and passwords to aggregation service – Sounds scary – but over 1.5 million people have done this in about 18 months’ time!! – 11/2/2003 66 let’s now talk about privacy … • Merriam Webster definition – a: the quality or state of being apart from company or observation b : freedom from unauthorized intrusion • Justice Oliver Wendell Holmes – “the right to be left alone” • Operational definition – Collection and analysis of personal data beyond some limit 11/2/2003 67 Public Attitude Towards Privacy • A (self-professed) non scientific study carried out by a USA Today reporter • Asked 10 people the following two questions Are you concerned about privacy? 8 said YES – If I buy you a Big Mac, can I keep the wrapper (to get fingerprints)? 8 said YES – 11/2/2003 ACM E-Commerce 2001 paper [Spiekermann et al] • Most people willing to answer fairly personal questions to anthropomorphic web-bot, even though not relevant to the task at hand • Different privacy policies had no impact on behavior • Study carried out in Europe, where privacy consciousness is (presumably) higher • 68 Public Attitude (contd.) • Amazon.com (and practically every commercial site) uses cookies to identify and track visitors – • 97.6% of Amazon.com customers accepted cookies Airline frequent flier programs with cross promotions – – Over 1.5 million people have trusted the aggregation service (called Yodlee) with the names and passwords of their financial accounts in less than 18 months • Adoption rate has been over 3 times the most optimistic projections • We willingly agree to be tracked Get upset if the tracking fails! Medical data is (perhaps) an exception to this 11/2/2003 69 What people really want • Some people will not share any kind of private data at any cost – the ‘paranoids’ • Some people will share any data for returns – the ‘Jerry Springerites’ • The vast majority in the middle wants – – 11/2/2003 a reasonable level of comfort that private data about them will NOT be misused Tangible and compelling benefits in return for sharing their private data – Big Mac example, frequent flier programs 70 Remarks on Privacy Is it ‘much ado about nothing’? – If indeed data collection was outlawed, and thus personalization impossible, wouldn’t the public lose – faced with generic, undifferentiated products/services? – Given the public’s attitude about privacy (as shown in their actions), are privacy advocates barking up the wrong tree? – Is it just a matter of time or generational issue, e.g. adoption of credit cards • Where do we stand? – Current position - loss of your privacy may be beneficial for you – Emerging position (post September 11th ) - loss of your privacy will be beneficial for everyone – Critical emerging debate - is privacy a right or a privilege? • 11/2/2003 71 Concluding Remarks • Internet is a high bandwidth, low latency, negligible • • • • • • cost, interactive channel to the customer Very high adoption rates for this channel Processing speeds and storage capacities continuing to increase while costs continue to fall Data analytics technology has grown rapidly Consumer marketing is ready for a paradigm shift Innovative companies have moved ahead Privacy is an issue, but not much of a concern 11/2/2003 72 That’s all folks!! Questions? Comments? 11/2/2003 73