Customer retention measurement.

I. Usually is complicated to find the optimal commercial actions and the idealexperiment to validate it. Out of the box thinking is critical to strike the issue!

II. The experiment design have to be SMART (Specific, Measurable, Achievable,Relevant & Time bound) in order to be generalizable and applicable to the populationof interest.

III. The are a lot of problems that we most to overcome: spurious relationships,correlation in observables, among others. For the res|ults to be valid, theexperiments must be conducted in a statistically rigorous fashion!

Two factors are the most outstanding in customer retention in telecomservice providers:

I. Intense marketing activities.Launch new tariffs, handsets and promotions on a continuous basis to lureend-users to their own service offering.Intense competence between operators

II. Low switch barriersSIM cards are mostly freePrepaid customers usually do not have to provide much informationCountries with Mobile Number Portability

Uncertainty regarding how consumer’s behavior would beUncertainty regarding how consumer’s behavior would be

I. The customer’s future churn behavior in absence of retention action.

II. The value of the customer to the firm

III. The probability that the customer, if targeted, will respond positively to theretention action and therefore no defect

IV. The cost of the retention action.

Economic value

An increase in retention rate of just 1% may result in substantial profitincreases

An increase in retention rate of just 1% may result in substantial profitincreases

A financial services company serves 1MM customers and the average defectionrate is 7% and the average net contribution per customer (per year) amounts to50 Euro (Van den Poel et al, 2003)

Real life situation example (1/2)

Making use of churn behavior insights may result in higher retention rates, andmore specifically in higher profits when offering the APPROPRIATE ACTIONS

Making use of churn behavior insights may result in higher retention rates, andmore specifically in higher profits when offering the APPROPRIATE ACTIONS

Real life situation example (2/2)

I. Only one third of the ideas tested on the Experimentation Platform at Microsoftimproved the metric(s) they were designed to improve (Kohavi, et al, 2009).

II. In Google, only about 10 percent of controlled experiments lead to businesschange

III. Avinash Kaushik wrote in his Experimentation and Testing primer (Kaushik 2006)that “80% of the time you/we are wrong about what a customer wants”

IV. Mike Moran (Moran 2007, 240) wrote that Netflix considers 90% of what they tryto be wrong.

V. “I’ve been doing this for 5 years, and I can only "guess" the outcome of a test about33% of the time!” (Moran 2008).

Is there a magic recipe for appropriatecommercial actions?

In many domains most ideas fail to improve key metricsIn many domains most ideas fail to improve key metrics

Capital One Bank has become the 5th largest provider of credit cards in theUnited States based on the most aggressive tester model of the world

“We have the ability to turn a business into a scientific laboratory where everydecision about product design, marketing, channels of communication, credit lines,

customer selection, collection policies and cross-selling decisions could besubjected to systematic testing using thousands of experiments”

Rich Fairbank, CEO & Cofounder

Test and learn mindset

I. It’s easier to draw the right conclusions using data generated throughexperiments than by studying historical transactions

II. The test and learn approach consist in take one action with one group ofcustomers, take a different action (or often not action at all) with a control group, andthen compare the results

III. The action only would be applied to the entire population of customers if it is asuccess

IV. The outcomes are simple to analyze, the data are easily interpreted, andCAUSALITY it is easily clear

Key ideas of test and learn mindset

This approach is based on the IDEALEXPERIMENT, although is not always possible to

achieved

This approach is based on the IDEALEXPERIMENT, although is not always possible to

achieved

A/A, A/B, A/Bn or A/A/B, A/A/B/B Testing?This are controlled experiments used to measures changesin web pages (Same idea of test and learn mindset )I. A/A test (Null Test or pilot test). Assigning users to two

groups, but expose them to exactly the same treatment.This can be used to:

Collect data and assess its variability for powercalculations, and Test the experimentation system

II. A/B and A/Bn test refers to one or multiple treatments(control + 2 or 3 treatments)

The multiple comparison problemIII. A/A/B and A/A/B/B testing refers when one considers a

set of test simultaneously with the same data andtreatment

× When the type I error rate for a single test is alpha =0.05, then make 3 multiple comparisons the actualalpha is .143 (1-(1-0.05)^3), with twenty 0.642(1-(1-0.05)^20) and with fifty .923 (1-(1-0.05)^50)

The more we test for the same thing, the more likely we are to falselyidentify a result as significant when in fact it isn’t!

