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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, including pages visited, frequency and length of visits to the site, and products viewed, is often hoarded on servers never to be seen again. But used correctly, this data can increase sales.

Many ecommerce 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 the on the customers’ visit volume, without including specific pages they visited.

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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  • Avatar
    Daniel Plotkin

    Hi Nimrod,

    Liked this post! There is a vast trove of business intelligence hiding in website behavior data and most companies are not leveraging it for strategic advantage.

    I want to specifically comment on this sentence in your post: 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.

    One of the major shortcomings of website user analytics solutions today is the fact that they only focus on high-level things like pageviews, session characteristics (e.g., referrer, number of pages viewed, time on site) and a handful of pre-selected on-page actions (e.g., clicked Add to Cart, submitted a form). However, there is a wealth of valuable intelligence that can be gleaned to generate insights for increasing engagement and revenue – by analyzing every user action within every page of every visit.

    Only by analyzing the complete user behavior data set can data scientists, product managers, marketers, etc. mine all of the business intelligence available within the behavior patterns of their visitors and customers.

    Why guess or make assumptions about what users are doing on the site? The data to tell you exactly what they are doing is readily available for anyone who wants it.

    I am with a team developing technologies to make this detailed behavior data available for immediate reporting and analysis by data scientists and BI/analytics tools – check out http://www.convizit.com to learn more.

    Keep up the great work!

    Daniel

    • Avatar
      Nimrod Ifrach

      Hi Daniel,

      Thank you for your comment! It’s great to hear you liked to post.

      I totally agree with you that there is much more in the realm of measuring the engagement of site visitors than what I addressed in this post. My goal in this post was to introduce the topic to people who are not fully familiar with the topic and to keep things simple and accessible.

      All the characteristics and attributes you mentioned, such as referrer, number of pages viewed, time on site, clicked Add to Cart and many more, are actually used in Optimove to understand customer/visitor behavior in order to increase engagement and revenue.

      Stay tuned for my next post!

      All the best,
      Nimi

    • Avatar
      Daniel Airfleet

      Hi Nimrod,

      This is a fantastic article!

      Daniel

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