Surely you know someone who was in a relationship that seemed perfect and they thought it would last a lifetime with that person, but overnight, they were left without knowing why. This feeling of confusion and loss is the same that many companies experience when suddenly their customers stop buying their products and services, and they don't understand why they've decided to abandon them.
Just like in a romantic relationship, when a customer decides to leave a company, it means something isn't working as it should, and it's important to identify what's failing in order to fix it.
On the other hand, companies that use predictive churn models are able to identify these issues in time thanks to data analysis that allows them to detect clues left by the customer indicating that something isn't right and they're likely to leave. This gives them the ability to react and take steps to effectively retain and win back their customers (similar to what happens in a relationship when you detect things aren't going well and you activate alarms to try to win back the other person before it's too late).
Maintaining happy and committed customers requires effort and dedication. Therefore, understanding churn is essential. But...
What exactly is churn rate?
It's a metric that measures the rate of customer cancellation or loss in a business. It's calculated by dividing the number of customers who cancel or stop using a company's services by the total number of active customers within a specific period of time.
A customer gained is not a customer conquered. According to HubSpot, the conversion rate of an existing user is around 50-70%, whereas the conversion rate of a potential customer is approximately 5-20%. So, what do you think is more important, retaining or acquiring?
In this highly competitive market where profit margins are tight, it's more crucial to maintain a customer base that protects your revenues and derived margins. Therefore, keeping your churn rate in check is not just a way to improve your business profitability, but a matter of survival.
Unfortunately, many businesses make the mistake of forgetting the last stage of the funnel: after investing significant effort in attracting, classifying, and nurturing leads to convert them into sales, they forget about their acquired customers. As we've seen, keeping your customers happy is not just as important as, but even more important than, the attraction and sales process.
How to combat an uncontrolled churn rate?
Clearly, a customer being satisfied with their first or initial purchases influences their loyalty, but not everything lasts forever, so if that relationship isn't nurtured, there's a possibility they might go to the competition. At this stage, executing a strong strategy of loyalty and retention is crucial.
This process involves implementing actions that keep your customers happy and loyal to your brand, encouraging them to continue buying your products or services due to the positive experiences and memories they have with you.
Loyalty and retention go beyond the product itself or offering the best price; it's about the consumer's experience with that brand, the positive relationship that develops between them, how they feel, and what that brand means in their life.
Let's take Apple as an example: you don't buy their products solely because they're innovative, intuitive, attractive, and of high quality. Apple's brand strategy is an experience in itself; it makes you feel like you belong to a tribe or social status and become a true fan. The sense of belonging to the Apple world is so powerful that it's hard to imagine a customer "abandoning" their family.
Predictive churn models, one of the jewels in the crown
Within the universe of predictive analytics, there are numerous models based on Artificial Intelligence that help organizations take a step further in their Data Journey and effectively solve their business problems.
The most well-known (and powerful) predictive models are regression and classification:
Regression models allow us to predict a value. For example, what estimated benefit we will obtain from a particular customer (or segment) in the coming months or help us estimate sales forecasts.
Classification models, on the other hand, allow us to predict membership in a class. For example, classifying which of our customers are more likely to make a purchase, abandon us, or commit fraud.
Among these, we find the predictive churn model: one that provides information on which customers are more likely to abandon you. How does it work? This model combines a series of variables with historical data from your customers along with current data. The results are binary: we obtain a yes or no (in the form of 0 and 1) based on their probability of abandonment.
Like all predictive models, it will be essential to retrain it with new data over time to maintain reliability and avoid becoming outdated.
Although the churn model itself is valuable, at Keyrus we work by combining different use cases that help us create the 360º view of the customer sought and desired by all companies, such as propensity to purchase or shopping cart analysis, among others.
These types of models to predict propensity to churn provide benefits such as:
Activating more effective marketing actions by knowing which group of customers is likely to stop buying from you.
Increasing Customer Lifetime Value (CLTV), which translates into reduced Customer Acquisition Cost (CAC) and greater profitability by retaining those customers longer.
Enhancing your company's branding by having more loyal customers and even transforming them naturally into ambassadors for your brand.
Knowing your customers more and better, which will result in iterating the strategy towards becoming more customer-centric.
Making more strategic decisions to optimize processes and campaigns.
In conclusion, knowing and controlling your business's churn rate is essential if you want to ensure profitability and sustainability in the short and long term. As we have seen, predictive churn models can help you identify these issues in time and take effective measures to retain and foster customer loyalty. Stay tuned as we will soon discuss how the potential of these predictive models can be elevated by combining them with other impactful use cases for our clients. Don't miss out!