Telco Churn Roi V3


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Telco Churn Roi V3

  1. 1. Reducing Voluntary Churn via Predictive Analytics for Telecom Operators Making the business case and determining appropriate retention campaign budgets for mobile subscribers with a high propensity to switch
  2. 2. Overview (Slide 1 of 15) Churn • Monthly (Voluntary)  Telco Readiness Checklist – Post vs Pre • Segmentation – Fixed vs Mobile • Predictive Analytics • Churn Prediction – Statistical modelling • Acquisition • Demographics – Costs of Customer Acq. • Usage (CDRs) (COCA) • Voluntary Churn Reduction • Servicing – Retention Campaigns – Budgeting • Retention – ROI / EVA – Voluntary Churn • FUDs • Customer Lifetime Value – Fears, Uncertainties, Doubts (CLV) • CSF – Critical Success Factors HK & AS 2
  3. 3. Telco Readiness Checklist  How are you defining your customer segments?  What is your monthly churn (total and per segment)?  Are you tracking reasons for churn? First we CRAWL …  What portion of your total churn is voluntary?  What is your monthly ARPU (Average Revenue Per User)?  What is your COCA (Cost of Customer Acquisition) per subscriber?  What is your definition of an active subscriber? Then we WALK  What is your active subscriber base (in millions)?  What are your average subscriber tenures (in months)?  What is your cost of capital (WACC)?  What is the breakup between Pre-paid & Post-paid for all of the above? Then we RUN  What about mobile vs fixed line (POTS) breakup?  How many months of CDRs do you keep online for call analysis?  What is your definition of CLV (Customer Lifetime Value) and its avg value? Then we  What financial metrics do you use to determine whether FLY! to fund a particular project? (EVA, ROI, discounted payback periods, etc) If you don’t have all the answers above you need to get started on them before going much further on voluntary churn reduction using predictive analytics. We have got to be able to CRAWL before we can FLY! 3 HK & AS
  4. 4. Segmentation (Slide 3 of 15) • Most telcos define their customer segments using some of the following „top-down‟ approaches: – By payment type (pre-paid vs. post-paid/contract) – By ARPU (revenue generated) – By tenure (age on network) – By demographics (location, income, job, gender, etc) – By usage • VAS, data/SMS/MMS, other non-voice penetration • Roaming, ISD/international, STD/domestic long distance, voice-mail – By handsets/devices • While this is an important first step, there are supplementary „bottom-up‟ segmentation approaches using statistical analysis and grouping by behavioral similarities that have better predictive power 4 HK & AS
  5. 5. Predictive Analyticsstatistics and data • “Predictive analytics encompasses a variety of techniques from mining that process current and historical data in order to make “predictions” about future events. Such predictions rarely take the form of absolute statements, and are more likely to be expressed as values that correspond to the odds of a particular event or behavior taking place in the future. • “In business, the models often process historical and transactional data to identify the risk or opportunity associated with a specific customer or transaction. These analyses weigh the relationship between many data elements to isolate each customer‟s risk or potential, which guides the action on that customer. • “Predictive analytics is widely used in making customer decisions. One of the most well-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer‟s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics are also used in insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields. • 5 HK & AS
  6. 6. Predictive Analytics (cont.) • The goal is to analyse your mobile customer demographics (fairly static) to drive bottom-up segmentation (correlation to churn propensity) – It is assumed you are ALREADY doing traditional top-down segmentation but are reaching its limits of usefulness • Then to take their behavioral/usage data (from CDRs which is quite dynamic) to arrive at a score for the probability to churn within the given time period for each statistical segment – At least 1-2 quarters (3-6 months) of Call Data Records (CDRs) are needed for the predictive engine to be effective but the more the better – To capture seasonal variations around festivals/holidays etc, 12-18 months is required (4-6 quarters) • This will be filtered against the list of high value (ARPU or profitability/CLV) subs to get those worth retaining • This is but one application for predictive analytics, others include: – Cross-sell & Up-sell opportunities (likelihood to buy) – Credit scoring for setting dynamic limits and risk management – Fraud detection (post-paid only) 6 HK & AS
