Webasap is a leading B2B tech company operating in the web analytics space.
Webasap has been measuring their NPS through surveys for the past few years.
Their NPS had been hovering around the mid-30s compared to an industry average of 49.
They knew they had to take some steps to address this.
2 years ago, they started analysing their NPS survey responses to improve their NPS. But nothing major changed for them.
The team at Webasap were concerned and wanted to launch an NPS improvement program.
The NPS was previously owned by the Product Manager at the company.
But now they created a team representing several departments to look at ways to improve their NPS.
This strategic team was headed by Emily, a customer insights manager who moved into customer insights from product strategy.
Just 6 months into her new role, Emily was excited to be given this responsibility.
She knew she had to look at new ways of looking at and analysing NPS.
She joined the company just 1 year ago, before which she was heading the customer retention program at an eCommerce company where she successfully reduced customer churn.
Read how Emily improved customer retention there using customer sentiment analysis.
As for this one? If you're looking to improve your NPS rating then sit tight as we navigate this example of Emily’s journey of improving Webasap’s NPS from 36 to 52 within just a year with customer sentiment analysis.
Webasap, like most typical most companies, had a fairly straightforward process of analysing NPS.
This is what it looks like:
Fairly simple, right?
But that’s where the problem lies. NPS analysis’ simplicity often creates a roadblock in conducting root cause analysis and finding out where exactly the bottlenecks are.
Emily knew the process outlined above is not enough to make any strategic changes enabling her to improve their NPS.
There were several problems she faced in the current process. Let’s talk about a few of them.
The reasons stated above meant Emily and her team were getting very generic, top-level insights - insights that didn’t provide any value or inputs to make business decisions.
With this in mind, Emily was determined to find a better way to find granular customer insights that would give her the true way into the minds of her customers.
Emily had already used customer service data to inform several business decisions and she understood the value of customer sentiment analysis.
She knew she had to analyse customer service conversations along with the regular NPS analysis to get the true picture of where the company was lacking.
If you're interested, check out our latest blog post on why customer sentiment analysis is essential to boosting NPS.
Now, moving on to the actual steps.
Step 1. Capture and compile customer data from each touchpoint
The key to improving NPS is understanding the drivers behind NPS scores.
But only analysing NPS surveys does not show the complete picture.
Emily made a shift in the data collection process and included customer conversation data from customer service in it as well.
This helped her get the raw, authentic, and rich data she needed to do an objective analysis of how the customers were feeling.
There are various reasons why customer sentiment analysis is beneficial here and you can find a detailed explanation in our previous article on boosting NPS with customer sentiment analysis.
Step 2. Analyse customer data at scale
A big drawback of analysing NPS data manually is it’s not scalable. Think 1000s of survey responses.
It is extremely hard to look at each of those responses individually and then compile average trends.
What’s even more arduous is linking those high-level insights to root cause insights from customer data.
Then there’s human subjectivity. When 2 or more people scan through surveys, there are bound to be different ways of looking at it.
Plus, there is no practical way of harmonising NPS survey responses and customer service data and finding correlations.
This is where an AI-based tool like SentiSum can help.
An AI-based analytics and reporting tool can analyse both your NPS survey responses and customer conversation data and give you details into what’s leading to a bad NPS.
With the help of AI, you can also go as granular as you want.
For instance, when Emily was analysing Webasap’s NPS, she found that customers are mentioning ‘poor usability’ a lot for bad scores.
She then looked at the customer service conversations and dug deeper.
This gave her insights like:
People who mentioned ‘poor usability’ or ‘usability’ negatively, also mentioned ‘app usability’.
Great. So the app is the real problem.
But wait, that’s not good enough. She went one step deeper and realised that when people mention ‘app usability’ what they mean is they’re having a problem with the ‘editing feature’.
One step further, she found it’s the ‘editing speed’ that’s causing the issues with the ‘editing feature.’
Finally, the biggest and most prominent problem with the ‘editing speed’ is that the app slows down considerably when customers use it for more than 10 minutes.
By just analysing the NPS surveys, Emily would just know that ‘usability’ is a driver of bad NPS.
Whereas now Emily understood that the prevalent issue why people are leaving bad NPS reviews is that the Webasap app slows down after users use its edit feature for more than 10 minutes.
Step 3. Make customer insights accessible across the company
Once you get insights like the one explored in the previous point, it's time to put these to work.
Customer service teams can often tell you more about what’s causing bad NPS than NPS surveys.
But because they often lack the data to back up their claims, their claims are often ignored.
But sometimes, the key customer insights don't reach everyone in the company.
With a strategic customer sentiment analysis program in place that blends your customer service and NPS survey insights, the entire company should have the data and use it make confident business decisions.
For instance, Emily took the data to her product team and informed them of the issues of slow editing on the app.
She learnt that the product team had not paid attention to the mobile app much and was more focused on the web app.
But with the feedback straight from the customers, it’s easier for them to evaluate their product roadmap and prioritise changes to the mobile app, especially to the editing feature.
Emily made this a regular practice where product, operations and other teams tweaked their roadmap or informed strategic decisions based on customer insights.
By following this structured process, including customer sentiment analysis in the mix, and mandating business changes based on these insights, Emily was able to improve NPS from 36 to 52 within just 1 year!
If you’re in the tech space and trying to figure out a way to improve NPS, it’s crucial to have a look at your customer service conversations.
By doing so, you’ll not only unlock a new way of looking at NPS analysis but also gain an edge against your competitors, who are still not doing this.
Complementing your NPS analysis with customer sentiment analysis of customer service data can enable you to find what’s driving your NPS at a much granular level.
It will also equip you with data-backed insights that can be used across your company to improve the overall customer experience and as a result, your NPS numbers.