If you are like many customer-centric organizations you run surveys such as Net Promoter Surveys (NPS) to understand customer loyalty. You try to ask open-ended questions to qualitative categorize your customers as promoters, detractors or just silent users. You also try to gauge the repurchase behavior from these customer outreach efforts.
Although these NPS based surveys are important, they do not provide insights into re-purchase behaviors.
There are a few approaches companies can take:
- Expand the survey questions to include areas such as product usage, value for money, quality, explicitly ask about the customer re-purchase intent.
- Start an in-depth interviewing outreach and ask customers, how they feel about the brand and product usage. But that has limitations. It only works if you have a small customer base but the approach starts to fall apart quickly. These approaches do not scale.
- Depending on the number of customers, you need an army of people to execute these one on one interaction. And not every customer interacts.
- It gets cumbersome to repeat the process regularly: cost and customer outreach and survey fatigue kicks in.
- You are dealing with data analysis on multiple fronts including codification etc to determine the root cause.
Because of these reasons, most companies execute these surveys and interviews once a quarter and stick to the classically structured questions.
However these surveys and interviews are a great opportunity to gauge customer "mood" and also the appetite for up-sell, cross-sell or retention sales. But we tend to miss them for a few reasons:
- The front line folks want to solve customer problems and answer their questions. This could be either by design, or they are not equipped in the subtle art of selling.
- The information that can us best help understand the customer interactions is in silos, so we miss out on some key insights.
On the other hand, you are constantly interacting with your customers, the day to day operations. They open support tickets, they make calls into your support organizations, they interact over direct messaging, emails, web-portals, and this list keeps growing.
The big question: How can we effectively use the data from these customer interactions to guide us in our retention and selling strategies?
This is where sentiment analysis and natural language understanding can help.
What if you had a system that constantly gauged the sentiment and mood of all your customers across all interaction points.
Not only that, what if this system could combine these insights with other predictors such as:
- recency, frequency, size or time between previous transactions
- product usage or
This analysis could could greatly help your customer success, retention sales teams to predict the cohorts of customers at risk, which ones are one time buyers and the ones that are repeat buyers.
Now you will be able to improve the following:
- Sell to the right kind of customer where your product is a better fit and results in low churn.
- Sell the sticky feature or module that results in better retention
- Sell to the most valuable business segment maximizing Customer Life Time Value (LTV)
This is where we can help.
- Our team can help in the analysis of your NPS surveys and customer interactions to predict loyalty, segments most likely to renew or buy additional products.
- Develop an actionable roadmap for your churn and customer loyalty models through data analysis and cost-benefit analysis