  7. 7. Acquisition (COCA) • COCA = Cost of Customer/subscriber Acquisition (also called COSA) • COCA has at least 3 components for most telcos: 1. Channel Margins per customer – Lower margins are more efficient for COCO (Company Owned & Company Operated) stores ONLY – Franchisee/retail partners need incentives (higher margins) to push your products/services – Should be between 1/4th to 1/3rd of your COCA (25%-33%) for telcos (anything above 34% is a red flag) Warning 2. Handset Subsidies – Only relevant if handsets are bundled with contracts (post-paid) – Also if handsets are locked to your network (portability) – Should be below half your COCA (< 50%) and if its above, that‟s another red flag Warning 3. Advertising/MarCom costs per subscriber – Includes all costs of MARketing COMmunications – Should be below 1/4th (over 25% would again be a red flag) Warning 7 HK & AS
  8. 8. Servicing (Slide 7 of 15) • There are costs associated with servicing your customers – The number of times they contact your call centre could mean the difference between a subs worth retaining or not at the SAME ARPU – If you have retail outlets, each time they walk-in, you will incur costs which need to be accounted for – Only after these ongoing costs have been factored in will you get a true picture of your customer‟s profitability or CLV 8 HK & AS
  9. 9. Retention • Loyalty programs (Unsustainable Competitive Advantage) – Shotgun approach like airline miles or credit card reward pts – Easier to implement (quick win) – Lazy approach thus less effective over the long term – What will you do when your competitors also offer the same rewards (bribes)? – What kind of mercenary behavior are you really encouraging from your customers (blackmail / threatening to quit)? • Churn prediction & reduction (Sustainable Competitive Advantage) – Only focused on those who are likely to leave you which can be lower cost or higher value offers at the same total campaign budget (since the money will need to be divided among fewer subs) – Better ROI / EVA but „slow win‟ (no results in the 1st quarter) 9 HK & AS
  10. 10. Monthly Churn • The is the portion of your active subscriber base that goes inactive (via passive/implicit cancellations) each month or explicitly cancels your connection/account • If this value < 1%/month, you are better off spending your money on other enhancements that your customers are demanding (higher ROI projects) • Its critical to track the reasons for churn of your subscriber base • In most cases the churn for pre-paid is higher than that for post-paid subscribers 10 HK & AS
  11. 11. Churn Prediction • Various statistical models will have varying levels of performance as far as predictive ability goes based on the data you feed them but most should have some kind of feedback loop (self- learning/continual refinement) since you don‟t want to keep changing your models every year as your customer profile drifts • Model Performance – This is the model‟s ability to correctly identify customers about to churn out voluntarily – For telcos this is usually between 65% - 85% with lower rates being more fiscally conservative (lower ROI) • Strategy Effectiveness – These are the ratio of churners who actually take up your retention offer – This varies between 5%-15% for most telcos with lower values being more financially conservative 11 HK & AS
  12. 12. Voluntary Churn Reduction • You can only reduce voluntary churn NOT eliminate it entirely (due to diminishing returns) • You CANNOT do anything about involuntary churn – Death of the customer – Moving / relocating outside your service area – Changing jobs/employers (for company connections) • Once you have the reasons for churn, you can focus on the voluntary • Until you get the reasons, a rule of thumb is that voluntary churn is usually around 2/3rd – 3/4th of total monthly churn for most telcos • The higher your voluntary churn, the more room for improvement (better ROI) • You should target to bring down the ratio of voluntary churn to about: – ½ in the short term (1-2 quarters) – 1/3rd in the medium term (1-2 years) – 1/4th in the long term • If your voluntary churn is already below 25% of total churn, spend your resources elsewhere (you are in pretty good shape for now) ! 12 HK & AS
  13. 13. Fears, Uncertainties, Doubts (FUDs) • Why should a model built in the West work for Africa? • My customers are uniquely different • What if I spend money to find out something I already know? • What if the retention campaigns do not reduce my churn? • How will my staff get trained so we are not dependent on outsiders/vendors to keep going? • Has this been implemented anywhere else in Africa? • Who are the reference clients? • Who are the partners? 13 HK & AS
  14. 14. Critical Success Factors (CSF) • Retention MUST be a top-down initiative since it requires the assistance of many different depts • The CFO, CMO, CIO/CTO must be involved at all stages of the project for support & buy-in by forming a steering committee that meets weekly initially and then monthly to review progress/milestones • The CMD/CEO must lay out the vision and drive the organisational changes needed to support this initiative 14 HK & AS
  15. 15. Our Partners (Slide 14 of 15) • Cranes Software – 14 years old with 600 employees – US$ 60 Million in revenues – Statistical Consulting (Predictive Analytics) – Bangalore, India • Siemens – Gurgaon, India 15 HK & AS
