Mining the Web for transforming customer data into customer value

Transcription

1 Mining the Web for transforming customer data into customer value Important announcements: - Weka data mining software : -Download software + tutorial -Start learning how to use it -alternatively, you may get an account to use Weka already installed on computers in lab in JS-012

2 What is new? New technology can have big impact on how business is done Analogy between electricity & Web With Electricity: Factories can stay open for longer hours Can build factories away from water power Develop new types of machinery With the Web: New channel + Data collection abilities Impact on 3 areas in business: e-commerce, e-media, e-markets

3 Impact of Web on Business 3 areas defined by how business makes its money: E-commerce: sell things that get delivered on truck/ retail E-media: provide content of interest to their audience (no delivery): sell audience to advertisers or by subscription E-markets: connect buyers and sellers (commission per transaction) Web niches E-commerce E-media E-markets Subscription based media Free media

4 Area No. 1: E-commerce Companies that sell things over the Web (retailers) to customers who pay directly for them, ex: Cisco systems, Dell, Amazon etc Customers can make orders anytime Understanding customer needs less inventory faster inventory turnover higher profits Prices can be changed immediately! Ex: changes their prices > 1,000 times per day!!! (based on scouring competition websites!)

5 Comparison w/ traditional/offline/brick & mortar retailing Similarities: Marketing, pricing, seasonality (main ideas & models are the same) Differences: e-commerce businesses can take advantage of collected data in realtime /no waiting

6 E-commerce businesses can use data to: Reconfigure site: personalize to user preferences at their return Recommend new products in response to recent purchases during check-out process! Remember customer s preferences from past visits & take them into account in future visits Coordinate offers based on both: customer preferences & real-time inventory control! These are almost impossible to accomplish in regular (offline) retail store! Benefits: to customer & business

7 Area No. 2: E-media (content providers) Free media: selling eyes to advertisers Or build customer loyalty + strengthen their presence on the web Ex: New York Times News Yahoo portal By subscription: Customers pay for content Ex: Wall street journal Consumer Reports

8 Free e-media Provide content to audience In return, they sell this audience to advertisers (earn money through advertising) So is it free? ex: portals Some search engines provide content in the form of a searchable index (ex: Google) 1. request Audience 3. info E-media provider 3. Ad 4. click 2. info traditional 5. $$ Advertisers

9 Differences between traditional & e-media Cost of distribution Information richness traditional paper/space on cable or radio channels no info e-media relatively low who is reading what content? Advertising no feedback Which reader sees what Ad? Who is responding to which Ad? can personalize Ads

10 Area No. 3: E-Markets Connect buyers and sellers: e-intermediaries Earn money from each transaction Ex: e-bay Related phenomenon: Companies that moved their supply chain to the Web fully automating: Purchases Pricing Service comparison among different vendors/suppliers Ex: GE

11 E-Markets: Emerging models B-2-B (Business to Business), e-services: some companies conduct all business on Web, ex: GM, GE Companies are moving into a trend where entire supply chain is synchronized, automated, and on the Web! Not an easy task!!!

12 Differences between traditional & e-intermediaries Supply chain management Larger scale Global ubiquitous Data mining opportunities/ advantage Automatic matching (between seller & buyer) DM on historical data insight on supply, demand, pricing mechanics suggest when, what, and in what quantities to make trade in order to optimize transactions

13 Focus on Customers Importance of Customer: comes from financial justification for Customer Relationship Management (CRM): Happy customers spend more money Happy customers stay around longer Cheapest way to sell is to have customers come to you (before shopping elsewhere) Loyalty This is why banks offer several different products that are typically needed (checking, loans, etc) Amazon wants to sell you cars, gardening tools, etc w/ books Broadening relationship with each customer precious principle: Know thy customer!

14 How Web affects CRM Despite importance of customer, after business transactions moved on the Web, all personal contact was lost! How can businesses claim to know their customers? The answer: Fake it! This is the role of Data Mining: Mine data to extract or discover useful information/knowledge (such as your customer s preferences, predict buying patterns, predict which items sell together?)

15 Role of Data Mining to insert intelligence back into CRM No personal contact, BUT plenty of stored historic transaction (& behavioral: a Web exclusive!!!) data In DM, data is of paramount importance Web plays a special role in this respect: Data collector Powerful new channel to reach customers Web s immediacy for business & customers: real-time environment interaction customer E-business Instant impact

16 Role of Marketing Before: No one knows who sees which Ad? Hence advertising separate from direct marketing Now: new data: who sees/clicks on what? this changes interaction Advertisement Direct marketing Who sees what? Who clicks on what? Advertisement Direct marketing Instant impact

17 Ideal Advertising Traditional Advertising: 1-way company customer Ideal Advertising : 2-way Banner advertising provides record of every (computer/browser) pair that sees the Ad, clicks on Ad, goes to Ad website New Data new opportunities for marketers company customer - who sees what? - How they responded?

18 Targeted Marketing Goal: Find list of people to be contacted w/ offers, promotions, etc, that will optimize the marketing campaign (ex: maximize response, etc) Relies on Predictive Modeling : Data mining that can learn from previous campaigns to answer questions such as: Which customers will churn? (leave) What is the next best offer to make for a customer? (Recommendation score for other products)

19 Beyond Targeted Marketing Predictive Modeling is only a small part of successful marketing More complicated goals: Understanding customers better What if more than 1 campaign? Which customer should get which offer? What if more than 1 message in same campaign? Which customer should get which message? Which message attracts the best customer? What if more than 1 customer touchpoint (different. Channels to communicate) in same campaign? What is the right message to send to each customer on any given channel? What is the right order for these messages?

20 Customer Value Customer value (CV) affects marketing by allowing the prioritization of customers Def: numeric quantity that describes the values (often in $) that a customer is worth to the company Typically an exercise in accounting, but important for data mining efforts CV provides one method of segmenting customers Goal: increase average CV for all customers (and not focus on only the most values ones trap) CV = data produced by accounting, but as a measure, supports other goals (ex: to measure effectiveness of a marketing campaign)

21 Real Time Considerations Two steps when using data mining in marketing: Step 1: Analyze data and discover patterns Step 2: Act on this knowledge On the Web, this can happen in real time, even during the same transaction! This is good! But still challenging! Typical challenges: Scalability (need to keep up with real time demands Ability to automatically validate the DM results (quality assessment in the absence of human DM user) Some offline DM is still crucial at least to allow humans in the analysis loop (interact w/ DM, understand patterns, etc)

22 Experimental Design for Marketing When studying effect of a marketing campaign, want to distinguish between different types of customers (positive, negative, neutral) Experimental Design: Form different customer groups: Targeted group Control group Then, compare New Trend: Analytical needs (Experimental design) This is NEW! Marketing campaign

23 Summary and Conclusions Data Mining Knowing the customer CRM/Marketing Profit! E-Business taxonomy: E-commerce E-media E-markets Source of revenue Selling goods to customers (ex: Amazon) -free media: selling audience to advertisers (Yahoo, Google) Acting as intermediary between sellers and buyers: bidding, auctions, etc (ex: ebay) -Subscription: selling content to subscribers (Wall street journal) Data advantages Rich info about customer experiences + preferences (clickstream + transactions) -what items liked by 1/most customers? -Knowing what content reached which customer? -Knowing which Ad reached which customer? How much each is willing to pay for what? -which customers like an item? Possible goals of DM / PM -Personalization -Targeted marketing/ -Analyzing advertising effectiveness -Targeting Ads to particular audience -Matching buyers to sellers to optimize transaction profit -More complex auctioning: sellers bid for price + service, multi-tiered offers