The more we test for the same thing, the more likely we are to falselyidentify a result as significant when in fact it isn’t!

I. In an ideal experiment the tester separates an independent variable (thepresumed cause) from a dependent variable (the observed effect) whileholding all other potential causes constant, and then manipulates theformer to study changes in the latter

II. The manipulation, followed by careful observation and analysis, yields insightinto the relationships between cause and effect, which ideally can be appliedto and tested in other settings

What is an ideal experiment?

Ideal experiment help to formulate causal question preciselyIdeal experiment help to formulate causal question precisely

Ideal ExperimentLearning from a business experiment is not necessarily as easy as isolatingan independent variable, manipulate it, and observing changes in the dependentvariable

For example: Wawa, an store in the mid-Atlantic United States, wanted tointroduce a flatbread breakfast item that had done well in simple tests But theinitiative was killed before the launch, when a rigorous experiment, completedwith test and control groups followed by regression analysis- showed that the newproduct would likely cannibalize other more profitable items

I. Randomized experiment: An experiment in which units are assigned toreceive the treatment or an alternative condition by a random process.The key success in this type of experiment is to ensure theseparation between the actions taken

II. Quasi experiment: An experiment in which units are not assigned toconditions randomly.

III. Natural experiment: Not really an experiment because the causecannot be manipulated; a study that contrasts a naturally occurringevent with a comparison condition.

Types of experiments

In non-randomized experiments it is necessary to correct for selection biasRandomized experiment is the best approach because provides amethodology to reliable evaluate ideas

In non-randomized experiments it is necessary to correct for selection biasRandomized experiment is the best approach because provides amethodology to reliable evaluate ideas

Types of experiments - Drawbacks

xxx

I. Random Experiments solve the selection/systemic bias, but have other

problems

II. Experiments are often costly and sometimes either unfeasible or unethical

III. We can hope to find a natural or quasi-experiment out there. But none of this

alternatives are not without challenges. For the results to be valid, the

experiments must be conducted in a statistically rigorous fashion

Kohl’s hypothesis:

Opening stores an hour later to reduce operating costs will not lead to asignificant drop in sales

During that testing, the company suffered an initial drop in sale

The analysis showed that the number of customer transactions had remained thesame; the issue was a drop in units per transaction. Eventually, the units pertransaction recovered and total sales returned to previous levels

They didn’t rush to equate correlation with causation. Without fully understandingcausality, companies leave themselves open to making big mistakes

Beware of correlation!

..We can misinterpreting statistical noise as causationUse the correct metrics it is very importantIf something seems rare/amazing triple check

xxx

I. A third variable* may adversely influenced the results

II. This spurious trend can make experiments look like there may be a true differencewhen none actually exists.

III. Before inferring causation researchers should check for equally plausible alternativeexplanations for the phenomenon under study

Confounding variable/ Natural experiments

*Spurious relationship/ Lurking variable/omitted variable

xxx

Common problems in randomized experiments(1/2)

xxx

Randomized experiments in the social sciences in particular suffer from a major

problem:

I. The missing counterfactual: Individuals or firms can usually not be observed

with and without treatment at the same time.

II. Attrition: Random attrition hardly biases the results, but if there are differences

between the rate of attrition in treatment and control groups, the result may be

biased. We can address this concern by imputing the values and

reestimatimating the model.

III. Subjects moved between treatment and control groups.

I. Randomization Bias Can occur if treatment effects are heterogeneous. The

experimental sample may be different from the population of interest because of

randomization. People selecting to take part in the randomized trial may have

different returns compared to the population average

II. "Hawthorne" Effects: People behave differently because they are part of an

experiment. If they operate differently on treatment and control groups they may

introduce biases.

