Understanding Customer Churn
Consider the situation in the picture.The person is trying to fill the bucket, but there are leakages & hence outflow of water. No matter how much effort is put by the person to fill water either by rapidly filling water in high frequency or in large quantities using larger filling containers, the bucket will not get filled completely. Even if by any means man achieves to fill the bucket completely, it will be for seconds since water outflow won't let the filled bucket to be sustainable. Now replace man with company, bucket full with water as target customer base, water inflow be incoming new customers & leakages as consumer attrition, no matter how much effort is put in bringing new customers, customer attrition will never let the company reach its full potential
Churn rate is defined as percentage of lost customers or revenue in a certain time period
It is of two types.
Customer Churn Rate=(No. Of customers churned in a period/No. Of customers at the beginning of period)*100
e.g - For the telecommunication industry a customer has many options to switch to other alternatives. Suppose a company has seen 1 out of 20 customers cancelling the subscription to prepaid recharge option & switching to other players last year, then the customer churn rate of the company is 5% per annum.
Revenue Churn rate(also known as MRR churn rate/recurring revenue)=(Revenue churned in a period/Revenue at the beginning of period)*100
Revenue churn rate measures rate at which company loses revenue due to loss of customers or downgraded subscriptions(i.e subscribing to cheaper services/products offerings by the same company)
It is useful for a company to compare churn rate with other competitors & see their performance in retaining customers compared to other companies in the same industry.It is important for a company to have low churn rate, because high churn rate implies high attrition, hence company with high churn rate needs to spend money to bring new customers on board just to maintain the vacuum created by customers leaving the company. Hence, it is important to understand the churn rate because it impacts profitability & growth for a company.
Contractual churn: Scenario in which customers decide not to carry with their expired contracts with companies,i.e not to renew services from existing service providing company.
e.g-A customer may have subscribed to a OTT platform for 3 months, after 3 months the customer may choose not to renew the subscription for instance due to lack of time to use OTT platform, hence it comes under category of contractual churn
Non contractual churn: Cases where customers leave potential purchase without completing the transaction. This is highly likely in e-commerce websites or retail stores.
For example, a customer may put the product in cart & initiate the payment, but at last moment he might not continue with the payment for reasons like low account balance, skepticism about product etc. and this comes under non contractual churn
Voluntary churn: It is the scenario where customers cancel their existing contract with the company.
e.g-A customer who has subscribed for 30 days of internet prepaid services of a company may choose to terminate the contract due to poor network, customer service or other factors. It is the most harmful form of customer churn for any company.
Involuntary churn: This is the case where a customer willing to continue service offering from the company has to discontinue due to failure of payment for faults on the company’s side like server errors, disabling of payment update system etc.
e.g-A customer who has defaulted on his credit card payment may not be able to continue his subscription despite his willingness to continue because he can’t afford to pay. It is the least harmful type of churn for any company.
Customer churn types may be different but understanding the type helps to know the underlying reason for churn. Cost of acquisition of a new customer is always higher than the retention cost of an old customer hence, it is important for an organization to understand the underlying cause of churn so that it can work on causes which are under its control to rescue customer attrition.
It is a technique to measure customer loss rate, understand if a company/product/service is losing customers & if it is losing, reasons for customers quitting.It also helps pinpoint customer segment as well as timing of their attrition using predictive analysis
The graph above shows the difference in total number of customers of companies with different churn rates over a period of time. As we can see a difference of just 2.5% translates into 4 times more customers for the company having lower churn rate. This is snapshot of 80 months data only, extrapolating this over a longer period will create gap exponentially between companies having different churn rates. Hence, it is important to minimise churn rate & churn analytics can help understand underlying causes of churn and will predict in advance the churn of customer helping companies to plan & take action in advance to minimise churn rate.
This is the snapshot of one of the dashboards of customer churn. As seen the information provided by it are
Customer status in terms of customer who are active, inactive, lost or new to the company
Churn risk on the basis of income-Risk % of churn of customers on the basis of income can be also easily identified.Company will need to focus only on the specific income groups to prevent churn saving valuable time as well as resources.
