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How to Boost your Sales with Customer Browsing Data

Tracking browsing data is common practice for many online brands. This data often gets buried in servers. But used correctly, this data can increase sales.

Nimrod Ifrach
August 28 2019

Many e-commerce companies track their customers’ browsing activity – when they entered the site, which pages they visited, and for how long. In some cases, this data just piles up on the company’s servers, but sometimes it can be used to generate actual value.

Recently, I received a data set containing some of the above information and wondered if I could draw some interesting insights. I tracked over 60K paying customers for a six-month period, in which they visited the site almost 700K times and made over 125K orders. To keep things simple, I focused on the customers’ visit volume, without including specific pages they visited.

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As a starting point, the first question I thought to answer was: How many times does a customer visit the site before ordering?

The graph below shows the distribution in the data:

A reminder: all of the customers in the sample ordered at least once before the six months period—which recorded their browsing activity—and at least once during the six months period. We excluded the visit on the order day. So, what does the data tell us?

The percentage of orders drops exponentially as the number of visits goes up. Intuitively, fewer customers visited the site over and over again, while more customers visited the site just once or a handful of times. We can also see that 71% of customers visited twice or more before ordering, and 54% visited at least three times before checkout.

The data contains visits and orders across a period of six months. Theoretically, a customer might visit the site and make an order six months later. In this case, we can debate whether there’s any connection between the visit and the order. Since the data regarding the specific pages and products from the customer’s visit is out of scope, the next graph shows all the visits made during the two weeks prior to an order, not including the one on order day:

Now it’s getting interesting. We can see that for 61% of the orders, the customer visited the site at least twice the two weeks before the order, and 40% visited three times or more beforehand.

Another thing I wanted to check; when exactly, two weeks prior to the order, did customers visit? Focusing on the first three visits within the two-week period prior to the order, we can see on average when the customer visited and the standard deviation in days:

It’s interesting that most of the customers visit 5-7 days before ordering, and don’t return until ordering. We can carefully assume that when a customer is on a verge of buying, they visit one last time to check the price, or possibly take a deeper look into the product details.

Visits Affect On Future Orders

So far, I’ve presented a descriptive analysis on the customers’ visits behavior. Using this analysis, (and some common sense), we can expect some correlation between visits and future orders. To test this hypothesis, I added an additional 100K non-paying customers to the 60K paying customers sample. Now, we can test if the visit activity effects future orders. The independent variable was number of visits within a two-week period and the dependent variable was 1 or 0 – did the customer order sometime in the following month. The picture below illustrates the process:

I ran the test for three different points in time to ensure consistent results. In addition, I divided the customer into two groups – customers who visited one or two times and customers who visited three or more. This way, we can create a confusion matrix and evaluate the results:

It seems we’re onto something! If you look closely, you can see that on average, from the customers who visited one or two times, only 25% percent ordered in the following month, while 44% of those who visited three or more times made a purchase.

To Wrap It Up

It’s obvious that more visits indicate higher interest, therefore greater chances of completing an order in the future. We don’t need to analyze data for that. But now, we can make a data driven decision –if a customer makes two visits in less than two weeks, we should invest marketing efforts to “push” the customer to a third visit. This blog is just the tip of the iceberg when it comes to analyzing and acting upon customers’ browsing activity –make it count!

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Nimrod Ifrach

Nimrod Ifrach, a Senior data scientist at Optimove, has vast experience in working with data in the iGaming industry. He delights in turning statistical power into business value, always keeping the data at the center. Nimrod holds a BSc in Industrial Engineering and Management from the Tel Aviv University.

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