E-Business. E-marketing and E-CRM

Transcription

1 E-Business E-marketing and E-CRM

2 Outline Introduction Customer selection Customer acquisition Customer retention Customer extension IS solutions: Big Data

3 What is (E-)CRM? Question: CRM stands for Customer Relationship Management: What is it? Why should you do it?

4 Customer relationship management What is CRM: Building and sustaining long-term business with customers Why: Acquiring customers is expensive, but so is keeping customers! E-CRM: Using digital communications technologies to acquire and retain customers E-CRM buzzwords: Magnetic, Sticky and Elastic

5 Customer lifecycle

6 Customer profiling Customer profile: 1. Information used to segment customer 2. Set of rules to describe the behaviour of customer IDIC framework to build one-to-one relationship: 1. Customer Identification 2. Customer Differentiation 3. Customer Interactions 4. Customer Customization

7 Customer conversion process Web browsers or offline audience ê Site visitor ê Engaged site visitor ê (Sales) Prospect ê Customer ê Repeat customer Conversion rate

8 Customer lifecycle

9 Customer acquisition Customer acquisition: Techniques to gain new prospects and customers In e-business two sides: Acquire new customers as qualified leads Encourage existing customer to migrate to online services Requires interactive marketing communications: Traditional/offline New/online

10 Online marketing communications Offline marke5ng Social Network marke5ng Search marke5ng Website and partner sites Online PR Online partnership Interac5ve ads Opt- in e- mail Viral marke5ng

11 Online marketing communications Search marketing: Search engine optimization Paid search Pay-per-click Paid for inclusion feeds

12 Sidestep: Google How does Google work? Crawling Indexing Ranking or Scoring: Google Page Rank Search Engine Result Page (SERP) Rank Query request and result serving Key-phrase analysis

13 Google Page Rank Formula: PR(A) = (1 - d) + d * SUM ((PR(I->A)/C(I)) Named after Larry Page Calculates probability to eventually end up on page Damping factor: 85% (= apply 15% random surfing) Is basis for Google rank Page Rank is determined by: Number of links directing to page Page Rank of referring pages

14 Google Page Rank picture A 3.3% B 38.4% C 34.3% D 3.9% E 8.1% 1.6% F 3.9% 1.6% 1.6% 1.6% 1.6%

15 SERP Rank (1/3) SERP: Search Engine Result Page Google Secret ( Google Love ): Based on Page Rank + Relatively large set of factors Continuously updated

16 SERP Rank (2/3) Contribu)on (%) 5% 6% 21% 7% 7% 7% 21% 11% 15% hup:// ranking- factors Page Level Metrics Domain Level Link Authority Features Page Level Keyword Usage Domain Level Keyword Usage Page Level Social Metrics Domain Level Brand Metrics Page Level Keyword Agnos5c Features Page Level Traffic/Query Data Domain Level Keyword Agnos5c Features

17 SERP Rank (3/3) Positive on-page factors: Keyword in URL/domain name/tag Negative on-page factors: Graphics / Affiliate / keyword stuffing Excessive crosslinking Positive off-page factors: Page Rank, Site age Line from Expert site, anchor text in link to Negative off-page factors: Zero links to, link buying, domain hijacking (

18 Advertising networks Best known for ability to present users with banner advertisements based on a database of user behavioral data Examples: AdSense, DoubleClick (Google Ads) iad (Apple) Ad server selects appropriate banner ad based on cookies and backend user profile databases

19 Price & Demand Pricing Strategy: Elasticity of demand Demand: the Long Tail?

20 Price elasticity of demand (PED) % change in quantity demanded ΔQ/Q PED = = % change in price ΔP/P Negative, but usually referred to as positive value PED value PED = 0-1 < PED < 0 PED = < PED < - 1 PED = - Descrip)ve terms Perfectly inelas5c demand (Rela5vely) inelas5c demand Unit elas5city Rela5vely elas5c demand Perfectly elas5c demand

21 Price-Demand curve Price ΔP ΔQ Demand

22 Price-Demand example P A B C D E F Q A: Price = 9 Quant = 2 Revenue = 18 B: Price = 8 Quant = 4 Revenue = 32 PED = (2 / 3) ( 1/ 8.5) = 5.67

23 Price-Demand example P A B C D E F Q E: Price = 2 Quant = 16 Revenue = 32 F: Price = 1 Quant = 18 Revenue = 18 PED = (2 /17) ( 1/1.5) = 0.176

24 Price-Demand example P A B C D E F Q C: Price = 5.5 Quant = 9 Revenue = 49.5 D: Price = 4.5 Quant = 11 Revenue = 49.5 PED = (2 /10) ( 1/ 5) = 1.00