  16. 16. Summary • Review the Telco Readiness Checklist before jumping into predictive analytics • Meet the benchmarks (for this effort to make financial sense for your enterprise): – Base >= 1 million active subs – Voluntary Churn >= 1%/month – Voluntary/Total Churn > 25% – 3-6 months CDRs minimum for analysis and modeling • The CSF are prerequisites for any such initiative to kickoff 16 HK & AS
  17. 17. Questions? Thank you! - HK & AS 2007 April 17 HK & AS
  18. 18. Appendix • What is CLV / LTV and its components (HBR)? • What is Customer Equity and how do we measure it (HBR & Wikipedia)? • What is Economic Profit (McKinsey)? • What are Social Networks (SN & ASN)? • What is Data Mining of CDRs (DM & KD)? • What is Residual Customer Value (RCV)? 18 HK & AS
  19. 19. Churn ≡ Attrition ≡ Defection • “Customer attrition, also known as customer churn, customer turnover, or customer defection, is a business term used to describe loss of clients or customers. • Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the quot;...cost of retaining an existing customer is far less than acquiring a new one.quot; Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients. • Companies usually make a distinction between voluntary churn and involuntary churn. Voluntary churn occurs due to a decision by the customer to switch to another company or service provider, involuntary churn occurs due to circumstances such as a customer's relocation to a long-term care facility, death, or the relocation to a distant location. In most applications, involuntary reasons for churn are excluded from the analytical models. Analysts tend to concentrate voluntary churn, because it typically occurs due to factors of the company-customer relationship which companies control, such as how billing interactions are handled or how after-sales help is provided. • When companies are measuring their customer turnover, they typically make the distinction between gross attrition and net attrition. Gross attrition is the loss of existing customers and their associated recurring revenue for contracted goods or services during a particular period. Net attrition is gross attrition plus the addition or recruitment of similar customers at the original location. Financial institutions often track and measure attrition using a weighted calculation called Recurring Monthly Revenue (or RMR). In the 2000s, there are also a number of business intelligence software programs which can mine databases of customer information and analyze the factors that are associated with customer attrition, such as dissatisfaction with service or technical support, billing disputes, or a disagreement over company policies. 19 • & AS HK
  20. 20. Customer Lifetime Value (CLV) from transactions • „The net profit a company accrues with a given customer during the time that the customer has a relationship with the company.‟ –RT Rust & KN Lemon, HBR (Sept „04); pg 112-113 • This implies FIRST having a consolidated/unified view of our customers –Currently I‟m viewed as 5 independent subs for my FLP, FWP, VCC, BB etc. • Then we must put in metrics to track the costs of servicing EACH customer –Number of calls, emails, visits to retail stores/shops etc. 20 HK & AS
  21. 21. CLV (cont.) over a customer‟s • Net Profit = Total Revenues generated lifetime – Total Cost (direct & indirect) for that customer • Total Cost = Service Costs + Retention Costs + Defaulted Amounts • Service Costs = Contact Costs + Repair Costs • Contact Costs = Email, Fax, Phone, visits to RWW/WWE • Retention Costs = Discounts + Upgrades + Loyalty Bonuses • CLV = LTV = EP – Customer Lifetime Value = Life-Time Value = Economic Profit 21 HK & AS
  22. 22. CLV (cont.) customer value (LCV), or • “In marketing, customer lifetime value (CLV), lifetime lifetime value (LTV) is a metric that projects the value of a customer over the entire history of that customer's relationship with a company. Use of customer lifetime value as a marketing metric tends to place greater emphasis on customer service and long-term customer satisfaction, rather than on maximizing short- term sales. • “Customer lifetime value has intuitive appeal as a marketing metric, because in theory it allows companies to know exactly how much each customer is worth in dollar terms, and therefore exactly how much a marketing department should be willing to spend to acquire/retain each customer. In reality, it is often difficult to make such calculations due to the complexity of the calculations, lack of reliable input data, or both. • “The specific calculation depends on the nature of the customer relationship. For example, companies with a monthly billing cycle, such as mobile phone operators, can count on a reasonably reliable stream of recurring revenue from each customer. Car manufacturers, on the other hand, have less insight into when or whether a customer will make a repeat purchase. • 22 HK & AS
  23. 23. CLV (cont) C u s to m er L ife tim e V a lue Net P ro fit T o tal T o tal R e ve n ue C o s ts HK & AS 23
  24. 24. CLV (Net Profit  Total Rev) C u s to m er L ife tim e V a lue Net P ro fit T o tal T o tal R e ve n ue C o sts In itial / U pfro nt O n go ing (C a p E x) (O p E x) fo r AL L p rod / se rvices H a rd w a re S o ftw a re F ix ed V a ria b le HK & ASta ls Ren U s a ge 24
