Analytical Engine

Mobile phone operators are facing a rapidly saturating market, thus facing customer retention as the primary challenge.  Mobile operators, like almost all big retail operations, have their own predictive churn models. Moreover, mobile operators have the luxury of having a wider array of variables. The “Know Your Customer” questionnaire and the usage behavior of the mobile phone typically provide access to a few thousand variables.  Modelers frequently add some off-the-shelf segments (from providers such as Claritas or Acxiom) in the mix to make a better sense of and add more predictive power to the model.

But the large number of variables adds at least two complexities.  First, even after performing variable reduction, modelers usually are still left with a high number of components/ factors, a number high enough to make a concise business presentation difficult.  Second, the high number of variables also affects the actionability of model insights.  No single variable affects churn in isolation, so it is hard to determine the independent impact of individual variables in customer churn.

An alternative approach to comprehend and act on the model recommendations will be to bundle the variables in logical groups.  For example, the variables related to time can indicate when a customer is more prone to churn and can be grouped as a ‘when’ group of variables. This ‘when’ group will not necessarily tell us why a customer is more prone to attrition, and would rather tell us when she is more prone to attrition.  Similarly, the variables’ indication of issues faced by a customer (her interactions with call center might be a good source for this) such as handset problem or network issues can be grouped as ‘why’ variable.  The demographic variables that are significant in predicting churn help the modeler to profile the customers likelihood to churn and can be grouped as ‘who’ variables. An integrated view of these three groups is more palatable, and from a modeler’s perspective, it is easier to convey to the business ‘who’ are more likely to churn, ‘why’ they are more likely to churn and ‘when’ they are more likely to churn.

The above approach does not only make the models easier to comprehend, but also increases actionability by providing a structure to the model findings.

Some recent academic literature has shown that companies using the standard Net Present Value (NPV) approach to calculate Customer Lifetime Value (CLV) and allocating their marketing spending might be losing substantial profits.

The standard CLV approach calculates the net present value (NPV) of all the anticipated cash flows coming in (revenues) and going out (expenses) over the projected life time for a given customer. Customers with a positive NPV are aggressively marketed and the ones which do not have a positive NPV are dropped. This is a pretty logical choice at the point of calculation- but only then. The existing conventional structure of calculating CLV is static and has the potential to leave money on the table for the company; because it does not account for the fact the company has the flexibility of abandoning a customer at any future point in time. Flexibility means options and options have a value.  The paper lays down a methodology (based on dynamic programming) to explicitly estimate the value of such options in the CLV calculation.

The authors of the paper cite the example of a specialty catalog company. They took data of 12 years and around 100,000 customers, and the difference in CLV using the traditional versus real options was as much as 20% in some cases. In fact, certain customers who had negative CLV’s turned positive when the value of real options was considered.

Retailers, communication companies and consumer finance companies who frequently use CLV as a decision making metric, are strongly recommended to read this paper.

Firing customers is not something companies prefer to do for profitability. Fierce wireless reports that Sprint recently fired some customers because of their frequent calls to customer care.

Ever wonder how many calls to care can actually get one fired from the wireless company, now that the precedent has been set? We did some back of the envelope analysis to understand it better.

Assuming that the fired customer paid an average bill of $60 per month and that Sprint has 30% margin and average cost per call of $4, it will take 5 calls in a month to make one a negative value customer. However the trick of all lifetime value calculations is to estimate future value of customers because you can be unprofitable in one month but go on to upgrade to a highest plan down the road. Since there is no reliable way to predict it, we will be conservative giving the benefit of the doubt to the customer. So to get fired, the customers must be making way more than 5 calls a month (probably more than 10-15) and that too for a few consecutive months; so that Sprint had no hope of making a profit in future from these customers.

While it is certainly a sound financial decision, it will be useful to determine whether these customers had anything in common (type of problem they were calling for, expectation setting at sales channel etc.) which led them to call more often.

Photo Credit: Pappalicious

In any customer facing business, the channels used to reach out to your customer as well as the channels your customer use to consume your products and services have a strong correlation with overall profitability. 

From the B2C perspective, advertisements, stores, online, mailers, charity, etc. are different ways of reminding customers about your offerings. From a C2B perspective, again, stores, online, home delivery orders, etc. are ways in which consumers consume your products. 