Churn risk on the basis of spending as well as time frame-Bottom left part illustrates risk profile of customer churn along with timeline. The segmenting or segregation of customer on the basis of spending groups alongside
Individual customer churn risk %-As seen in the bottom right part for each individual customer churn risk % is mentioned alongside customer details like gender,spending. It can help company identify customers at high churn risk % and if necessary can create customised offering for them
Let’s look at another dashboard to understand the benefits of churn analytics. Summary of insights a company will be getting from this dashboard created from churn analysis & few of the benefits for the company can be summarised as follows
Insights |
Benefits |
|---|---|
Churn rate |
Understanding of performance w.r.t industry benchmark & nearest competitors |
Churn rate by gender |
Insights on requirement of redesigning, pricing or other requirement for different genders |
Churn rate by age group |
Ease of identifying age groups where churn rate is high & getting the research team to do focussed study on specific age groups. Customised products can be designed for certain age groups or product offering can be scrapped for specific age groups depending upon the |
Product category wise churn rate |
Very easy to identify the popularity of different products & ones with high churn rate might be redesigned, repriced, targeted to specific customer segment only as per analysis |
Credit score wise churn rate |
Customer credit card information is easy, faster & cheaper to obtain & is a good indicator of expenditure pattern as well income worth of a customer. It will not only help identify credit score card vulnerable to high churn rate but will also identify high revenue potential segment |
Churn rate by country |
Churn rate may vary country wise due to differences in economic, cultural, social factors to name a few. Churn analytics can help companies aggregate countries into categories appropriate for different products on the basis of churn rate |
Churn rate in card holding vs non holding categories |
Many times churn rate may be due to external factors. Information of this type will help segregate factors in the control from those which are beyond control of company |
Customer churn rate in different salary categories |
Design customised offerings for different salary group |
The above two illustrations are just the tip of the iceberg. Churn analytics can help companies transform their business in very broad categories by getting deep insights and align their strategies to leverage the information obtained from the analysis.
In summary broad benefits of churn analysis are:
High customer satisfaction- By gaining insights on churn rate across various customer categories, products can be redesigned, pricing can be modified as per customer category & will lead to higher customer satisfaction
Lower expenditure on marketing- Customer acquisition cost is always higher than retaining old customers. By predicting churn in advance the focus can be shifted to reduce churn and hence extra expenditure on acquiring new customers to fill the loss of lost customers can be reduced
Low customer acquisition cost- Since analysis will give information about churn of various categories hence, product offering will be easy to design as per customer segment. It will be easier to attract customers to offerings and extra expenditure on promotional activities will be reduced
Opportunities for cross selling & up selling- Cross selling is practice of selling additional products to existing customers.Up selling is practice of selling high value products to existing customers. Since, churn analysis gives churn rate of different categories on the basis of age, income, credit card score, country etc., hence this can be leveraged to both cross sell & up sell products who have low churn rate in different customer segments to new segments created from overlapping features of customers(country, age, gender, salary etc.)
Prevention of loss of market share to competitions- Identifying products with high churn rate and mapping with the customer profile where churn rate is high can be utilised to plan better offerings & hence lost market share to competitors due to high churn can be mitigated
Industry |
Application |
|---|---|
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Financial Services |
Detect flawed product/service offerings, insights to design products for different segments, predict in advance customer planning to switch |
FMCG |
Identify products with high churn rate, increase customer loyalty by mapping products to correct customer category |
Consumer Tech |
Identify app churn rate, reasons for churn rate |
Healthcare |
Minimise loss of market share to competitors by identifying reasons of high churn rate, use insights from analysis for expansion in new territory |
Edutech |
Identify reasons underlying churn rate across products & customer categories. Utilise the information to mitigate market share loss to competitors, product design, high satisfaction level of customers & expansion |
Manufacturing |
Measure churn rate of buyers & identify seasons of high churn rate. Segregate internal & external factors responsible for high churn rate .Also, can be utilised to predict suppliers planning to switch to competitors |
Telecom |
Predict customers planning to switch to other carriers |
Energy |
Identify revenue risk across seasons, festivals |
e-commerce |
Enhance returnability prediction capability, predict customer likeability in advance, identify customers planning to think to switch to competitors & evaluate revenue risk across festivals |
Retail |
Identify factors responsible for high churn rate, estimate customers planning to switch to competitors & mitigate this risk of loss to competitors |
Travel |
Predict churn rate of website/ app visitors. Identify causes of churn rate & design package or products to minimise churn rate |
Company: A German bank(name hidden for confidentiality of client),
Background: Financial services sector has increasingly become saturated with a wide array of alternatives available to customers. It has created 2 major challenges
i)Difficulty for banks of differentiating their services from competitors
ii)High churn rate
Problem Statement: Due to the sophistication of the datasets, the client, a leading German banking company, found it difficult to determine the reason behind customer churn. The bank was also looking for acceptable models of churn analysis to help them recognise the preference of various groups of customers as well as identify the associated probability of churn. The client experienced difficulties spanning two main areas, including: i) Inability to improve impact of marketing campaigns ii)Difficulty in analyzing patterns & build effective programs to reduce churn
In order to help them compare scenarios, predict threats, forecast capital, balance risks against projected returns and work to meet regulatory requirements, the German banking company turned to churn analytics as a tool to solve the issues.