  25. 25. CLV (NetP  TRev  CapEx) C u sto m er L ife time V a lue Net P ro fit T o tal T o tal R e ve n ue C o sts In itial / U pfro nt O n go ing (C a p E x) (O p E x) fo r AL L p rod / se rvices H a rd w a re S o ftw a re F ixed V a ria b le R e n ta ls U sa ge DISGUISED CDMA HK & ASC 25 H a n dse ts A cce sso ries G SK HC TELCO EXAMPLE
  26. 26. CLV (NetP  TRev  OpEx) CLV Net P ro fit T o tal T o tal R e ve n ue C o sts In itial / U pfro nt O n go ing (C a p E x ) (O p E x ) fo r A L L p rod / s e rv ic es H a rd w a re S o ftw a re F ix ed V a ria b le U s a ge R e n ta ls C L IP V o ice D a ta HK & AS 26
  27. 27. NP  TR  OpEx  Variable CLV Net P ro fit T o tal T o tal R e ve n ue C o s ts In itial / U pfro nt O n go ing (C a p E x ) (O p E x) F ix ed V a ria b le U s a ge DISGUISED CDMA TELCO EXAMPLE V o ice D a ta Home R o a m ing SMS R -C o n n e ct R -W o rld MMS HK & AS 27
  28. 28. NP  TR  OpEx  Var  Voice CLV N e t P ro fit T o tal T o tal R e ve n ue C o sts In itial / U pfro nt O n go ing (C a p E x ) (O p E x ) F ixed V a ria b le V o ice D a ta Home R o a m ing HK IS AS &D 28 L o c al STD In c om ing
  29. 29. NPTROpExVarVoiceTariffs CLV N e t P ro fit T o tal T o ta l R e ve n ue C o s ts In itial / U pfro nt O n g o in g F ix ed V a ria b le V o ice D a ta Home R o a m in g L o cal STD IS D In c o m in g HKix ed F & AS 29 O n -N et O ff-N et M o b ile
  30. 30. CLV (NP  Total Costs) CLV Net P ro fit T o tal T o tal R e ve n ue C o sts S e rvice R e ten tion D e fau lted C o sts C o sts A m ou n ts C o nta ct R e p a ir D isco u n ts L o ya lty U p gra de C o s ts C o s ts C re d its B o nu ses C o s ts HK & AS 30
  31. 31. CLV (NP  TC  Service Costs) CLV Net P ro fit T o tal T o tal R e ve n ue C o s ts S e rv ice R e ten tion D e fau lted C o s ts C o s ts A m ou n ts C o nta ct R e p a ir C o s ts C o s ts P ho ne F a x es E m a ils V is its DISGUISED CDMA C a lls RW W & TELCO EXAMPLE WWE HK & AS 31
  32. 32. Customer Equity (CE) • „The sum of the lifetime values of all the firm‟s customers, across all the firm‟s brands…‟ –Rust & Zeithaml, HBR (Sept ‟04) • “Customer Equity is the Net Present Value of a customer from the perspective of a supplier. It can - and should - also include customer goodwill that is normally not expressed in financial terms, eg a customer's level of loyalty and advocacy.” – • Maximising Customer Equity should be the PRIMARY goal of ALL firms to ensure long term success • ALL other measure including Brand Equity are secondary 32 HK & AS
  33. 33. Calculating Economic Profit Revenue ARPU Interconnect (incoming minutes) – Costs Economic Profit (EP) Interconnect (outgoing minutes) is a measure of the current profitability of Network usage (total minutes) individual customers and is stated as Cost of store (FSD interactions) monthly figure Call center cost (call center interactions) Collections cost (by credit category) Bad debt (amount overdue) Billing and IT (fixed per sub) G&A and others (fixed per sub) = Economic Profit 33 Source: McKinsey (2004 Q3)
  34. 34. Differentiated cust. treatment should be applied to the 11 value drivers across the cust. lifecycle Cost Lifetime value drivers and associated revenues & costs Revenues Cumulative customer lifetime value in dollars Customer joins (rejoins) service 2000 Customer leaves service 10 Bad debt 1500 Migration 8 1000 9 Cross-sell/up-sell 5 500 Churn 11 Win-back 6 0 Credits & adjustments 7 Renewal promos -500 1 Consideration 4 Cash cost to serve -1000 3 Recurring revenue 2 -1500 Acquisition cost Months of subscription life -2000 1 2 3 4 12 13 36 37 38 39 40 Source: McKinsey (2004 Q3) 34
  35. 35. 20% of prepaid subscribers use phone as receiver ONLY & do not generate outgoing calls Percent of subscribers, I/B and O/B MoU by quotient of inbound vs. outbound MoU Monthly Percent of total postpaid subscriber base, MoU Subscriber minutes of DISGUISED CLIENT distribution use 90 EXAMPLE I/B and O/B develop in Fraction of subscribers 45% exact opposite way to who use phone as Subscribers postpaid - O/B receiver only much Monthly O/B MoU 40% 80 constant, I/B rising in larger than in postpaid Monthly I/B MoU prepaid 35% 70 30% 60 Insight: 25% 50 20% receivers-only 20% 40 seem as inactive subscribers, yet 15% 30 generate incoming 10% 20 revenues and can 5% 10 potentially be switched to outgoing 0% 0 No 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 > 3 No usage IB OB Quotient of inbound MoUMcKinsey (2004 Q3) MoU / outbound 35 Source:
  36. 36. Breaking the 1st time recharge barrier presents an opportunity for increasing prepaid revenues DISGUISED CLIENT Number of recharges done by customers of one cohort1) EXAMPLE Percent of the cohort customers 50% Insights: Tariff 1 Significant portion of 45% prepaid acquisitions Tariff 2 •Trial offer of prepaid have never recharged 40% recharge card directly Subscribers recharging at 35% with bundle at special least once, recharge an price average of 4.1x in the 30% following 6 months •Specific recharge offers 25% for infrequent recharge 20% customers 15% •Alternative recharge 10% methods based on 5% customer location 0% Number of recharges2) 0 1 2 3 4 5 6 7 8 9 10+ 1) All prepaid customers acquired in specific calendar month 2) Number of recharges per subscriber 36 Source: McKinsey (2004 Q3)
