4 ways to reduce customer churn using analytics
Customer churn rate is the percentage of your customers or subscribers who cancel or don’t renew their subscriptions during a given time.
A higher percentage of customer churn could be attributed to bad customer service calls, slow response, or even no response. Even if you do have a great influx of new customers, customer churn is never good news. It is important to retain the existing customer base as they aid and abet sustained growth.
How can you reduce customer churn? The answer lies in data-driven insights. Read on to know more.
Laying out a roadmap and implementing it
You can reduce your customer churn with the help of insights drawn from your raw database. To draw up actionable conclusions, start by defining your primary KPIs (not more than five, three being the ideal number). Take inputs from stakeholders in this discussion to ensure the right selection of KPIs.
Often data is collected and goes to different teams but no action is taken to derive actionable insights from that data or implement them. This can be due to –
- Communication gaps among the teams
- A miscalculation in the implementation timeline that leave teams frustrated
- Drawn action plans are not being implemented due to a lack of resources
So you need experts who can pinpoint the right patterns and come up with practical conclusions.
Mapping your customer journey
When you try to map your user journey and be with them as they go through your website/ app, you will be able to see exactly where they are dropping off. You can then work from there to see what is the obstacle that your users are facing that’s leading to a churn.
You can retrace their steps, get a complete look at their journey. You can not always blame just one trigger for your long-term customers leaving you. Evaluating the final nail in the coffin is not what you should do, you need to go after the other issue and obstacle that can lead you to identify the problem area.
You need to quantify the major dropout points and work out ways to undo the harm done. This way you can also predict your user’s next possible actions. And if there is something you can do to win them back, you would know the right interaction point as well. Your ‘moments of truth’ will decide whether your customer trusts you or not.
When mapping your customer journey, find answers to questions like:
- Emphasize heavily on the ‘why’ at varying stages – why will the customer need two CTAs here, why will they click on the link that you want them to, why will they be motivated to come back to you or why not, etc. the derived answers will give you insight into your customer’s behavior.
- What will keep them engaged with your product or service, and when will different interaction points become a hindrance in their journey? You can go back to previous stages where your customers visited before they dropped off.
- Which way is the easiest for your customers to reach their ultimate goal, can that goal be achieved in three steps or ten steps? And what can you do to ensure that users don’t leave in between the process?
- How are you different from your competitors? Again ‘why’ will they choose you over others?
As you try to put a number or a measurement scale at various steps, you will be able to understand what is working in your favor and what is not.
Focussing on user segmentation
Your resources are limited, be it in terms of time, manpower, or customer incentives. You will have to focus on high-quality leads. You can use customer journey mapping data to predict who is more likely to stay like how well they interacted with offers, did they drop off very early or stayed till last, did they leave any feedback, etc.
You can also use your buyer personas to match with potential buyers for predicting the right fit for your product and divert your efforts accordingly. You can develop and implement algorithms based on buyer personas, deep dive into their characteristics for an accurate prediction model, and anticipating who is more likely to stay.
You will have multiple user segments depending on your product/ service offering. But do ensure that your assumptions are precise and use your existing data as well for future models.
Using predictive analytics and text analysis
At any given point in time, you have access to tons of data. To manually curate it or even factor in different variables is overwhelming. Therefore you will need machine learning and AI for developing predictive and text analytics.
Your text feedback is a gold mine for you to understand what your customers feel, and what improvements are they expecting. You can combine these insights with your customer churn prediction models to anticipate possible churn and take actions accordingly.
Also with your user segmentation, you can see which users can be let go and which have a higher lifetime value to your business. Your algorithm once developed, will continually evolve as per customer inputs and their possible future actions. Thus you will be able to prevent the churn from happening, especially for your most valued customers saving you the cost of acquiring new customers.
All this said and done, the data volume will keep on accumulating each day, it’s highly advised to use it to your advantage and get an edge over your competitors.
The more personalized you make your users’ experiences, the higher the recall value you get. Furthermore, data-driven insights give you precisely that. If you are still not convinced or are looking for insights to be interpreted for you, get in touch with us here to discuss what we can do for you.
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