III. Substitution Bias: Control group members may seek substitutes for treatment.

This would bias estimated treatment effects downwards

IV. Selection effects/ Self selection effects. This is not considered a problem in

randomized experiments because randomization renders selection effects

irrelevant. But, if you compare only the customers that accept the promotions with

those on the control group we are faced a issue of self selection bias.

Common problems in randomized experiments(2/2)

The key to success with treatment and control groups in randomizedexperiments is to ensure separation between them so that the actions taken withone group do not spill over to the other (shadow the effects)

I. Usually, retail companies test new promotions by applying it across multiplestores; Nevertheless, varying treatments in such way it may lead to spillovers forcustomers who visit multiple stores. Alternatives: Geographic separation, vary theactions over time, among others

I. Our case: Telefónica and their promotions. We could recommend them toapply promotions in different weeks or we could try to find patterns in the data,another alternative is compare with another country. But trying to avoid thecommon mistake of running experiments that merely adjust currentbusiness actions.

Out of the box ideas

SMART DESING

SPECIFICFocus on what are we

trying to measure.

MEASURABLEIs it possible to measure,

monitor and evaluate partialand final results within the

time frame indicated?ACHIEVABLE

Is it possible to achieve theresults with quality ?

REALISTIC ANDRELEVANT

What is the reason ofthe mesurement?

TIME BOUNDThe time frame of theexperiment, that wouldinclude pre and postintervention analysis

• Does the experiment have a clear purpose?• Have stakeholders made a commitment to abide by the results?• Is the experiment doable?• How can we ensure reliable results?• Have we gotten the most value out of the experiment?

Smart design

Smart design - Specific

What is the causal relationship of interest?/Which experiment could be used to capture thecausal effect?

I. Keep it simple. Look for experiments easy to execute using existing resources andstaff, this is because expose our population of interest to many changes/interventionsat once, <unless it is difficult or expensive to take samples large enough (for example:within-participants design)> might shadow or occult positive results due to correlationbetween the interventions

II. Focus your experiments on settings in which customer respond immediately

III. Establish proof of concept test. Start from the general to the specific

Key note: In literature it is recognized that the place to start improvementsis customer acquisition, not in lifetime customer value. The effects ofexperiments on customer acquisitions can be measured immediately,while the impact on customer lifetime value could take 25 years to assess

Smart design – Measurement (1/2)What is the identification strategy?

If you cannot rely on experiment, it needs to be explicit

The basic elements of experimental research are well-known:

I. Selection of participants and assignment of them to treatment and control conditions,

preferably using a random procedure;

II. Application of the intervention of interest to the treatment group but not to the control group;

III. Monitoring the research situation to ensure that there are no differences between the

treatment and control conditions other than the intervention;

IV. Measurement of selected outcomes for both groups; and statistical analysis to

determine if the groups differ on those dependent variable measures. This also include

the pre-post analysis, environmental analysis (competence), design of the intervention,

subjects eligible to be included in the experiment, out of the box thinking .

V. Measure everything that matters, viewing the results in different context

(segments, channels, etc)

I. Define Key metrics (Define the feedback mechanism to observe how customer respond todifferent treatments)

Behavioral metrics: : what are the changes that we want to measure

Perceptual measures: how customers think they will respond to your actions. Thisspeculative form of feedback is most often obtained via survey, focus groups, conjoint studiesand other traditional forms of influence customers’ behavior rather than just their perceptions.

II. Diagnostics metrics (What are those metrics that help us to confirm our results.)

III. KEY POINTS

Analyze only users who were actually exposed to the changeConsider lower variance metrics as

Boolean metrics which are easy an simple to test (example, retention ratio, conversion rate,etc.)Continuous metrics can be combined with control variables in order to reduce variability.Continuous metrics can be also used and spliced in order to reduce the variance andunderstand through the segmentation the causes(example: revenue spliced by use and probability of churn)

Smart design – Measurement (2/2)

What is the mode of statistical inference?

