Great customer experiences lead to conversions. You create great UX by ensuring that the customer journey is as frictionless and intuitive as possible. You can’t accomplish that unless you are able to predict customer behavior from the first point of contact to the point of conversion. Brands who are successful at this often rely on insights they gain from big data.
Mined Data Can be Combined With Direct Customer Feedback For Great Insights
Big data doesn’t need to stand alone, nor should it. These large repositories of information can be combined with more intimate and real time information to gain insights on customers’ thoughts, opinions, and motivations.
Here’s an example. Your analytical data may tell you that a particular product is simply not selling. If that is all you go by, it might be easy to dismiss the product itself as a loser.
The problem is that if you make that decision without communicating with your customers or customer facing staff, your assumptions could be wrong, and your predictions about customer behavior could be off base. In this case, the issue may be with pricing, the performance of a landing page, or some other factor not the product itself.
When you listen to your customers and your staff, you can combine the insights they offer you with data to learn about customer preferences, weak spots, and trends.
Big Data Can Provide Information That Can be Used to Personalize Experiences
Data that tracks customer preferences and behavior can be used to create personalized website experiences, and to curate personalized content. This can be done on both a micro and macro level.
On a micro level you can collect specific data about customers. Then, you can predict their behavior based on their specific purchasing habits, social media behaviors, and the data you are able to collect from them through their interactions via social media.
On a macro level, you can collect and store information from general data sets. This can give insights into overall customer behavior, website analytics, and other more general data. All of this information can then be used to create personalized website experiences, to recommend products, and find other ways to predict the customer’s wants and needs throughout the purchasing journey. Salespeople in particular can use this information to identify data driven sales opportunities.
Shopping Cart Analytics Can Predict Future Needs
What customers add to their carts is meaningful. Whether or not they complete purchases, adding an item to a shopping cart is an indication of interest in that item. It can also show need for an item at a given point in time or under specific circumstances. That’s important as well.
Of course, whether or not the customer continues on and makes a purchase is also meaningful. Data can tell you if your pricing, level of earned trust, website performance, or other factors are successfully pushing customers through the funnel. Data can also tell you not only when you are losing them and why.
Combine these metrics, and you can curate special offers, customize your ads, and address roadblocks to conversions. This includes, but is not limited to improving the checkout process so that it is more efficient and is perceived to be safer.
Engagement Metrics Can Be Used to Determine Which Content Will Hit With Customers
If you know what to look for, engagement metrics can help you predict which content is going to earn comments, likes, and shares. By digging into this information, you can determine which content is likely to be a hit, when you should publish the content, and how you should promote it.
The key is to think beyond the obvious. For example, ‘my customers like videos most’ isn’t an insight. Everybody likes video content best. Now, ‘my customers like how to videos when they are viewing my product pages’ is helpful.
It’s also important to use data to understand how customers are engaging. Liking, sharing, and commenting are three different behaviors. The motives behind those behaviors can be very different as well. For example, people tend to like posts without doing much investigation.
Merge Customer Data With Performance and Logistical Insights to Predict Trouble
Your data shows you that bounce rates on two of your landing pages go through the roof near the end of the month. You also notice more shopping carts are left abandoned. Other analytics show you that orders to be shipped out of one of your facilities are frequently cancelled.
What’s going on? You have part of the picture. You know what customers are doing. They are abandoning shopping carts, cancelling orders, and backing out of your landing pages. The question is, what else is going on. Big data can tell you that there are logistical issues impacting your warehouse. It can also indicate that performance issues due to end of month demands are making your pages load slowly. Once you know these things, you can predict when logistical and performance issues may result in problematic customer behavior.
The better you can predict your customers’ wants and needs during any part of the customer journey, the more you will be able to predict what their behavior is going to be. This means you will be better prepared to give them what they need at any point in time. This will help you to design the overall customer experience, curate content, and recommend products among other things. You’ll also be able to predict trends. All of this will allow you to create the kinds of customer experiences that will drive more conversions.