However, the key here is to understand which customer prefers which B2C and which C2B channel. Right-Targeting Customers is as important as Targeting Right Customers! Someone who spends 14 hours a day in front of his computer and has no time to go to a store 5 days a week may prefer a home delivery channel. On the other hand, a student in a college is only interested in the bargain channel, irrespective of the inconveniences, maybe. This report also mentions how retailers need to manage their investments across channel against the scale and timing of their expected return. I would go beyond Ron’s Right Channeling [read post] to include all aspects of targeting under the concept of Right-Targeting. Having said that, I agree that today’s world is about multi-channel customers, and the need of the hour is to optimize channel returns, rather than just channel re-alignment/phase-out.�

Targeting Right Customers- Its equally important is to understand how channel profitability gets affected if you are not targeting the right customer. For instance, Wal-Mart, even with its Everyday Low Prices (EDLP), must be making money on some products/ some SKUs and these would drive the overall positive profitability. However, what if your customers are not buying your profitable SKUs? What if the draw that brings them there is not luring them to buy more? What if there is no up sell/cross-sell/bundling that happens there? And suddenly the business realizes that channel profitability is coming under immense pressure! (One of our earlier posts tries to answer the question of market basket analysis and product bundling). 

Re-Targeting - And last but not the least– taking the difficult decision of phasing a channel out. If it’s not generating any returns (directly or indirectly), the business needs to reconsider the cost of the channel. But what about retaining those customers who are loyal to the channel and have helped you get some mileage out of it? They need to be Re-targeted with a new offering/communication. Traditional retargeting refers to targeting customers who did not convert the last time, though!

This report (PDF) here talks about issues to be kept in mind when moving customers from one channel to another. I am sure Kevin would want to talk about Multi-channel customers being better than single channel customers, and without refuting his argument I would classify my argument as being restricted to customers that are being phased out of a channel. This example demonstrates one case where the retailer has effectively leveraged multiple channels over a period of time by effective use of catalogs and internet channel. 

In Summary

• Right Targeting – Know your customer. Cater to their needs through their preferred channel. It increases the share of wallet, as well as longevity of the customer
• Targeting Right – Pick your customers for each channel-product combination well. Know what and how you are selling and who will buy it.
• Re-Targeting – If it’s imperative that you move away from a particular channel, think about retaining your loyal channel customers. A little caring goes a long way in creating customer loyalty.

The qualifying argument behind all this is that if you are thinking about channel optimization, it needs to be a more concerted discussion based on your strategy/business objective, your data, your needs and your concerns. What we know for sure is that strong data analytics is known to help not only Targeting Right (which is the most common application), but Right Targeting, as well as Re-Targeting.

A discussion I had with a group of analysts suggested, based on rigorous data analytics, that rewards points and redemptions as value added services do help credit card companies retain a few more customers [This image here does prove the retention hypothesis], but its a huge cost on the account level P&L⁽¹⁾. Probably, one of the worst rewards offer is Air travel related redemptions. So, if you are a credit card company and you want to advertise your rewards catalogue, be cautious about advertising Airlines Redemption, is what the group suggeted.

The premise of rewards program offered by credit card companies, traditionally, has been low redemption rates and a lot of people not even redeeming their points ever. Add to it the hassles faced by customers in redeeming, you have defeated the purpose of the program. Why offer a value added service you don’t want people to use (and create negative customer experience)? And, just in case someone ends up using it, why compound the problem with an unfavorable cost structure for such a service?
Assuming that rewards programs are aimed at retaining customers, are the redeemers those customers whom I want to retain through this offering? In other words, am I retaining the right customer?

Lets focus on the second question - Who is the right target for these rewards? Someone who stretches the margin the most (through frequent high cost redemptions, hitting P&L through rewards costs as well as high customer servicing costs), or someone who is generating good revenues through revolving credit, relatively infrequent bulkier redemptions and is sticking on to the credit card because of a certain variety of reward?
Offering rewards as a retention ploy would imply ceilings and floorings to redemptions (by type) for managing customer profitability. For instance, credit card companies impose hypothetical floorings by allowing air ticket redemptions only if someone is utilizing at least 20,000 points or more.

• Another strategy for rewards programs could be aimed at identifying customers with a lower propensity to redeem while maintaining a higher retention probability given rewards utilization.
• However, the best strategy in such cases would be to build multi-stage rewards and retention model that predict point balance utilization, preferred category of redemption, retention driven by rewards usage and most importantly, P&L sensitivity models for rewards usage. This requires deep analytics expertise and a thorough understanding of credit cards’ rewards program.
• Additionally, this report coming through Celent highlights how rewards disposition is likely to change going forward and move towards blended rewards and cash-backs and the economics are likely to become more favorable by leveraging low cost internet, mobile advertisement channels.

⁽¹⁾For P&L, however – lets look at this example⁽²⁾

Include the various maintenance cost items, program costs, and servicing costs on top of the air ticket cost and you probably end up with a non-existent or a very “thin” margin!
⁽²⁾ Under the assumption of zero revolving credit and other sources of revenue that credit cards’ companies generate. While revolving credit is the biggest source of money for credit card companies, the attempt here is to show that at the base level, the credit product is becoming unprofitable because of a faulty rewards program
⁽³⁾ Optimistic, my friends tell me!

Jay recently posted a comment asking us to elaborate on our churn heat map posting. The best way to explain is to show an actual churn heat map which we created for a client.

