I’d like to share with you the topic of customer success sentiment. People are asking us, at Komiko, about whether we can analyze sentiment of emails. What most of them mean is the NLP (Natural Language Processing) approach that analyses the text and scores it to positive, neutral or negative sentiment.
Sentiment analysis is ok when analyzing social media sentiment trends. It can work on large volume of short posts. But when it comes to analyzing one single piece of text, even short, the accuracy drops dramatically.
One of the leading CRM vendors showed off their new sentiment analysis engine on the following example: “Had a great meeting with the client yesterday. They love us and want to buy.” You would expect it to be positive 100% or if you are a professional statistician that likes to hedge your bets, then 90% positive. The sentiment analysis engine score was positive 77%, which is what? B-. When you try analyzing text that includes sarcasm or slang the results are expected to be much worst.
Even if you do manage to determine the right sentiment of emails, it still needs to be evaluated in context. In many cases, negative sentiment in sales means that the prospect is serious. They want to buy, and they want to make sure that it’ll work for them. Sentiment may come out as negative. Some people would even argue that positive sentiment classifies tire kickers that are just browsing.
The worst thing that can happen to sales people is not negative sentiment. It is simply when their prospects stop replying to their emails and phone calls. It is the same for customer success teams, or as my business partner Hal Howard says, “customer apathy is the biggest enemy of retention”.
Meaningful customers and prospects sentiment and even more interesting intention should be based on their digital body language and the context of the interactions – sales, customer success sentiment, support.
What is digital body language and customer success sentiment?
In short, it is about whether your customers reply to your email and how fast. Do they accept and show to meetings? How many people are engaged and how senior they are?
The term digital body language was originally coined by Steven Woods at Eloqua.
When you have access to email system you can get much richer set of signals that helps you characterize the relationships is much more reliable way.
It is possible to predict the probability of an opportunity to win or account to retain based on these signals with a pretty high accuracy. Since it’s based on these signals you can also provide a very clear guidance of what should be done to improve the probability to win or retain.
As an example, analyzing successful and less successful opportunity engagements, shows that on average, won opportunities had 2 times more meetings than lost ones. As the opportunity progressed the ratio gradually went up and ended at 3x more meetings for won opportunities.
Another example in the area of customer retention. Top performer Customer Success Managers have managed to engage 3 times more contacts than low performers. They also engaged 2 times more people from their team vs low performers.
You can also learn more about digital body language blog.