Churn analytics helped bank to
i) Identify churn reduction opportunities
ii) Align tactical & strategic decision making for achieving business goals
The final results after analysis & implementation were:
1) Reduction ofcustomer churn from 10% to 3%
2) Improved customer retention rate by 85%
3) Overall annual ROI improved by 70%
Churn analysis is not a routine job in which analysts or companies do it randomly at any time in the day. Usually it is done in two scenarios:
1) Red flag- For instance, suppose a company is facing continuous decline of consumers subscribed to its product/service offering. It will raise a red flag to dig deeper into the reasons underlying this trend.
2) Engagement activity- The reason for doing this analysis may not be bad always. Sometimes companies do it to see the impact of a new initiative on relationships with customers. For example, a company might introduce new engagement activity in the form of thank you mails on birthdays, post purchase. The company might be interested in knowing its impact on customer attrition & retort to churn analysis and evaluate churna before and after initiation of engagement activity
Note that little churn is common for any company.Generally acceptable, churn rate between 5-7% is a healthy churn rate,i.e not a cause of concern. But the rate may be high & low depending on the industry type
Trouble arises when churn rate is higher than customer acquisition(new customers subscribing).
Hence, the following factors indicate the company is having a churn problem & there is requirement of churn analysis.
Following are the indicators of appropriate time for doing churn analysis
Churn rate is higher than new customers- This situation leads to reduction in customer base and is exacerbated by absence of upselling(sales technique where an existing customer is sold more expensive products, add ons etc to generate more revenue) to existing customers & hence taking a direct hit on revenue of the company.
Shrinking LTV (lifetime value) of customer- Lifetime value of a customer is the overall business worth of a customer to the company over their period of relationship. Constant churning reduces LTV & hence declining LTV requires churn analysis to decipher the reasons
Churn rate above 10%- As mentioned earlier 5-7% is considered average churn rate, and high or low churn rate value varies across the industry.But double digit churn rate is high for any industry and implies something is wrong with either product, sales executive, customer complaint department, post purchase service to name a few and hence makes it difficult for any company to grow in long run.
More downgrades than upgrades- If company is offering add on features and different plans but customers are downgrading i.e subscribing to cheaper products then it will take toll on revenue & underlying cause for downgrading trend has to be identified
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Data Category |
Data Type |
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Customer information |
Geographical location.e.g-zip code, coordinates, city, locality name etc. |
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Income category |
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Gender |
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Occupation |
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Household Size |
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Age |
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Marital Status |
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Products Usage |
Product type e.g- Category of product ranging from least expensive to most expensive, multi user or single user etc. |
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Variety of products- Different types of products subscribed by the customer, |
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Frequency of usage-How many times a customer uses the product in a day/week/month.Time frame can be decided on the basis of industry standard in which company is operating |
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Duration of product usage over a period of time. Period of time will again depend upon the industry standard in which company operates |
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Product preferences, i.e the certain characteristics customer is looking into product or services |
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Purchase History |
Frequency of purchase/subscription |
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Frequency of purchase/subscription |
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Time of purchase on day/season of purchase |
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Value of purchase |
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Payment methods |
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Complaints |
Frequency of complaints |
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Topic of complaints, i.e product/service feature, behaviour of service representative, payment failure etc. |
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Mode of complaint-Phone call, email etc. |
Note:The list above is an exhaustive list of data required but not a complete list. Data requirements will vary depending on the company, industry, macro economic factors & discretion of the analyst.
Missing values in dataset- A data set may have missing values because of customer not answering every question,system or staff making error in collecting data or maybe the variable is not relevant for customer
It has further 2 categories
i) Missing values across a single variable-This is the situation where there are missing values across data set of only one variable. It can be handled either by coding the missing values with numbers during analysis or deleting the missing value data sets or through multiple imputation
ii) Missing values across multiple data sets - The best approach to handle it is to do multiple imputation.