  37. 37. First recharge stimulation provides DISGUISED CLIENT ARPU uplift EXAMPLE Test results from standardized ROI-reporting 5% bonus on recharge to new customer two months Test design after activation contacted with SMS Offer: 5%-10% on next (ARPU in EUR) 8 recharge within 30 days Target group – Communication: Target Control group 7 group contacted with SMS or mailing 6 Average – Target group: New revenue lift of customers with 5 € 0.42 (Rs. 23) 2-6 months since in months 0-3* activation without first 4 Launch of campaign recharge 3 – Campaign design: 9 -3 -2 -1 0 1 2 3 different sub-campaigns defined Months relative to campaign drop-date Standardized post- Submitted to Test campaign reporting Environment and available in June tested in Feb / 37 2004 March 2004 Source: McKinsey (2004 Q3)
  38. 38. Calculating CLV Create Compile Migration Assuming Calculate clusters no dis- having similar migration C frequency CLV (per A B D continuity characteristics matrix derivation cluster) in market forces Cluster B 85 % Clusters constructed Cluster B 85 Abwande 5% % rung per network age- Cluster K group, according to: 10 Clust % er B Abwand- 5% erung - Existing segment Cluster K 85 % Cluster K - Handset model 10 Abwande 5% % rung - Current rate plan Cluster J 10 % - Current usage - Economic profit Firstly, on basis of The second step On the basis of the The migration historical data, all cust are documents, with the help calculation of migration probability is now allocated to network age- of a quot;migration matrixquot;, frequency, an assertion applied to project the groups then, the cust in the cluster to which cust can be made about how expected lifetime path each network age-group, from a certain network probable it is that a cust of the customer. on the basis of their age-group will migrate the in the next network age- existing segment, handset following year, and how CLV is calculated by group will belong to a model, and probability to many cust migrate overall multiplying the average churn, are allocated to (a migration occurs when particular cluster (e.g., realized EP for each clusters having similar one of the parameter probability that a cust in cluster with the characteristics changes cluster B this year, will be path/migration 38 in cluster K next year) probability Source: McKinsey (2004 Q3)
  39. 39. Calculating CLV Calculation of CLV per cluster for different network age groups CLV for network age group N • (the next to last age group with none zero EP‟s): CLVN/R = EPN+1/R CLV for network age group N-1: CLV(N-1)/R = [ fRA* (EPN/A + CLVN/A ) + fRB * (EPN/B + CLVN/B) + ... ] / (1 + i) Key: fRA = Probability, that the customer in following year (i.e., at N) will be in clusterN/A i = Discount rate Source: McKinsey (2004 Q3) 39
  40. 40. CLM datamart is generated using a case tool* First step: Third step: Second step: Define subject areas in data Generate scripts automatically for Create references or mart physical database creation links/dependencies Create tables within each – Modify the model accordingly if among tables subject area syntax errors detected Define foreign keys for Define all data fields including applicable tables and code, name, data type, length check consistency and other attributes Identify primary keys** for DBA executes the each table scripts to create the 40 * E.g., Sybase Power Design database ** A unique identifier of an entity Source: McKinsey (2004 Q3)
  41. 41. Exploiting Social Networks (via Data Mining of Telco Call Data Records)
  42. 42. Overview • What is a Social Network (SN)? – Network Classification – Augmented Social Networks (ASN) – Mapping SN (Visualisation) – Pattern Recognition (Interpretation) – Social Network Analysis (SNA) • What is Data Mining (DM)? – How do we mine CDRs? • What types of customer behavior are we interested in? – What are the applications in DM of CDR? • Customer Lifetime Value (CLV) – Residual Customer Value (RCV) 42 HK & AS
  43. 43. Definition of SN • „A social network (SN) consists of any group of people connected through various social familiarities ranging from casual acquaintance to close familial bonds. Members of a social network may not have any real awareness of the network as a whole. • The rule of 150 states that the size of a genuine/functional social network is limited to about 100-150 members. • Social networks are often the basis of cross-cultural studies in sociology and anthropology. The rule of 150, mentioned above, arises from cross-cultural studies in sociology and especially anthropology of the maximum size of a village (in modern parlance most reasonably understood as an ecovillage). It is theorized in evolutionary psychology that the number may be some kind of limit of average human ability to recognize members and track emotional facts about all members of a group. However, it may be due to economics and the need to track quot;free ridersquot;, as larger groups tend to be easier for cheats and liars to prosper in. Either way, it would seem that social capital is maximized by this size.‟ – 43 HK & AS
