When you want to improve user experience with your product, or when you want to improve customer experience with your service, how do you go about doing it?

First, you might have a few meetings to identify what’s causing users to have difficulties with your products or causing customers to complain about the quality of your service.

Next, you might brainstorm some solutions and come up with one strategy that could improve the situation.

Finally, you roll out your new plan.

Testing Your Hypothesis

How do you know if the strategy you’ve adopted through your basic troubleshooting process is working? If you can verify that it’s working through surveys, how do you figure out the degree to which it is working?

Imagine you have a cloud call center and you want to roll out a strategy to improve customer service by adding some nifty new software that gives your users many more options to improve the quality of the calls; how do you know if what you’re doing is working?

Well, you could interview your agents, monitor their phone conversations with customers, and call a few customers to get their point-of-view.

Let's say that the majority of the agents said that they found their calls went more smoothly, the calls you monitored suggested that this was true, and the customers you called for feedback said that they were satisfied.

Does this mean that you have successfully resolved the problem?

Although you get a favorable overall impression that the new software did improve customer service, how can you be sure? Since you can’t survey everyone, it’s a random survey of agents, monitored calls, and customers. Consequently, how do you know if you just happened to survey the bulk of the calls that went well but missed those calls that would indicate that things haven’t improved that much overall ... or, perhaps, things have got even worse.

Well, the good news is that there is a way to get more effective feedback than relying on subjective, random surveys. By using analytics to review your data over time, you can bypass all survey bias issues.

How to Mine Data

While analytics are a good way to evaluate the data that you’ve selected, it’s only as good as the data you review. If you mine irrelevant data, it won’t give you much useful information once you analyze it. If for example, an accountant who is trying to calculate company overheads includes how much employees spend on eating out during their lunch breaks, he would gather irrelevant data that would skewer the accuracy of his total sum. Although this is a humorous example, the point is that your analysis is only as good as the data you collect.

2 Ways of Sorting Between Relevant & Irrelevant Data

While it’s possible to dig deep into the process of data mining technology, probability theory, and statistical interpretation to get insights into how to decide what data to mine, it may not be necessary. Two basic principles of business and psychology may be enough to help you figure out what type of data you should collect:

1. Gather data on what is not working and what is working.

If you know that things are not going well for your business, you can probably make a few educated guesses about what could be causing the problems. In order to verify your hypothesis, you need to collect data on what’s not working. Once you’ve found what’s not working, you might, as in our call center example, decide to try a new strategy to fix the problem. Now, in order to verify if your solution is working, you need to collect data on what’s working. Naturally, there are many other possible permutations, but the point is that by collecting data on what’s not working and what’s working, you’re empowered to initiate measurable changes.

2. Gather personal data.

We’re not talking about breaching privacy but of using personalization in your business processes. For instance, Amazon uses personalization to suggest books, music, and TV shows that suit their customers' distinct interests. If you start mining your data for information that can later be personalized, then you will increase engagement, improve conversion, and earn higher revenues. Machine learning is a powerful way of gathering this type of personalized data.

In closing, by gathering relevant data and analyzing it, you can delight more customers because you’ll be solving problems important to them, making appropriate suggestions for other products, and responding faster to their needs.

Also Read: How the UX (User Experience) Affects SEO