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CRM by Mind Map: CRM

1. calculated across buyers and non-buyers

2. Share-of-Wallet and Market Share

2.1. Defination

2.1.1. New node measured on a percent basis and can be computed based on unit volume, $ volume or equivalent unit volumes (grams, ounces)

2.1.2. Share-of-Wallet New node

3. Popular Customer Based Value Metrics

3.1. Size of Wallet

3.1.1. Information source Primary market research

3.1.2. Evaluation assumption large wallet size indicates more revenues and profits

3.1.3. defination Size-of-wallet ($) of customer in a category

3.2. Share of Category Requirement

3.2.1. Information source Numerator volumetric sales of the focal firm - from internal records Denominator total volumetric purchases of the focal firm’s buyer base- through market and distribution panels primary market research (surveys) and extrapolated to the entire buyer base

3.2.2. Evaluation Accepted measure of customer loyalty for FMCG categories does not indicate if a high SCR customer will generate substantial revenues or profits controls for the total volume of segments/individuals category requirements

3.2.3. Defination SCR (%) of firm or brand in category

3.2.4. for categories where the variance of customer expenditures is relatively small

3.3. Share of Wallet

3.3.1. Defination Individual Share-of-Wallet (ISW) of firm to customer (%)

3.3.2. Information source Numerator From internal records Denominator primary market research (surveys)

3.3.3. New node

3.3.4. if the variance of consumer expenditures is relatively high

3.4. Aggregate Share-of-Wallet (ASW)

3.4.1. Information source Numerator From internal records Denominator market and distribution panels primary market research (surveys) extrapolated to the entire buyer

3.4.2. Defination Individual Share-of-Wallet (ISW) of firm to customer (%) Aggregate Share-of-Wallet of firm (%)

3.4.3. Evaluation Important measure of customer loyalty

3.5. Transition Matrix

3.5.1. shows that the recommended strategies for different segments differ substantively

3.5.2. makes optimal resource allocation decisions only by segmenting customers along the two dimensions simultaneously

4. What is CRM

4.1. acquire & retain profitable customers

4.2. long-term and sustainable customer relationships - Add value

4.3. interdisciplinary field

4.4. maximizing customer satisfaction

5. CRM Business Strategy

5.1. Customers is core business

5.2. Effectively managing CR means Success

5.3. select & manage customers for Long Term

5.4. effective sales, marketing &services processes supported by customer-centric business philosophy & culture

5.5. Simple idea - treat different customers differently

6. Classification of CRM Applications

6.1. facing

6.1.1. customers interaction

6.2. touching

6.2.1. interact with the applications

6.3. centric intelligence

6.3.1. analyze results

6.4. Online networking

6.4.1. build personal relationships

7. Levels of e-CRM

7.1. Foundational service

7.2. Customer-centered services

7.3. Value-added services

8. Strategic Customer Based Value Metrics

8.1. Past Customer Value

8.1.1. Equation New node

8.1.2. Transactions have to be adjusted for the time value of money

8.1.3. Limitations: Does not consider whether a customer is going to be active in the future.

8.2. Lifetime Value

8.2.1. Equation

8.3. Customer Equity

8.3.1. New node

8.4. RFM

8.4.1. Recency

8.4.2. Frequency

8.4.3. Monetary value

8.4.4. Limitations Independently links customer response data with R, F and M values and then groups customers, belonging to specific RFM codes (1 to 5) May not produce equal number of customers under each RFM cell since individual metrics R, F, and M are likely to be somewhat correlated For practical purposes, it is desirable to have exactly the same number of individuals in each RFM cell

8.4.5. Breakeven Value Breakeven - net profit from a marketing promotion equals the cost associated with conducting the promotion Breakeven Value (BE) = unit cost price / unit net profit BE computes the minimum response rates required in order to offset the promotional costs involved and thereby not incur any losses

8.4.6. RFM and BEI Customers with higher RFM values tend to have higher BEI values Customers with a lower recency value but relatively higher F and M values tend to have positive BEI values Customer response rate drops more rapidly for the recency metric Customer response rate for the frequency metric drops more rapidly than that for the monetary value metric

8.4.7. RFM Computation – Regression Regression techniques to compute the relative weights of the R, F, and M metrics Relative weights are used to compute the cumulative points of each customer The higher the computed score, the more profitable the customer is likely to be in the future The pre-computed weights for R, F and M, based on a test sample are used to assign RFM scores to each customer This method is flexible and can be tailored to each business situation

9. Types of e-CRM

9.1. Operational

9.2. Analytical

9.3. Collaborative

9.4. strategic

10. Examples of Customer Services

10.1. Search and comparison

10.2. Free Trial

10.3. Technical & information and Services

10.4. Customized products & services

10.5. Status Tracking

11. Tools for Customer Service

11.1. Personalized web pages

11.2. FAQs

11.3. Email & automated response

11.4. Chat rooms

11.5. Live chat

11.6. Call centers

11.7. Troubleshooting

12. Issues Related to CRM Failures

12.1. Difficulty in measuring

12.2. Failure to identify & focus on specific business problems

12.3. Lack of active senior & sponsorship

12.4. Poor user acceptance

12.5. poorly defined business process for automate

13. How to implement CRM to avoid its failure

13.1. Conduct a survey

13.2. Carefully consider Sales,Service, Marketing and channel/partner management

13.3. Quality but not quantity

13.4. how CRM match organization objectives

13.5. refining existing CRM processes or reengineering CRM

13.6. Evaluate the workdone

13.7. Prioritize the organziation's requirement

13.8. Select the appropriate CRM software

14. Customer Selection Strategies

14.1. New node

14.1.1. Used for finding the best predictors of binary dependent variable

14.1.2. New node

14.1.3. Decision tree algorithms can be used to iteratively search through the data to find out which predictor best separates the two categories of a binary target variable