Have remaining biases been eliminated through statistical analysis or othertechniques?

To ensure that the conclusions about intervention effects drawn from experimental design arecorrect, the design must have three important elements sensitivity, validity and reproducibility

• Sensitivity refers to the likelihood that an effect, if present, will be detected (power, size, Type error I/Type error II)

• Validity refers to the likelihood that what is detected is, in fact, the effect of interest (Internaland external validity)

• Reproducibility refers to the likelihood that any confirmatory discover or effect may not be vanish onreplication

The strength of experimentation is its ability to illuminate causal inference.The weakness of experimentation is doubt about the extent to which that

causal relationship generalizes

The strength of experimentation is its ability to illuminate causal inference.The weakness of experimentation is doubt about the extent to which that

causal relationship generalizes

Smart design - Achievable

xxx

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Smart design – Realistic and relevant

I. In theory, it makes sense to test any part of the business in which variation can

lead to differential results. In practice, however, there are times when a test is

impossible or unnecessary. Some new offerings simply can’t be tested on a

small scale.

II. Not every decision can be made with the scientific rigor of a controlled

experiment. For example, you cannot run a controlled experiment on the

possible acquisition of one company by another.

xxx

Smart design – Time boundDefine the period time of the experiment considering pre and post analysis

depending on the metric of interest. For example, for a specific promotion there

exist a 90/10 rule adoption after 90 days, a non-loyal user has only 10% chance of

become a loyal user.

xxx

Statistical methods (1/2)Simple hypothesis tests.

I. Parametric statistics are used for normally distributed data measured on the intervalor ratio scales:

T-test: compares means of two groups (paired/unpaired tests)

Chi-square test: compares observed frequencies within categories tofrequencies expected by chance

II. Nonparametric statistics are used for data measured on the nominal or ordinalscales that do not meet certain assumptions about population parameters such asabnormal distribution

Bootstrap Hypothesis Test, Wilcoxon rank-sum (test one group against ahypothetical median)

Mann-Whitney (compare two unpaired groups)

Wilcoxon matched pairs (compare two matched groups)

Kruskal-Wallis (three or more unpaired groups)

Friedman (three or more matched groups)

xxx

Statistical methods (2/2)Useful in randomized experiments

I. Regression

II. Quantile regression analysis (need review)

III. Analysis of variance (ANOVA): compares means of three or more groups.Multivariate analysis (e.g. MANOVA): analyzes relationships among three ormore variables

IV. Differences in Differences approach

V. Propensity score matching

I. Logistic and Probit regression: predicts odds of a dichotomous outcome

VI. Survival analysis

xxx

Non – experimental methods

xxx

There are more sophisticated non experimental methods to estimate

program impacts:

I. Regression

II. Matching

III. Instrumental Variables

IV. Regression Discontinuity

V. Differences in differences in combination with methods above.

These methods rely on being able to mimic the counterfactual under certain

assumptions

Impediments to valid results fromexperiments

xxx

The validity of experimental results, i.e. the extent to which results reflect the truth, is

obviously a matter of importance. There are two distinct forms of validity:

I. Internal validity The question being asked is whether the experimental treatmentis actually responsible for changes in the value of the dependent variable or if

confounding factors have been in operation. Since laboratory experiments afford

greater opportunities for controlling extraneous or confounding variables than do

field experiments, internal validity is a bigger problem in the case of the latter.

II. External validity External validity has to do with the extent to which experimentalfindings can be generalized to the population from which the participants inthe experiment were drawn. In other words, the issue is the degree to which the

sample represents the population. Given the naturalistic setting of field experiments,

this category generally provides greater external validity than do those

experiments conducted within a laboratory environment.

Sensitivity

xxx

Sensitivity is the ability to detect a difference between the treatment and thecontrol conditions on some outcome of interest. If the research design has highinternal validity, the difference will represent the effect of the intervention.