Outliers- Outliers can be identified using descriptive analysis and the reason underlying outliers has to be identified. For example there might be a sudden spike in discontinuation of subscription with underlying cause discount offers from competitors. Identifying the underlying cause will thus help company focus on its pricing strategy, Outliers can be identified by scatter plots, line graphs depending upon the data type. It can also be identified using statistical methods by standard deviation or Turkey's method(multiple comparison test to find means that are significantly different from each other).
Univariate analysis(analysis of only one variable)
i)Sorting Data:
ii)Heights of 6 people are arranged in descending order & clearly 2 is an outlier Boxplot:
Boxplot shows data distribution by showing quartile ranges.The * mark will help identify the outlier
iii)Bar Graph:
iv)Line Graph:
Outlier would be easily identifiable since it would be far away from the mean value
Bivariate analysis(used to find relationship between 2 variables):
i)Scatter plot analysis:Scatter plot is a type of plot or mathematical diagram to determine the relationship or patterns between two variables by plotting individual pieces of data in the form of data for two variables
Here the relationship between output & input is being analysed. For the point marked under the circle, output is high & simultaneously input is low compared to other data points and hence it is an outlier.
Multivariate Analysis(It is analysis of 3 or more variables):
i)Tukey's test: This test compares the differences between means of values rather than comparing pairwise values(done previously with casewise significance chosen)
-Outliers due to data entry error can be identified by cross verifying the outlier original value by either interviewing customer or checking the original data sheet & it should be deleted or corrected by going to original source of data to get the correct value
-Sampling error in the form of selecting not a sample representative of the population can also cause outliers & hence these outliers should be deleted from the data to be analysed.e.g -Selecting people of age groups between 20-30 for study from a population where age vary from 0-80 years would lead to sampling error
-Outliers due to natural variation are not unusual & hence they need not be modified or deleted from the dataset.e.g- Consumer demand may become 10 times during a particular time period due to seasonality, festivals.So, these outliers don’t need to be removed because they are naturally occuring
There are multiple techniques to do churn analysis among which popular ones are:
1) Logistic regression-It is the most popular technique, due to its reliability and quick outcome.It gives binary dependent variables as output & independent variables can be multiple.e.g-Logistic regression to check whether a customer will inscribe or not in next 3 months with X1,X2,X3 as independent variable which will include customer demographics data, transaction data, complaint issues,
INPUT------OUTPUT
X1
X2 --> Logistic regression--> 1 or 0
where,1-customer will unsubscribe
0-customer won’t unsubscribe
Note-For output variables which are not binary,they can be transformed into categorical or ordinal categories
Categorical variables- These are variables that have categories among themselves but no inherent ordering between them.For example, population can be categorised into various age groups say 0-20 years,21-40 years, 41-60 years, 61-80 years for analysis of consumption of internet data per day. As we can see, the categorical variable of age group is created by segregating the population into age groups & there is no inherent ranking in the age group.
Ordinal Variables- Similar to a categorical variable is an ordinal variable.The distinction between the two is that the categories have a simple ordering.For example, suppose you have an economic status variable with three categories.Besides being able to classify individuals into these three categories, the categories may be ordered as low, medium and strong.
2) Cox regression- Cox regression (or proportional hazards regression) is a way of investigating the effects of various variables when a single occurrence takes place.The aim of the model is to assess the influence of several factors on survival at the same time.In other words, it enables us to analyse how defined variables affect the rate of occurrence of a specific event (e.g., illness, death) at a specific point in time.This amount is usually referred to as the danger intensity.The aim of the model is to assess the influence of several factors on survival at the same time. In other words, it enables us to analyse how defined variables affect the rate of occurrence of a specific event (e.g., illness, death) at a specific point in time. This amount is usually referred to as the danger intensity. In the literature on survival-analysis, predictor variables (or factors) are typically called covariates.In short, it is possible to view the hazard feature as the chance of dying at t-time.
For churn analysis, the churn will be hazard feature & the factors included in the hypothesis will be covariates.
Both methods are extensively used & very reliable.Selection of method depends upon data type & output requirement.
Churn Analytics is the solution for identifying reasons of customer attrition/churn.It can pinpoint the underlying factors behind the churn & hence will provide the decision makers a direction to devise the strategy for reducing churn
Churn is one of the major reasons behind lowered profit henceforth low ROI of the company. The analysis will hence improve profitability alongside customer satisfaction, lowered customer acquisition costs.This can be applied across a broad range of industries in an environment where increased competition has reduced the perceived differences of product offerings.
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