Customer churn predictions made easy with Magicsheets
Customer retention is at the heart business development and subscription-based businesses rely on loyal customers to continue operating. It can be hard to know which customers are most likely to churn however. This becomes increasingly worrisome as long-term users cancelling their memberships and switching over to competitors’ products is bad news for a range of businesses: from SaaS business, streaming platforms, to bloggers and online influencers. I
So what if we could predict which customers are most likely to churn?
Once we know this, we can take the necessary steps to make sure they stay happy and keep using our product! It is not realistic, however, to attend to every single user individually, and many loyal members don’t need any extra incentives to stick with a product or service in the first place. Once we have identified who is likely to churn therefore, we can turn our attention to them specifically. This can be achieved through applying an AI system, yet it can be costly to learn how to build Machine Learning, and most businesses already have enough on their plates as is!
This is where Magicsheets comes into play: you don’t need any special training or coding background to be able to make the needed predictions!
Simply press “predict” and let the magic unfold. You can focus on keeping your customers happy while Magicsheets deals with the data.
Let’s walk through a simple example of Customer Churn prediction to get a better idea of how all of this works. You can use the Magicsheets Customer Churn template to follow along, and then reuse it for your own use case. You can also watch a brief walk-through video if you prefer!
Customer Churn data set: what is the prediction based on? 🤔
For our example, we are using a data set containing data of customers of an existing telecom company (available here).
Now that we have the data in our spreadsheet, we can base the Customer Churn prediction on data concerning the profiles of our customers. In this case, the data include:
- Demographic data: gender, age, etc.
- Geographical data: where are the customers based?
- Service data: which services have the customers purchased?
- Financial data: how much have the customers paid us in the past?
Each of these different features (properties) occupies one column in the spreadsheet.
We are aiming to predict whether or not the final 30 customer profiles (combinations of all the different features) will churn or remain loyal to the brand. We assume we already have data concerning 70 customers who we know have or have not churned.
Based on this existing data, we hope to create a model that will recognize customer profiles of customers that are likely to churn, and those that are likely to remain loyal to our brand. Using this model, we will be able to predict which of the remaining 30 customers are likely to churn!
Predicting Customer Churn 🏃♀️ with just one click ✨
- Let’s start by firing up the Magicsheets add-on.
2. Select “Class”: We want to predict a “Yes/No” answer to our question: “Is this customer likely to churn?” We thus classify the customers into two classes, and we want to predict the appropriate class for the remaining thirty customers.
3. Select the relevant data: These are the feature columns we are using to build our model.
4. Select the output cells: These are “Churn prediction” column cells where the predictions will appear. In this case, for the remaining 30 customers.
5. Press “Predict” and let the magic happen! ✨ The result will appear directly in your spreadsheet, in the “Output” cells you selected.
Using your own data 🔢
To make predictions using your own data, you can simply follow the same steps to get the predictions you need. Just make adjustments where you need, to make predictions suitable to your company’s unique data.
In many cases, you might not need as many fields as we have used in our example, but that is completely fine. Magicsheets can work with very few, as well as hundreds of data fields.
In addition, don’t worry if you don’t have thousands of data rows. Magicsheets can work with both very small and very large amounts of data, just input the numbers, open the add-on and off you go!
Can I trust these numbers? 👀
At this point, you might be thinking: “That all sounds great, but can I actually trust these numbers?”.
One way to assess the quality of your Magicsheets prediction, is to look at the “accuracy” level in the add-on. The higher the accuracy, the better the model that runs under the Magicsheets hood and the more precise the predictions.
How can I make my results better? 👩🏫
Not all results are equally accurate. In the Machine Learning world, your results’ quality will largely depend on the data set used. Here are some suggestions to make the most out of your data:
1) The size of the data set can make predictions more accurate:
You can definitely make a good prediction with only 20–30 customers’ data, yet the results are often likely to be more accurate if you have 200–300 customers’ data for example.
2) Select the features that make the most sense to what you’re looking to predict:
Magicsheets can generally pick out the data fields (the columns) that matter most for the desired results. However, sometimes we need to use our own judgement to decide which predictions are most applicable.
For example, “eye colour” might not be very useful when predicting customer churn for a SaaS business, — or it might! This is where the professional’s best judgement comes into play.
Predicting other things 💡
Magicsheets can be used to predict much more than just Customer Churn, however. This is just one Customer Churn-focused example of how our Machine Learning magic can be used to help you power up your business with our data-driven solutions.
For more ready-to-use case examples, visit our template gallery.