We want to know if

I. There is a causal effect

We can incorrectly conclude that there is a cause when they do not (Type I error) andincorrectly conclude that there is not a cause when there is (Type II error)

II. How strong is the effect

We can overestimate or underestimate the magnitude of the effect, as well as thedegree of confidence.

How we can ‘tune’ the experimental design to maximize sensitivity?

Size effect

xxx

It is a quantitative measure of the strength of a phenomenon. Example:

The regression coefficients.

The mean difference

Odds ratio

Relative risk

About 50 to 100 different measures of effect size are known

Standardized effect size measures are typically used when the metrics of variables beingstudied do not have intrinsic meaning (e.g., a score on a personality test on an arbitraryscale), when results from multiple studies are being combined, when some or all of thestudies use different scales, or when it is desired to convey the size of an effect relativeto the variability in the population.

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Difference in Difference

Assume that we have a natural experiment where we have a before and

after comparison for one set of observations (the treatment –T) which is

compared with another set (the control C). Then the treatment effect is

calculated taking the difference in two differences:

Treatment Effect:

Sample size calculation

xxx

I. Statistical power. It is the probability of rejecting the null hypothesis whenit is in fact false and should be rejected. This is an indicator of the likelihoodof the test of actually detect a difference. Low values indicate the incapacity ofthe test of detected a difference even if it really exist. An increment in the samplesize produce an increment in the statistical power

II. The size of the effect. You must decide how large a difference should be. Thisshould be based on understanding the experimental system you are using andthe scientific question you are asking

III. Specify required Type I and Type II errors. Allowable Type I and Type II errorsshould be specified if the power analysis is conducted a priori. Smaller errors willgive more conclusive results but at the expense of requiring larger sample sizesand possibly at great cost. A trade-off must be reached based on the intendeduse of the results

IV. Variability. The variability within groups can increase or decrease the power ofthe test. Greater variability will require a larger sample size to reach a givenstatistical power.

Sensitivity

xxx

Key notes:

I. The more fluctuation there is in the sample data, the less significant the result

II. The smaller the data set, the smaller the significance of the result of the test

III. The smaller the observed difference between the treatment samples, the lesssignificant the result

Increase the power:

CRM at pay-TV company: Using analytical models to reducecustomer attrition by targeted marketing for subscriptionservices. Expert Systems with Applications (2007) (1/2)

The authors develop a churn model in the context of the pay - TV market and testtheir attrition- prevention strategy. They target customers with a high churnprobability (top 30%). Those customer were divided into four groups viasystematic samplingThe three course of actions were:

I. Give free incentives (enhancing the service)II. Organizing special events to pamper customersIII. Obtaining feedback on customer satisfaction

through questionnaires (intentions to purchase)IV. Do nothing (control group)

I. There are two main cost components in a retention campaign: the cost ofcontacting customers and the cost of the incentive provided to customersso that they do not churn

II. SMS as communication channel is used extensively in marketing, in particularfor cross-selling and up-selling. Although it is a very cheap way to approachcustomers, it is not always appropriate for customer retention for tworeasons: first it does not provide any feedback or insight on the reason whycustomers might be thinking of churning; secondly it might provide costlyincentives to customers who are not planning to churn in the first

III. The authors believe that customer retention based on a dialoguestrategy should also be implemented: telcos can collect customer feedback thathelps understand the true nature of churn, and minimize the cost of incentives /compensation. While talking to customers, front-office staff can react flexibly tocustomer needs and show that they do care, which does not necessarily meangiving something away for free

CRM at pay-TV company: Using analytical models to reducecustomer attrition by targeted marketing for subscriptionservices. Expert Systems with Applications (2007) (2/2)

The perils of proactive churn prevention using planrecommendations: Evidence from a field of experimentsJournal of Marketing (2015) (1/5)

I. The authors examine the effectiveness of a retention campaign using a large-scalefield experiment (65.000 customers) in which some customer were offered planrecommendations and some were not (based in their past behavior). They find thatbeing proactive and encouraging customers to switch to cost-minimizing plans can,surprisingly, increase rather than decrease customer churn. The experiment wasconducted over a 6-month period (the intervention was applied at the end of the thirdmonth)