• Height of each row represents % of all activations
• Each row split into three parts –returns, voluntary churn, and involuntary churn, from left to right, with the % of total churn in that row determining width
• Particularly high or low levels of each type of churn are indicated by color, providing a “heat map” for where the largest churn issues reside

Depending upon your organization, you can choose what segments/sub-segments and products to use to create the heat map

For an executive this churn visual gives a starting point for generating hypotheses and serves as a useful reporting tool to see the efficacy of churn reduction activities

Is it always a good idea to push more services to customers?

Our analysis shows the answer to be a very big ‘No’.

Recent analysis of mobile data services at a major US wireless operator proved this clearly. For the high- risk credit classes, the high data charges from mobile content drove the users to stop paying their bills, leading to a rise in involuntary churn of customers.

As the total data charges in the first two bills increase, there is a huge jump in the involuntary churn rate especially after $15 mark. Almost half of the customers with $30 and up in their first two bills are charging-off.

(click on the thumbnail to view graph)

So what’s the solution? In our opinion it is two-fold:

1. Rigorous analytics to identify the risky segments
2. Proactive customer retention strategies like providing SMS/email alerts to inform customers about excessive usage

Not only will this strategy decrease the involuntary churn rates, it will also act as a customer satisfaction tool and help customers make more informed decisions.

Predicting the customers who are high risk is of little use, if you do not know what you can do to keep them.

This and other such interesting insights from the recently published whitepaper on customer churn. Here is a summary:

Having worked with dozens of clients to successfully improve their profitability and increase the lifetime value of their customers, Diamond has identified three core churn management missteps:

1. An over-reliance on saves queues and churn models as the primary (or often only) tools to address churn.
2. The absence of a clear understanding and prioritization of addressable churn drivers, and thus sub-optimal alternatives for allocating remediation resources.
3. A departmental focus on incomplete performance targets (i.e. the Acquisition group’s focus on gross adds; Marketing’s focus on gross revenue; and the Retention group’s emphasis on churn or save rates), at the expense of overall profitability.

This paper outlines current challenges in churn management, details best practices for developing a clear understanding of true churn drivers and priorities, and presents the organizational challenges that must be overcome to eliminate the root causes of churn and thereby increase profitability.

It’s an interesting read which talks about the primary research techniques, process analysis along with data analytics to get to the root of the problem.

As markets become more mature, competitive and undifferentiated, many companies find themselves struggling with high customer attrition (churn). Companies try to cover up by acquiring new customers by means of profit-eroding attractive acquisition offers, which in many cases, encourages churn behavior as competitors adopt similar acquisition strategies. In order to address the root cause of customer attrition, it is vital for companies to identify the “dissatisfiers”. The “dissatisfiers” may result from several factors (dimensions); for e.g., poor products, service, lack of trust, company policies, perception/brand issues or dissatisfied segments and in most cases - a combination thereof.

Since the “dissatisfiers” can exist on several dimensions, analysis of historical data can provide invaluable insights. However, the real challenge of churn related data analytics does lie in the execution of the analysis, but rather in the generation of insightful interpretation with a big-picture view of things and actionable remediation plan. Going by the 80-20 rule, companies stand to benefit the most by identifying the “dissatisfiers” that have maximum impact and are most addressable. Due to complex nature of such analysis, a structured and comprehensive approach to data analytics is required.

Diamond has developed a technique, “Churn Heat Map”, which is a useful tool that can allow companies to identify churn drivers in an efficient and reliable way. The tool is used to analyze historical customer attrition data to generate a color coded heat map of churn rate modulated by the severity of churn and volume on a grid of churn drivers and customer segments. The churn drivers and customer segments are chosen from standard attributes in order to address specific needs of a problem. The rules for color coding are also customizable (for e.g. red color may indicate above industry churn for a segment). The “Churn heat map” is a useful in customer attrition remediation projects in a variety of industries - telecommunications, financial services (credit cards, banking, brokerage etc.), media, and many more industries plagued by churn/attrition problems. Such a tool provides the following benefits:

• Allow rapid hypothesis generation
• Help identify relatively addressable and significant pockets of churn (low hanging fruit)
• Serve as a reporting tool for visually monitoring the efficacy of churn remediation activities
• Leverage experience and knowledge across products, markets, and geographies by constantly enriching the model with additional categorization variables

Several off-the-shelf software such as SAS Cube, SAS Enterprise Guide, SQL Server, SGI MineSet 2.5 can be customized and leveraged to implement such a tool with limited development effort.

A sample application of such a tool when applied to a telecom churn data analytics is shown below:

The above figure shows the logical flow of transformation of historical marketing data into a churn heat map, which can then be readily leveraged to generate hypotheses, which once validated can yield actionable recommendations leading to early wins in a churn remediation initiative of a company.