Whats Next in Web Analytics: Visitor / Customer Retention
Let’s say you are in charge of marketing the web site. You have a multitude of media choices, but not an unlimited budget.
If possible, you want to only spend time and effort attracting visitors who are going to generate the highest value for the business. What if you had access to a web analytics report that told you, in advance, that one source of visitors generates customers who visit / buy only once, and another source generates customers who visit / buy multiple times?
Do you think this report would be useful in making the decision on where to spend the limited budget?
You betcha. Welcome to visitor / customer retention metrics and reporting.
Visitor / customer retention metrics have been used offline for decades in direct and database marketing. This is the same space a lot of current “best practices” in search marketing and visitor conversion to goal came from. But these metrics have always been available only at the very high end of analytics offerings, because you need to track visit behavior over time at the visitor level. To do that, you need a data warehouse, and they’re not cheap to own or operate. Up until the past several years, most applications to track individual visitor / customer behavior over time had to be custom built.
But today, most of the analytics vendors provide some type of access to tracking visitor history over time, at least at the high end of the line. For example, if you’re using WebTrends Enterprise, you may have seen these retention metrics (Latency, Recency) but didn’t know what they meant or how to use them. I expect this “visitor history” functionality to “migrate down” at least into the mid-tier price range over the next couple of years. So even if you’re not quite ready for visitor / customer retention reporting yet, now is the time to start learning about this area and the metrics surrounding it. You’ll want to know what’s going on when all of a sudden everybody starts demanding this information.
Here’s the basic idea behind retention measurement. Some visitor sources tend to generate high goal conversion rates and others low goal conversion rates. Tracking for this takes place mostly in the “present”, the visitor either converted to goal or they didn’t. I’m here to tell you that the following is also true: visitor source affects the value of the visitor or customer over time, after the initial conversion to goal. Some sources will create visitors / customers with high value in the future, and others will create visitors / customers with low value in the future – and everything in between.
Simply put, the profitability of the visitor / customer in the future is affected by their initial experiences with the web site, most specifically the marketing approach that brought them to the site in the first place. Here’s an example:
A client was running 30 simultaneous campaigns of all kinds, 3 messages across 10 different media. They ranked all campaigns by initial conversion to newsletter, and killed the bottom 1/3 of campaigns. Later, (with a custom solution) I was able to prove the visitors generated by these killed campaigns were responsible for 80% of the total revenue generated by all 30 campaigns over the next 3 months. In addition, the campaigns with the highest up front conversion to newsletter were in fact at the bottom in terms of generating revenue. Creating a matrix of campaign sources, we found the type of media used, not the message, was responsible for this divergence, and the most expensive media had the highest conversion to newsletter and lowest subsequent conversion to sale.
So when they killed the bottom 1/3 of the campaigns based on initial conversion, they in fact destroyed the potential value of their visitor stream while dramatically increasing the cost per new visitor. I predicted they would need to “hold on tight” because even though their sales were strong in the present, they could expect them to go soft in the future. They didn’t believe it. About 30 days later, sales dropped significantly, and it was because they had spent so much effort attracting visitors with low potential value while turning off the stream of visitors with high potential value – value to the company in the future.
Perhaps you have measured this behavior after the fact and are already taking advantage of it. Good for you! By using retention metrics on your campaigns, you will be able to predict this “potential value” behavior in advance. With a little experience, you will be able to look at a report and say, “Man, this campaign isn’t doing anything now, but in 30 days or so it is going to be huge”.
The bottom line? For every campaign, you should have two metrics, one that speaks to the current value of the campaign (probably sales / conversion rate), and one that speaks to potential or future value of the visitors / customers being created by the campaign. Otherwise, you might just “optimize conversion” yourself right out of business.
But wait, as they say, there’s more. These same retention metrics can be applied to non-marketing, non-campaign behavior as well. Any visitor / customer activity on a web site – visits, purchases, downloads, game plays – can be profiled using retention metrics and used to predict how likely it is the visitor or customer will ever come back and engage in this activity again. Knowing this, you can execute customer maintenance and “win back” activities targeted to the very visitors / customers who are in danger of calling it quits with you – and not expend money or effort on those who are not likely to ever come back.
For example, take a subscription site. Subscribers were paying for online access to content that was available offline in the form of endless legal manuals. To be sure, this content was difficult to use in that form, and the idea of the same content being “searchable” online was quite appealing to the target audience.
The problem was that for some reason, many people were excited when they subscribed to the site and then a year later they did not renew. I was able to predict which accounts were not likely to renew using retention metrics. The sales force was given this information prior to renewal date, and was able to allocate their precious time towards working only the accounts most likely not to renew. The result? Renewal rate jumped by 30%.
If all this retention stuff sounds a bit like CRM to you, well, it is. But it’s built on sensible, simple and reliable metrics that are now easier than ever to get your hands on, thanks to the next generation of web analytics tools.
Over the next several months we’ll be exploring the whole area of visitor / customer retention and the metrics involved, so that when the time arrives and people start asking “what’s next”, you will be ready.
Jim Novo has nearly 20 years of experience using customer data to increase profits. He is co-author of The Guide to Web Analytics and author of Drilling Down:Turning Customer Data into Profits with a Spreadsheet. If you want more visitors to take action on your web site, try using the free conversion metrics calculator, downloadable here. If you need to sell more to customers while reducing marketing expenses, get the first nine chapters of the Drilling Down book free at http://www.drillingdownbook.com.