II. Possible explanation for how this campaign increase churn:

Lowering customer inertia (customers start to explore competitive offerings)

Increasing the salience of past usage patterns among potential churners(overspending on current plan /customers only want to pay for what they actually use,the customer over-purchaserd and will want to lower or cancel their subscription)

Uncertainty regarding how consumer’s behavior would beUncertainty regarding how consumer’s behavior would be

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Experimental research:

Pilot study: Investigate the differencesbetween control/intervention groups duringthe three months before the campaign andselect those customers that for theircharacteristic were more likely to accept theoffer.

Campaign’s/churn preventionstrategy/intervention approach: Customersin the treatment condition were contacted viaphone and encouraged to upgrade their plan.To incentivize the customer the companyoffered and additional credit for the followingthree months if their upgrade their plan.

Control group: they were not contacted by the operator and did no receive any monetaryincentive. However, they were free to switch to any of the plans offered by the firm. Neither thecontrol or treatment group receive any targeted promotional activity in the preceding three months

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Statistical Analysis:I. Aggregate effect of the treatment : They measure post-campaign churn behavior by

determining the percentage of customers who left the service, at any time during the threemonths after the campaign.

II. Individual differences in revenue (difference in differences approach): for eachcustomer, they calculate the difference between her average consumption during the threemonths before and the three months after the campaign. They use two metrics of revenue:net and conditional revenue.

Conditional revenue: revenue in periods in which customers are active (i.e. whencustomer churns, the revenue for subsequent months is not accounted for)Net revenue: all periods are considered, i.e. when a customer churns the revenue forconsequent months is set to zero.

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Customer Heterogeneity in Campaign response:

Understanding how customers differ in their response to the campaign is important to uncoveringpotential underlying mechanism for how the campaign impacted customer behavior. This can helpto finding better targets for future campaigns. Here they segments according to some of the goodpredictors of churn (before applied campaign)

I. Customers that have a downward sloping trend in usage (trend: % of change in revenue from the 1st to 3rd term)

II.Monthly individual usage variability (individual coefficient of variation in monthly revenue)

III.Consumers whose usage is above their plan allowance (Overage: average revenue less the allowance of theplan)

The campaign increased revenue forcustomers with lower overage, highervariability and positive trend. So thetreatment impacted customersdepending on their pre-campaignbehavior

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Propensity score analysisTo churnTo accept a promotion (the switching alternative)

Lineal modelRevenue differences

Ratios of interactions vs. percentiles

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Proactive customer education, customer retention, and demandfor technology support: Evidence from a field experimentManufacturing and Service Operation Management (2015) (1/8)

Do service provide efforts to educate customers influence customeroutcomes?

I. Proactive customer education can reduce the number of customers whochurn and have significant economic benefits for the provider

II. Major public cloud infrastructure service provider in 2011

III. 366 out of 2673 accepted the service/treatment (they analyze only thosethat accepted, so to avoid selection bias it is necessary to guarantee thetreatment is not correlated with any customer attribute)

IV. Treatment: the provider offered initial guidance on how to use basicfeatures of the service

V. The provider ran the experiment during october and november 2011 andthey observe customer use of the service until August 2012

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Survival analysis: Quantify the instantaneous risk that the event (attrition) will occur attime t. Applications

Serve as a descriptive approach

The Cox proportional hazards regression model, allows testing for differences insurvival times of two or more groups of interest

Help to predict when a certain behavior is likely to occur, for example help plannersto understand the time-points at what customers are most likely to be receptive tomarketing communication initiatives, and also those beyond which further effort islikely to be ineffective, thereby reducing the amount of wasted marketing effort

Also can be useful in forming a judgment of the value of a customer. If the likelihoodof repurchased in the near future can be established, assumptions can be madeabout the future profitability of a customer’s business

The survival function to calculate the probability that a current customer will still becustomer and generate revenues in the next months and years. (Customer lifetimevalue)

