From a traditional business view to a business post-pandemic, customer health is the new success currency.  For most, there have been more eyes on financial statements for business this year than ever before.  For Customer Success departments those conversations have shifted from how do we decrease churn to how do we increase our overall customer health. With that added scrutiny and pressure it has become apparent that there needs to be a better way to gauge customer health and have that measurement be forward-looking.  Changing the way you think of customer health and to be able to project what their health WILL BE is different than saying this is what my customer has done in the past.  And that right there, shifts the dynamic of the customer health conversation.

The obvious next question is, “how?”  How do companies make that pivot to become forward-looking when it comes to customer health, satisfaction, and even net promoter scores?

The answer is artificial intelligence, machine learning, predictive analytics, predictive customer insights.  Using technology such as that may sound a bit overwhelming and too data-heavy.  In the past, they have been!  Traditionally, tools that were using those technologies were not created with a CS user in mind until recently.  

Today, tools that focus on customer insights, including customer health, are able to predict what each customer or account’s health will be in the future based upon your customer data.  Tools created with customer insights in mind can not only help you predict your customer health but also tell you what influences it the most, both negatively and positively.  Let that sink in. By using a predictive customer insights technology like Squark, you can find out what is impacting your customer health and either, prioritize and greatly increase those behaviors, or dive into it full speed and make some updates to your business to better your overall customer health.

In order to provide such a powerful outcome, Squark has worked hard to harness the power of data science and has packaged all of the heavy lifting into the tool to make the user experience easy and reliable.  Let’s review this process in four simple steps.

  1. Select the project type that best answers the question you are trying to predict.

    In this case, our question, “what will my customer health scores be for X accounts,” is a multi-class project.

  2. Upload your current and past customers’ historical data by either CSV or one of our pre-built connectors. Once that data is uploaded, you select the column that you want the tool to analyze, learn from, and, ultimately, predict. Squark prepares and enhances the data you’ve uploaded, meaning it cleans it and even appends missing data points to make results more insightful.

    Customer health can be represented in various ways, such as A, B, C, D, or F or Excellent, Good, Fair, or Poor. Squark will work with your data how you do.

  3. Upload or connect to a list of customers of whom you’d like to generate predictions. Make sure you label the target column the same way it reads in the historical data file.

    This will be the file of current customers whom you’d like to predict customer health.

  4. Evaluate your results. Read through the predictions and learn which data most influenced the results (both good and bad).

    See each account/customer’s predicted customer health and review what data most influenced their health score (again, both good and bad). In this data set, we can see that the one-year contract type, attending quarterly business reviews, and those located in territory one all have had a positive impact on customer health. Whereas monthly contract type, quick start onboarding, and those in territory three have all negatively impacted your customer health scores.

This type of technology is emerging for a reason. It’s the most accurate and efficient way to prioritize and plan resources for CS teams. It is all data-driven; emotions and gut feelings are removed from the equation. It enables you to predict and be where your customers need you most, which is key in today’s business environment.  

Review the areas of opportunity in those data points, identify those detracting from your customer health, review your customer health by segment, and truly get a data-backed 360 view of your clients and know how to prioritize your teams’ actions to increase your score. It’s a win-win for both you and your customers.  Better health scores contribute to better CSAT and retention numbers, AND you can refine your customer engagement strategy so that their customer journey is a positive one. 

If you have any questions or would like to review how Squark can work with you to predict your customer health, please reach out to us here.

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The Next Step in Predictive Analytics: Predictive Customer Insights

Squark is a no code predictive analytics SaaS that automatically analyzes business data you work with every day to predict customer outcomes, uncover new opportunities, and mitigate risk. It is a simple and easy to use no code AI software that doesn’t require data science nor technical expertise. Confidently use clicks to command Squark’s no code data science. Make informed decisions about what your customers will do next to increase your business impact and deliver a better customer journey.

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