3 Ways Machine Learning Can Help Your Business Identify Customer Pain Points
In today’s retail and SaaS world the competition is stiffer than ever. Customers have sky high expectations, and everyone is looking for an edge to stay ahead. So if you’re looking for a way to differentiate your customer experience, reduce churn and increase customer lifetime value, then we’ve got 3 ways in which machine learning (ML) can profoundly change how you map your customers journey and identify pain points along the way —giving your company a competitive advantage.
1. Use your existing survey data to predict survey outcomes for every customer.
Most companies will use some combination of customer satisfaction score (CSAT), customer effort score (CES), and net promoter score (NPS) surveys at various customer touch points in order to measure customer experience. It’s a great start, but these surveys typically have low response rates (5–20%) and leave you with a view of only a small segment of your customers.
With machine learning you can actually learn from your existing survey responses, and leverage them to accurately predict what the response would be for all unanswered surveys. So, instead of only having CSAT, CES, and NPS for a small portion of your customers you can have them for every one of your customers, in real-time no less.
Now you can know exactly when a customer would have a bad experience and give you a low score; allowing you to respond and take quick action to keep them from churning. Remember, 65% of customers think a positive experience with a company is even more important and influential than great advertising. So give them a personalized experience that’ll keep them happy and coming back for more.
2. Automatically identify organic trends in customer conversations and feedback
You probably collect customer feedback from a variety of sources: product review sites, comment boxes on your website, or open text fields on the surveys we just discussed. Alongside that, you also have customers talking about you on socials or chatting with your customer service or sales reps. All this feedback and conversations are generating a wealth of customer data that you’re most likely capturing, but struggling to truly analyze and leverage.
Most teams will try to rely on humans to notice trends or catch mentions of products that customers are talking about. But reading through this mountain of customer feedback is an impossible task. It’s just too much data for people to go through and make sense of! The result is a ton of time and effort being burned.
This is where ML can really shine, let the machine look at your data in an automated and unbiased way.
This will allow your team to save a ton of manual time and valuable effort, and easily see what trends, complaints, products, or services your customers are talking about — in an unbiased, real-time, and data driven way.
3. Predict customer journey outcomes
When you record every customer touchpoint from: visiting your website, to going to a store, to using your software, to reaching out for support, and then track past customers outcomes (such as if they churned, repeat purchased, or changed their average order value), you can leverage this data, and use ML to predict outcomes for new customers.
It’s like having a crystal ball for when your customers might not renew their subscription or return to the store. With this knowledge, you can identify pain points in their journey and get ahead of the negative outcome before it occurs.
The truth is that you most likely already have the data and systems needed to take advantage of Machine Learning. It’s come a long way from expensive custom solutions that are hard to understand and use. So if you want to take the first step to better understanding your customers, their journey, and their pain points, reach out to us and learn how you can easily take advantage of machine learning for your business and longterm success.