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Pre-intervention

This research employs non –parametrical and semi-parametrical survival analysisand count data models to examine the differences in retention and demand of

technology between 2 customer groups in their tenure with the provider

This research employs non –parametrical and semi-parametrical survival analysisand count data models to examine the differences in retention and demand of

technology between 2 customer groups in their tenure with the provider

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Field experiment

Assumption: The treatment assignment is independent of any unobserved customer or agentattributes that may influence outcomes; However…

The research question: What are the effects of PCE (Proactive customer education) oncustomers’ retention and demand for technology support during the early stage of theircoproduction process?The likelihood of treatment varies over the course of a day, over the days of the week, andover the field experimentCustomers may differ in unobserved ways depending on their time of signup, similarity, thenumber of agents applying the treatment and the fraction of treated customers over timeControlling for that differences is critical to ensure the quality of the results

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I. They start with a simpler linear probability and Probit models that willfacilitate the economic interpretation of our findings

II. Then they examine the effect of the treatment on the likelihood of acustomer surviving up to a certain age

III. They infer that the treatment’s effect on customer survival decayedsince most of the effect occurred during the first week of tenure.

I. They employ non-parametric survival analysis to determine the overall effect

of the treatment on customer retention.

II. For this, we study the rate at which customers churn at time through the

hazard function

III. We use the log-rank and Wilcoxon tests for the equality of hazard functions

between the treated and control customer groups. The latter test places more

weight on earlier failure times which is important for us since in our context the

hazard rate of failure is highest during the early stages of customers’ tenure


We suggest that PCE has this effect because (i) customers will derive more

value using a service they understand better due to the treatment and,

additionally, (ii) the treatment generates a small yet important switching cost

that motivates customers to continue using the service


Decay of treatment effect on retentionDecay of treatment effect on retention


Does Service Bundling Reduce Churn? Journal of Economicsand Management Strategy (2014) (1/3)

I. A firm engages in bundling when it sells two or more separate products in a package for a

single price. In this paper, The authors study bundling of wired telephone, cable

television, and broadband Internet services by cable operators, often called “triple

play”

II. Bundling can play any of two distinct roles—either to attract new users to a firm’s

service, or to prevent existing users from leaving, altering the cost of switching, delaying

the decision to swicth

III. Paper’s key question: Does bundling reduce churn, and if so, how much? This is done

with data between 2007 and 2009

IV. The paper contributes to disentangling bundling’s casual effect from other factors

spuriously correlated with churn, this is done properly identify dynamic effects of bundling

using only repeated cross sections of data. the primary focus of this paper is to determine

whether bundling alters households’ payoffs from switching, and consequently reduces

churn.


The data for this project come from Forrester Research, Inc. Each year since 1997,

Forrester collects thousands of mail surveys of U.S. households on their technology

purchases and preferences. 2007–2009. The data contain a wide range of

demographic information, this demographic information is useful both as controls,

and for identifying comparable subgroups across years

Beyond demographics, the data contain information on the use of telecom

services and their providers. Specifically, we can observe, for each year, whether a

household subscribes to the following services: wired telephone, pay television (cable

or satellite), and broadband internet (cable, DSL, cable overbuilder, fiber, or satellite).

However, this information is limited in some important ways


We tested whether bundling appears to increase switching costs by analyzinghousehold-level choices for telecommunications services that are often packaged in atriple play: wired telephone, pay television (satellite or cable), and broadband Internet.

Xi is a vector of household characteristics that are constant over time. The primarygoal of our empirical analysis is to assess whether β3 > 0. This parameter measuresthe difference in churn rates at time t, between bundlers and non-bundlers (Bit-1 is abinary variable indicating whether the household had a bundle at time t-1, ATit-1 is abinary variable indicating whether the household had all three services at time t-1(bundled or not).

The effect is only "visible" during times of turbulent demand. This analysis highlightsthat bundling helps with customer retention in service industries, and may play animportant role in preserving contracting markets

Conclusions

CONCLUSIONS

Customer retention measurement.

Data & Analytics

Transcript of Customer retention measurement.