  44. 44. NetworkTV, radio)Classification • Broadcast (e.g., – Linear networks (one to many) – Sarnoff‟s Law: „…the value of the network is proportional to the number of actors‟ • Paired connections (e.g., phones, fax, email) – One to one – Metcalfe‟s Law: „…the value grows with the square of the number of actors (nodes)‟ • Social Networks – Many to many (eBay, Amazon) – Reed‟s Law: „…when the network allows communities to form then the value grows exponentially with the number of actors‟ • Allen E. (Sept ‟03) – 44 HK & AS
  45. 45. Profiting from Networks • Platforms win because of network effects • 3 developments enable business platforms –An understanding of the potential value of networks –The ability to connect different networks –The business practices to turn potential network value into actual profits • Interconnecting two networks creates value greatly exceeding the combined values of the original two unconnected networks (synergy) • Network value chain (NVC) –Broadcast Paired Social –Lower value Higher value 45 HK & AS
  46. 46. NVC • The higher up the Network Value Chain you can place your business the better • The value of your network increases vastly –The value of business opportunities for joining disconnected networks increases even faster –Leads to a „winner-take-all‟ situation –The company benefiting from a larger network can afford to pay more to grow that network (ROI is greater as you scale) • The „stickiness' of your network increases –Some of the most stressful events in life like shifting homes or changing jobs involve the disruption of social networks 46 HK & AS
  47. 47. Four C‟s CNN, Yahoo, your network‟s value to increase • Content (e.g. • Allen E. (Sept ‟03) Amazon) – – „Content is king‟ – Either info or transactions – Current, frequently updated, & relevant – Sarnoff‟s Law (publishing) • Connectivity (e.g. mobile networks, dating sites) – Metcalfe‟s Law (connecting people) • Collaboration – Scalability – Attracting new networks – Sarnoff X Metcalfe (cube power) • Communities (e.g. Usenet) – Reed‟s Law (social networks) HK & AS 47
  48. 48. The Laws of Network Value • Sarnoff 1500 – Linear – Content 1250 • Metcalf 1000 – Square – Connectivity 750 • Sar x Met 500 – Cube 250 – Collaboration • Reed 0 1 2 3 4 5 6 7 8 9 10 11 – Exponential – Communities Sarnoff Metcalf SxM Reed HK & AS 48
  49. 49. Augmented Social Nets (ASN) • S-Nets with Identification and Trust • Objectives – To create a „…system that enables more efficient and effective knowledge sharing between people across institutional, geographic, and social boundaries‟ – „To establish a form of persistent … identity that supports the public commons and the values of civil society‟ – „To enhance the ability of citizens to form relationships and self-organize around shared interests in communities of practice in order to better engage in the process of democratic governance‟ • „… it is a model for a next-generation online community that could be implemented in a number of ways, using technology that largely exists today‟ • „It is a system that would enhance the power of social networks (SN) by using interactive digital media to exploit the transitive nature of trust through the principle of six degrees of connection‟ – SN = „who do you know‟ 49 – ASN = „who do you trust‟ HK & AS
  50. 50. ASN (cont.) • “The Augmented Social Network (ASN) was proposed in a June 2003 paper presented at the PlaNetwork Conference by Ken Jordan, Jan Hauser, and Steven Foster. The paper makes the case for a civil society vision of digital identity that treats Internet users as citizens rather than consumers. The ASN is described as an Internet-wide system that enables users to find others who have relevant interests or expertise, in a context that engenders trust, so that they can form a social network more effectively. At its core is a form of digital identity that supports appropriate introductions between people who share affinities through the recommendations of trusted third parties. It also supports the distribution of media using the same Internet- wide recommendation system. • 50 HK & AS
  51. 51. Mapping Social Networks • Network visualisation tools • • Your telco‟s links to other sites online HK & AS 51
  52. 52. Social Network Analysis (SNA) • SNA „…is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities’ • „The nodes in the network are the people and groups while the links show relationships or flows between the nodes’ • „SNA provides both a visual and a mathematical analysis of human relationships’ • „Management consultants use this methodology with their business clients and call it Organizational Network Analysis [ONA].’ –Valdis Krebs, 2004 – 52 HK & AS