25 Effect on revenue % change in quantity demanded ΔQ/Q PED = = % change in price ΔP/P PED value PED = 0-1 < PED < 0 PED = < PED < - 1 PED = - Descrip)ve terms Raising price increases revenue Raising price increases revenue Change in price does not effect total revenue Raising prices decreases revenue Raising prices reduces revenue to zero

26 Effect of Internet on Price 1. Increased price transparency 2. Downward pressure on price (commoditization) 3. New pricing approaches Dynamic pricing: Auctions, Yield management Aggregated buying Price discrimination 4. Alternative pricing structures or policies Payment per use Rental at fixed cost per month Versioning Free: to build market awareness

27 The long tail concept Larger share of popula5on in tail than under normal distribu5on The Future of Business is Selling Less of More Chris Anderson (2001)

28 Customer lifecycle

29 Customer retention Goals: Retain customers of organization (repeat customers) Keep customers using online channel (repeat visits) How? Satisfaction è loyalty è profitability

30 Customer retention: key tools Personalization: Delivering individualized content Mass customization: Delivering customized content to groups of users Collaborative filtering: Profiling of customer interest coupled with delivery of specific information and offers, based on interest of similar customers

31 Customer retention: lifetime value Customer lifetime value (LTV or CLV): Total net benefit that a customer will provide a company over the total relationship with the company Formula GC: yearly gross contribution per customer M: yearly retention cost (per customer) r: yearly retention rate d: yearly discount rate

32 Customer lifetime value

33 Components of lifetime value Suggestions for product or service improvement Net impact on operating profit Referrals Reduced cost of serving customer Purchases of new products Purchases of standard products Customer acquisition cost

34 Customer lifecycle

35 Customer extension Customer extension: Deepening the relationship with the customer through increased interaction and product transactions Goal: increase lifetime value of customer Techniques: Re-sell Cross-sell Up-sell Reactivation Referrals

36 Outline Introduction Customer selection Customer acquisition Customer retention Customer extension IS solutions: Big Data

37 IS solutions Pre e-business era customer info: Marginal info about link purchase ç è customer E-business era customer info: Who bought what? What did they buy earlier? What did they look at? How did they navigate? How do they respond to promotion, review, layout... Core: database technology 1. Personal and profile data 2. Transaction data 3. Communications data

38 Big data Q: What is Big Data? Various definitions: Data sets which are so large they require new forms of processing; Data sets which standard database tools cannot handle; Data sets which are too extensive to permit iterative analysis: one-pass analysis is necessary; High volume, high velocity and/or high variety information assets that require new forms of processing.

39 Examples of big data Large Hadron Collider: petabyte (10 15 ) per second. Walmart: 1 million customer transactions per hour. Social network analysis: 2.5 exabytes (10 18 ) per day. Google translate: 200 milliard words from UN and EU documents.

40 How much data

41 Why now? Automatic data capture (often secondary) Simulations (e.g. meteorology, physics) Exponential growth in computer memory 3 dimensions in data growth: Volume Transaction data, sensor data, social media,... Velocity RFID, sensors, require near-real time processing Variety Different formats, sources, unstructured data

42 Big data is so

43 Big data is data mining Data mining is Computer science: Parallel processing Distributed databases + Statistics: Descriptive: Statements about the data we have: Business intelligence: Measurements, trends... Inferential: Statements about data we do not have: The future (forecasting) The population (prediction)...

44 What is data mining? Knowledge discovery in databases: Non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data Data mining: Development and analysis of algorithms for the extraction of patterns and models from large data bases. Model: Abstraction of reality (the application domain). Describes relationships among attributes (variables, features), tuples (records, cases), or both.

45 Example: classification trees Credit risk predic5on Income > Good Risk Age >37 37 Marital status Bad risk married not married Good risk Bad risk

46 More examples Linear regression: Spend = a + b*income + c*wealth +... Logistic regression: P(Sale) = g(d + f * budget + g * price +...)

47 Data mining for CRM Techniques include: Query-driven Model-driven Rule-based Collaborative filtering

48 Data mining process model

49 Data mining process model Business understanding Project objec5ves and requirements Conversion to data mining problem defini5on Preliminary plan to achieve objec5ves

50 Data mining process model Data understanding Ini5al data collec5on Familiarity with data Iden5fy data quality problems Discover first insights into data Detect interes5ng subsets to form hypotheses for hidden informa5on

51 Data mining process model Data prepara5on Construct final data set Table, record, auribute selec5on Transforma5on Cleaning of data

52 Data mining process model Modeling Select and apply various modeling techniques

53 Data mining process model Evalua5on Evaluate the constructed model Review steps executed to construct the model

54 Data mining process model Deployment Carried out by client, not by the data analyst Simple version: Generate report Complex version: Implement repeatable data mining process

55 Summary (E)-CRM Customer selection Customer acquisition Customer retention Customer extension IS solutions: Big Data