14.1.4. Problem with the approach: prone to over-fitting; the model developed may not perform nearly as well on a new or separate dataset

14.2. Logistic Regression

14.2.1. Method of choice when the dependent variable is binary and assumes only two discrete values

14.2.2. By inputting values for the predictor variables for each new customer – the logistic model will yield a predicted probability

14.2.3. Customers with high ‘predicted probabilities’ may be chosen to receive an offer since they seem more likely to respond positively

14.2.4. Linear and Logistic Regressions

15. Business Value of CRM

15.1. Cost avoidance

15.2. Increased revenue

15.3. Margin increases

15.4. Reduced inventory costs

15.5. Increased customer satisfaction

15.6. Increase in staff productivity

16. Risks of e-CRM

16.1. Taking on more than can be delivered

16.2. Getting over budget & behind schedule

16.3. Poor user adoption

16.4. Expensive maintenance & support

16.5. Isolation

16.6. Garbage in-garbage out

16.7. Failure to measure success

17. marketing Metrics

17.1. Traditional

17.1.1. Market Share

17.1.2. Sales Growth

17.2. Primary Customer-based

17.2.1. Customer Acquisition

17.2.2. Customer Activity

17.3. Popular Customer-based

17.4. Strategic Customer-based

18. Traditional and Customer-Based Marketing Metrics

18.1. Traditional Marketing Metrics

18.1.1. Market share Share of a firm’s sales relative to the sales of all firms – across all customers in the given market Market Share (%) (Equation refer to ppt) Evaluation Information source Numerator Denominator

18.1.2. Sales Growth Compares increase or decrease in sales volume or sales value in a given period to sales volume or value in the previous period Sales growth in period t (%) (equation refer to ppt) Information source Numerator and denominator: from internal records Evaluation Quick indicator of current health of a firm Does not give information on which customers grew or which ones did not

18.2. Primary Customer Based metrics

18.2.1. Acquisition rate Acquisition defined as first purchase or purchasing in the first predefined period Acquisition rate (%) = 100 * Number of prospects acquired / Number of prospects targeted Information source Numerator: From internal records Denominator: Prospect database and/or market research data Evaluation

18.2.2. Acquisition cost Acquisition cost ($) = Acquisition spending ($) / Number of prospects acquired Precision depends on communication channel Direct mail vs. Broadcast Information Source Evaluation: Important metric about cost efficiency of a campaign Difficult to monitor on a customer by customer basis

18.2.3. Average Inter-Purchase Time (AIT) Easy to calculate, useful for industries where customers make frequent purchases Firm intervention might be warranted anytime customers fall considerably below their AIT

18.2.4. Retention rate Retention rate (%) = 100 * Number of customers buying in (t) and in (t-1) / Number of customers buying in (t-1) Defection rate (%) = 1 – Retention rate Retention rate (%) = 1 – Defection rate Lifetime duration = 1 / (1- Retention rate) Retention rate (%) = 1 – (1 / Lifetime duration) Assuming constant retention rates, number of retained customers in any arbitrary period (t+n) = Number of acquired customers in cohort * Retention rate (t+n)

18.2.5. Numerator & denominator: from internal records

18.2.6. Survival rate Measured for cohorts of customers Survival ratet (%) = 100 * Retention ratet * Survival ratet-1 Number of Survivors for period 1 = Survival Rate for Period 1 * number of customers at the beginning

18.2.7. P (Active) Probability of a customer being active in time t P(Active) = T n n is the number of purchases in a given period T is the time of the last purchase Non-contractual case

18.2.8. Projecting Retention Rates Rrt = Rrc * (1 - exp(-rt) ) Rrt is predicted retention rate for a given future period, Rrc is the retention rate ceiling r is the coefficient of retention r = (1/t) * (ln(Rrc) – ln(Rrc – Rrt ))

18.2.9. Lifetime Duration Average Lifetime duration = Customers retainedt * Number of periods / N N = cohort size t = time period Differentiate between complete and incomplete information on customer Complete information - customer’s first and last purchases are assumed to be known Incomplete information- either the time of first purchase, or the time of the last purchase, or both are unknown Customer relationships Contractual (“lost-for-good”): Lifetime duration is time from the start of the relationship until the end of the relationship Noncontractual (“always-a-share”): Whether customer is active at a given point in time One-off purchases

18.2.10. Win-back rate Part of a acquisition process Applicable to both Contractual and non-contractual situations Proportion of the acquired customers in a period who are customers lost in an earlier period Indicates either a successful communication of an important change in the product offering or service or a change in the customer needs

18.3. Popular Customer Based metrics

18.3.1. Share of Category Requirement

18.3.2. Size of Wallet

18.3.3. Share of Wallet

18.3.4. Expected share of wallet

18.4. Strategic Customer Based metrics

18.4.1. Past Customer Value

18.4.2. RFM value (Recency, Frequency and Monetary)

18.4.3. Customer Lifetime Value

18.4.4. Customer Equity