Taking a peek into the future is a skill many of us are trying to master, but few are doing very well at.
But, there must be some way we could start pulling this daunting task off...
As many SaaS businesses continue to shift their focus toward customer expansion and retention, there are a variety of metrics and tools available to help drive these twin goals.
Two popular approaches to this are Net Promoter Scores (NPS) and predictive analytics. Let's dive into what these are and how they are used...
Net Promoter Score (NPS) is used to gauge customer loyalty by asking customers how likely they are to recommend a company on a scale from zero to ten.
The score is then calculated by subtracting the percentage of detractors (those who score 0-6) from the percentage of promoters (those who score 9-10).
NPS is a popular, and widely used tool for measuring customer satisfaction.
But, it has limitations in terms of providing actionable insights. It also isn't very good at predicting future customer behavior.
Predictive analytics, on the other hand, uses data and algorithms to analyze behaviors and predict future actions.
Predictive analytics can be a huge help so that companies can anticipate customer issues.
They can then proactively address them before they become bigger problems down the line, or the biggest problem: a churned or lost customer.
This allows companies to save money and time, and spend more time on the tasks that drive the most revenue.
And that's what we're all here for, right?
One of the main benefits of using predictive analytics in customer success is the ability to improve customer satisfaction.
Traditional NPS surveys can only provide a snapshot of how the customer is feeling at a specific, too small, point in time.
This doesn't give CSMs a bigger picture to look at. It’s utilizing a fraction of the huge amount of data we have.
And that can leave them surprised down the line.
And it can leave businesses scrambling to make up for that loss in revenue.
When predictive analytics comes into play, CSMs use that holistic view of how they're using the product to inform what their next best action should be.
These actions will allow the relationship to stay happy and grow.
For example, if a customer has a history of submitting support tickets related to a particular issue that has caused churn with similar customers in the past, predictive analytics can flag that customer as high-risk.
When coupled with technologies such as Generative AI, it can then trigger proactive outreach from the customer success team to address the issue before it leads to dissatisfaction, or worse, churn.
By taking a more proactive approach to customer success, businesses can have a better understanding of what their customers need before it is too late.
Predictive analytics can also help businesses provide more personalized customer interactions.
These personalized actions can pay off big time: a better customer relationship with tailored actions means less time wasted by CSMs.
By analyzing customer data, businesses can gain insights into individual customer preferences and behavior, allowing them to tailor their interactions to each customer's specific needs.
Not every customer needs a QBR every quarter, and some customers might need more help implementing a specific feature much more than others.
By using predictive analytics, customers can get the support they need without the worry of too little or too much.
This leads to truly successful relationships that don't have that all-too-common surprise factor.
While Net Promoter Scores (NPS) can be a useful tool for measuring customer sentiment, they have limitations in terms of providing actionable insights and predicting future customer behavior.
Predictive analytics, on the other hand, provides a data-driven approach to understanding customer behavior and needs, which can lead to more effective customer success strategies.
The benefits of using predictive analytics in customer success include improved customer satisfaction, increased revenue, and a more manageable workflow.
And one way you can implement predictive analytics is with Tingono!
Our AI-powered models take the guesswork out of your next best action.
It aggregates all of the data points on your customer so you can find the actions that will help them stay with you longer- and expand when possible.
Instead of relying on the broad metric of NPS, allow yourself to get specific and let machines do the work that humans can't.
Want to do more with actionable predictions? We want to talk!