Most ecommerce businesses know they should gather feedback about customers’ interactions with their websites so they can better understand the customer experience. This includes things like why customers click (or don’t click) on certain elements within a page and what stops customers from adding items to their basket and completing the checkout process.

But gathering this kind of customer feedback is difficult and time consuming when done manually. A simple 10-question, open-ended survey, for example, can generate thousands of customer responses that must be categorized and analyzed in order to yield meaningful data.

There’s some good news, though: Machine learning technology is making it easier for ecommerce businesses to perform the kinds of research that can help them improve their customers’ web journey, boosting online sales volume and profits in the process.

Problems Solved by Machine Learning

A sentiment analysis machine learning engine helps solve two of the biggest problems associated with online customer research. The first is managing the sheer volume of data that’s generated. The technology uses sophisticated algorithms to analyze the language used in customer responses and extract key themes, recurring topics and sentiment analysis, either positive or negative. Machine learning accomplishes this in a fraction of the time it would take to do manually.

The second problem is human bias. When customer surveys are done manually, it’s impossible for human bias not to creep into how responses are grouped and language is analyzed. This taints the survey results and makes it difficult to understand what customers really think about their online experience. Machine learning will give you a true reflection of how customers feel about their experience on your website.

Machine learning is ideal for gathering customer research data and uncovering insights at scale. It automates the process by grouping themes together so you can uncover useful insights and act on them quickly to improve your customers’ online experience. By using machine learning to analyze online customer survey research, your ecommerce business will be able to spend less time analyzing data and more time acting on what your customers tell you about their online experience so you can make improvements.

How Machine Learning Boosts Win Rates

Inspired by how the stock market uses trends in data to predict future outcomes, a machine learning predictor engine predicts the outcome of experiments before they have even run. Trained and tested on historical records from more than eleven thousand experiments, it can predict with a high degree of accuracy if a test is likely to win, lose or be inconclusive. Based on this, you can decide whether it makes sense to run an experiment or redirect these resources into other areas that will generate a higher return.

The result is a higher win rate. For example, the industry average win rate is 24%, but our clients’ average win rate is 44%, nearly double the industry average.

Embed Machine Learning Throughout Your Optimization Process

Successful ecommerce businesses recognize the importance of ongoing research to uncover customer pain points. Equally, they recognize their limitations in being able to continuously improve their customers’ poor experiences due to time, budget and resource constraints.

You can use machine learning to help you become more effective in discovering your customers’ pain points and implementing solutions that have a higher probability of improving your customer’s digital buying experience. As a result, you will enjoy accelerated revenue and profit growth.

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