Effective Churn Management
"Marketing aims to know and understand the customer so well that the product or service fits them and sells itself."
After all, isn't this what businesses strive to achieve?
Customer churn refers to when a customer ceases their relationship with a business. Many businesses typically treat a customer as churned once a particular amount of time has elapsed since their last interaction with the services.
The full cost of churn includes both lost revenue and the marketing costs of replacing those customers with new ones.
It is a lot cheaper to retain an existing paying customer than to compete with others to acquire new customers. Reducing churn should be a key business goal for every business instead of it being an afterthought.
Imagine if you were indeed able to find out which customers are most likely to churn. If and when you could achieve this, what if you could turn them around, you would be adding to the business's bottom line.
Over the years, I have witnessed many businesses excel at managing customer churn, and others that fail appallingly due to severe lack of oversight and imagination.
In this article, let's focus on the telecom sector, though other businesses could adopt similar methods.
The telecom industry is going through a significant transformation. There are far more competitors in the market then there used to be, all fighting to survive and catch the new prospective customer.
The profit margins are slimming down, costs are not necessarily dropping either, yet many management teams remain complacent.
The two typical methods used to re-attract customers that are falling out of a contract would be to:
- Provide a discount to the existing contract rates
- Offer free devices or additional services at no extra cost
The telecom businesses usually review the competitor landscape and try to offer a better package to work out the new contract plans.
To succeed at retaining customers who would otherwise abandon the business, marketers and retention specialists need to be able to:
- predict in advance which of their existing customers will churn through proactive churn analysis, and
- know which marketing actions will have the most significant retention impact on each individual customer.
We all know that a large proportion of customer churn can be eliminated once the team has this knowledge.
It may sound simple in theory; however, the realities involved with achieving this "proactive retention" goal are incredibly challenging.
Churn prediction modeling techniques attempt to understand the specific customer behaviors and attributes that signal the customer churn's risk and timing. The accuracy of the method used is critical to the success of any proactive retention efforts.
It is important to note that if the marketer is unaware of a customer about to churn than it would be like the blind leading the blind. It is a scenario typically seen in many telecom companies around the world.
We witnessed that in many organizations, executives opted to create special retention-focused offers or incentives inadvertently provided to happy, active customers without being armed with knowledge on customer churn, resulting in reduced revenues for no good reason.
Most of the churn prediction modeling methods crafted rely on quantifying the risk based on static data and metrics. For example, information related to the customer exists in the systems at the present point in time.
These churn prediction models predominantly are based on older statistical and data-mining methods. One of the most common practices is to use a Logistic Regression model. It is a mathematical model that used binary data to estimate the probability of an event occurring or not occurring.
Although the approaches offer some value to the business to help identify a certain percentage of at-risk customers; however, they are relatively inaccurate and end up leaving money on the table.
One thing to remember is that a discount is not always what customers need. They, like all individuals, are looking for value for money. You could run a scenario where you provide extra services and products for a small increase in their monthly plan. You would be pleasantly surprised by the success. However, to achieve this, whereby your retention increases (reducing churn) and your overall ARPU (Average Revenue Per User) increases, you need to think differently. Let data be your friend; let it be your guide.
You would be surprised at the amount of data a telecom company has on a user and the incredible insights that can be derived. If only they were to think differently.
Sample of Data Accessible:
- Customer Records:
- Name;
- Address;
- Products on Contract;
- Bills and Payment Information.
- Customer Credit Scores;
- Live Location Data;
- Customer Services Records;
- Email Campaign Information;
- Marketing Campaign Information;
- Quality of Service from Network;
- Competitor Information;
- Demographic Information;
- Location;
- Social.
- Interest / Behavioral Data;
- And so much more.
The reason for looking at all this data, and more is because churn analysis should not start when the customer's contracts are ending. To truly understand churn, you need to understand the customer. To achieve that, you need to understand the full customer journey.
The customer's journey starts before him/her signing up for the service.
Some of the questions you want to ask are:
- What's the customer's age?
- How long have they been using the services?
- What services do they currently purchase?
- How did they find out about the services on offer?
- What marketing campaign did they click on to find the services?
- How successful was that particular marketing campaign?
- How many leads and conversions did that campaign generate?
- Where was the campaign run?
- What was the cost of acquiring that specific customer?
- What was the competitive landscape at the time the customer signed up for the services?
- What is the location demographic at the service installation site?
- During the customer's contract period, how many infrastructure outages and maintenance affected the customer's service?
The list can go on and on and on...
You may wonder why all the questions or why to stitch the data, for what purpose.
Ever heard the saying, "You don't know, what you don't know!" or surely you must have heard, "Ignorance is bliss!". Well, we have seen it all in multiple companies.
Having access to location data together with financial information, you can:
- Optimize location-based customer acquisition targeting
- Optimize service delivery
- Optimize infrastructure planning
- Calculate risk areas, i.e., customers living in areas prone to delay payments
- Optimize offline and online marketing campaigns
- Building customer profiles and use the information to review retention campaigns
Sounds too far fetched? Not really!
We have used this information and within two quarters, helped organizations drop their customer acquisition costs by over 80%, reduced their call center costs by over 40%, increased month on month retention by over 30%.
Churn Management is a very vast subject area. We can go on and write about how to do it exactly; however, there is never a one size fits all scenario. You need to review each organization, its customers, and products separately.
You also need to ensure you can stitch all the data together from multiple sources and departments.
And, most importantly, know the questions to ask!
The only real limit to churn management apart from lack of resources is the lack of imagination. Don't let traditional methods weigh your organization down. It is the time to seize the opportunity and think radically.