  53. 53. SNA • “Social network analysis (related to network theory) has emerged as a key technique in modern sociology, anthropology, sociolinguistics, geography, social psychology, information science and organizational studies, as well as a popular topic of speculation and study. • People have used the social network metaphor for over a century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. Yet not until J. A. Barnes in 1954 did social scientists start using the term systematically to denote patterns of ties that cut across the concepts traditionally used by the public and social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Social network analysis developed with the kinship studies of Elizabeth Bott in England in the 1950s and the urbanization studies quot;Manchester Schoolquot; (centered around Max Gluckman and later J. Clyde Mitchell), done mainly in Zambia during the 1960s. It joined with the field of sociometry (begun by J.L. Moreno in the 1930s, an attempt to quantify social relationships. Scholars such as Mark Granovetter, Barry Wellman and Harrison White expanded the use of social networks. • Social network analysis has now moved from being a suggestive metaphor to an analytic approach to a paradigm, with its own theoretical statements, methods and research tribes. Analysts reason from whole to part; from structure to relation to individual; from behavior to attitude. They either study whole networks, all of the ties containing specified relations in a defined population, or personal networks, the ties that specified people have, such as their quot;personal communitiesquot;. • 53 HK & AS
  54. 54. Pattern Recognition (PatRec) • PatRec „is the art of finding order in often chaotic masses of data.‟ • „One of the goals of PatRec is to quickly narrow down your set of possibilities‟ –Macro-filtration (before fine particle analysis) • „One of the toughest challenges in PatRec is knowing when you‟ve looked at enough info to make a reliable judgment.‟ –HBR (Nov ‟02) 54 HK & AS
  55. 55. PatRec (cont.) • “Pattern recognition is a sub-topic of machine learning. It can be defined as quot;the act of taking in raw data and taking an action based on the category of the dataquot;. • Most research in pattern recognition is about methods for supervised learning and unsupervised learning. • Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. • A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features. • The classification or description scheme is usually based on the availability of a set of patterns that have already been classified or described. This set of patterns is termed the training set and the resulting learning strategy is characterized as supervised learning. Learning can also be unsupervised, in the sense that the system is not given an a priori labeling of patterns, instead it establishes the classes itself based on the statistical regularities of the patterns. • The classification or description scheme usually uses one of the following approaches: statistical (or decision theoretic), syntactic (or structural). Statistical pattern recognition is based on statistical characterisations of patterns, assuming that the patterns are generated by a probabilistic system. Structural pattern recognition is based on the structural interrelationships of features. A wide range of algorithms can be applied for pattern recognition, from very simple Bayesian classifiers to much more powerful neural networks. • An intriguing problem in pattern recognition yet to be solved is the relationship between the problem to be solved (data to be classified) and the performance of various pattern recognition algorithms (classifiers). • 55 HK & AS
  56. 56. Data Mining (DM)advance from • „…an analytical tool that enables business execs to describing historical customer behavior to predicting the future.‟ – Martin Morgan, Telecommunications International (May ‟03) • DM enables companies to: – Proactively manage business relationships – Drive growth – Answer complex questions like: • Who are your most profitable customers? • How can you increase your levels of customer satisfaction, loyalty, lifetime value (CLV or LTV)? – Identify business opportunities – Implement strategies that increase revenue – Reduce expenses – Offer new competitive advantages • Same as Knowledge Discovery (KD) but less „sexy‟ and older by a few decades 56 HK & AS
  57. 57. DM (part deux) • It‟s a multi-step process that includes: –Defining a business problem –Exploring and conditioning data –Developing the model –Deploying the knowledge gained • Telecom operators must tackle specific business challenges like: –Segmenting customers (top-down and bottom-up) –Predicting customer propensity to buy (or to churn in the next period) –Detecting fraud/abuse –Increasing organisational efficiency 57 HK & AS
  58. 58. What DM is NOT … • “The term quot;data miningquot; is often used incorrectly to apply to a variety of other processes besides data mining. In many cases, applications may claim to perform quot;data miningquot; by automating the creation of charts or graphs with historic trends and analysis. Although this information may be useful and timesaving, it does not fit the traditional definition of data mining, as the application performs no analysis itself and has no understanding of the underlying data. Instead, it relies on templates or predifined macros (created either by programmers or users) to identify trends, patterns and differences. • A key defining factor for true data mining is that the application itself is performing some real analysis. In almost all cases, this analysis is guided by some degree of user interaction, but it must provide the user some insights that are not readily apparent through simple slicing and dicing. Applications that are not to some degree self- guiding are performing data analysis, not data mining. • 58 HK & AS
