There are certain metrics we use by default, because everyone understands them, and because they reflect the health of a website rather simply: Visits, Visitors and Page Views.
Other complementary metrics then follow, which attest to visitor engagement, such as conversion rate, bounce rate and time spent.
But what if I told you that in addition to being too generic, these latter metrics could actually cause faulty interpretation of the reality of your digital activity?
Let’s focus on conversion rate, which is sometimes responsible for triggering hysteria in cases of significant variations.
How should conversion rate be calculated?
No matter your industry, your website is a tool enabling your customers and prospects to engage in a relationship with your brand. You are thus providing them with an additional channel in the hopes of achieving one or several goals that will lead them to the ultimate goal: the purchase of a product or a signup for a service.
The way to track this indicator has always been conversion rate, and its calculation is rather simple:
Conversion rate =
|Visits that reached the goal||
It’s quite simple to track the performance of your site via a single indicator. If my site records 154,789 visits over the course of one day, and amongst these visits, 4,362 reached the goal I’ve set, the conversion rate is therefore:
Conversion rate =
x100 = 2.82%
Why conversion rate interpretation is debatable…
This method of calculation is interesting if we expect concrete engagement from our visitors each time they come to our website. If we take the example of social networks, we might expect to see a goal (publishing a tweet, commenting on a Facebook post, etc.) reached with each visit, at least for the most engaged people amongst the audience.
Nonetheless, for the majority of other types of sites, a goal conversion is rarely achieved upon each visit.
This is why, as an analyst, conversion rate bothers me. The objective of a rate is to strive for 0 or 100. So knowing that my conversion rate is painfully wavering between 2% and 5% tends to be depressing.
And if we keep things to a visit scope, our fate will always be despairingly low conversion rates.
In my view, there are 3 reasons why conversion rate is less meaningful today:
- Who really believes that the act of purchasing is decided and concluded in one single visit? Given the multiplicity of offerings available today, customers are increasingly fickle and less loyal, and need to compare to feel reassured before agreeing to buy. It takes a customer several visits to go from the consideration stage to the actual purchase.
- (Spoiler! I’m going to state the obvious here.) Our world is more and more mobile. Tablets, and smartphones especially, play a more significant role, even surpassing computers in terms of traffic in France (source: AT Internet data). The pathways between your visitors and your brand have become complex and clearly cross-device in nature. This therefore represents even more visits that should be added to the denominator of your conversion rate calculation.
- The purchasing cycle is an approach that we too often neglect. Your potential customers may come to your site to see what’s new with your company, or to browse your offerings for information purposes, without necessarily intending to buy. Depending on what you sell, your visitors don’t need to purchase during each visit.
Think “visitor-centric” for a conversion rate close to 100%
I can already hear the cries of protest from those of you who use this metric on a daily basis to justify your site’s performance to management. What if I told you there are other conversion metrics that are more meaningful, which near 100%, as a real conversion rate should?
To analyse your website, and to account for the bias of conversions that require several days or several visits, you can take, for example, a visitor-centric approach, rather than a visit-centric approach.
Let’s look at the below site, which shows a conversion rate of 2.4% of visits.
By taking the Visitors conversion rate based on cookies, we see this conversion rate lift slightly to 3.9% (+1.5pts, or a 62% increase).
It’s also possible to restrict your analysis to Identified Visitors – those who have logged in to your site at least once during the analysed period.
This means that during the period analysed, I was able to convert 44.8% of my users! We’re finally reaching a conversion rate that makes some sense.
What about conversions as they relate to your brand?
We mentioned earlier that today’s visitors now take a cross-device journey. Analysing a site or app’s conversion rate without accounting for this cross-device factor can therefore skew your analysis.
Here we see the conversion rate based on visits for my brand, which has a responsive site and two mobile apps (iOS and Android): 1.4%
Here as well, a visit-based prism reduces the relevance of our analysis. We’d be better off basing our analysis on visitors, despite a major constraint: web visitors are generally identified using cookies, whereas they are identified on mobile apps using IDs generated by their mobile phones. This means I have no common key to tell me that a certain cookie on my responsive site corresponds to an ID on my app: 6.9% of visitors converted
Let’s therefore focus on one portion of our audience: Identified visitors.
Here, we can clearly see that the overall conversion rate for my brand is 27.21%, which means that no matter the platform used, I was able to convert 27.21% of my identified visitors.
What about the purchase cycle?
When analysing a rate, we often forget to think about the period. Analysing the evolution of a conversion rate from month to month, or from week to week, is quite arbitrary.
If we take the approach of visitors’ conversion rate, your industry surely has an impact on your customers’ purchasing cycle:
- Office meal delivery service: You might hope to convert clients once per day
- Drive-through grocery pickup: Your customers might make a purchase every 5 to 10 days
- Flash sales on retail goods: You might expect customers to purchase once per month
As you can see, using the same frequency of analysis for these 3 different industries would not be relevant.
By changing the granularity or the period of your analysis, you’ll be able to track the activity of your potential customers with greater precision.
Daily granularity (delivery service)
Weekly granularity (drive-through service)
Monthly granularity (flash sales)
This is the same data set, but the granularity of the analysis gives us different angles.
Don’t throw away everything you’ve done up until now – just think differently. With the analyses available in User Insights, which are user-centric, you’ll be able to explore new possibilities that can help you better understand your users. In short, be sure to adapt your metrics to your needs.