  59. 59. DM (cont) • Case studies (just Google these online) –Telstra Mobile (Australia‟s largest mobile operator) is reducing customer churn using data mining with SAS Enterprise Miner –A european operator calculates revenues and costs for EACH customer so it knows the actual value of each subscriber not just the ARPU –A US operator uses DM to ensure that calls are routed effectively by continuous monitoring of performance rules and data analysis of: • The history of component & trunk usage • Current network activity metrics • Retention (higher ROI than Acquisition) –Cost of keeping an existing customer is 10 times less than acquiring a new one 59 HK & AS
  60. 60. Knowledge Discovery computer science (KD) • “Knowledge Discovery is a concept of the field of that describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. The most well-known application of Knowledge Discovery is data mining also known as Knowledge Discovery in Databases (KDD). • Knowledge Discovery is the process of deriving knowledge from the input data. Some forms of Knowledge Discovery create abstractions of the input data. In some scenarios, the knowledge obtained through the process of Knowledge Discovery becomes further data that can be used for continuous discovery. • Knowledge Discovery is a complex topic that can be further categorized according to – 1) what kind of data is searched; and – 2) in what form is the result of the search represented. • 60 HK & AS
  61. 61. DM Myths • Expensive, dedicated DB, data marts or analytic servers are needed – Costly to purchase & maintain – Require data extraction for each DM project – Major waste of time • Enterprise-wide Data Warehouse (EDW) is the solution – Functions as a customer & operational db – Total cost of investment is considerably lowered – No need to purchase & maintain additional hardware – Minimise the need to move data in & out of the EDW which is labour- intensive – A US operator got consistent info 90% faster after switching to EDW from fragmented data marts – Operator decision are based on actual customer behaviour rather than gut instinct 61 HK & AS
  62. 62. CDR DM Applications • Customer Loyalty & retention – CLV & residual customer value (survival time analysis) • Fraud & risk management • Testing various marketing plans to determine ROI • Formulating new plans based on identified calling patterns • Optimising network utilisation • OSS analysis • Trend forecasting • Real-time traffic analysis • Credit scoring – Post-paid customers (outstanding balance caps/limits) 62 HK & AS
  63. 63. CLV & RCV • Customer Lifetime Value (CLV) – „…the net profit a company accrues from transactions with a given customer during the time that the customer has a relationship with the company.‟ – HBR (Sept. ‟04) • Residual Customer Value (RCV) – The remaining net profit that can be accrued from a given customer (could also be expressed as a % of CLV) to help determine if its worthwhile to try to retain them • Customer Equity (CE) – „The sum of the CLV of all the firm‟s customers across all the firm‟s brands‟ • Brand Equity (BE) – „The sum of customers‟ assessments of a brand‟s intangible qualities, positive or negative‟ 63 HK & AS
  64. 64. CLV (cont) CLV = VE + BE + RE –VE = Value Equity –BE = Brand Equity –RE = Relationship Equity • VE „is the objectively considered quality, price, and convenience of the offering‟ • BE „is the customer‟s subjective assessment of a branded offering‟s worth above and beyond its objectively perceived value‟ • RE is like the „switching costs – the customer‟s reluctance to go elsewhere because of learning curves, community benefits, relationships with salespeople‟, etc. 64 HK & AS
  65. 65. TCR & TCC CLV = NPV (TCR – TCC) • TCR = Total Customer Revenue • TCC = Total Customer Costs • AC = Acquisition Costs CR (initial/capex) CC • RC = Retention Costs (ongoing/opex) Rs. TCR • TCC = AC + RC = CC TCC • TCR = CR time 65 HK & AS
  66. 66. RCV • TCV = CV (area under the CV graph) • RCV = area under CV graph AFTER time „t‟ = TCVt • If RCV > RC, keep customer –Value of retention incentives must be < RCV - RC Rs. • If RCV <= RC, let them go CV –Not worth the expense of keeping RCV • For new customers, t=0 and RCV = TCV0 time t 66 HK & AS
  67. 67. Examples (North America) • Brand equity (low involvement goods) – Facial tissues – Grocery products – Low-priced and frequently purchased • Prepaid recharges are getting closer to FMCS = Fast Moving Consumer Services each year in those markets where prepaid mkt share > 1/4th – 1/3rd (25% - 33%) • Relationship equity – Air travel – Rental cars – Any service that involves loyalty programs • If post-paid / contracts have any form of redeemable reward points then they would fall into this category 67 HK & AS
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