Many marketing teams are missing out because they think that conversion rate is just one metric. It’s not.
Conversion rate (CR) is the Swiss army knife of metrics, one which can morph and change to measure a variety of different elements of your marketing program. Even better, conversion rate is relevant no matter what your end goal:
- Lead generation.
- Direct sale.
- Eyeballs for advertising.
- Engagement.
- Acquisition.
- Retention.
- Or something else.
The key to success is to broaden your definition of what a conversion is, define a formula for each instance, and then leverage it at all stages of your program. Here are four examples that I use with my consulting clients.
1. Conversion rate for email list growth
For most companies, their website is ground zero for email list growth. But few are quantitatively measuring how successful their efforts here are. Enter ‘email list growth conversion rate.’ The formula I use for this is:
Why do I use new, not returning, website visitors? Because many of my client’s sites are build for return traffic – think people who come here to MarTech to read the articles a few times a week or maybe even daily. Many return visitors are already on the email list – by looking at just new website visitors we weed them out, and it gives us a good estimation of how effective the website call-to-action to subscribe is.
Use case: This version of the CR metric is my go-to KPI when we’re testing copy, creative, location, and other elements of an email opt-in on a website with a goal of boosting performance. But even if you are not testing, this is a metric you should watch.
2. Step-by-step conversion rates
It’s good to know how successful a marketing campaign is. It’s even better to understand just why it’s working. By using ‘step-by-step conversion rates’ you get an understanding of how many prospects moved forward at each step – and how many didn’t. Here the CR formula is:
You calculate the CR for each step, then put them together in a journey map to identify and address areas of friction along your journey. Here’s an example:
By looking at the CR from the previous step, you can see that the place we lost people is between the landing page (213,331 visits) and people adding the product to their cart (1,024). Only 0.5% of people who got to the landing page went on to add the product to their cart. The weak link in this journey is the landing page – that’s where I’d begin to look for ways to boost bottom-line performance.
Use case: By calculating the conversion rate at each step, you can identify what’s working (high conversion rate) and what’s not (low conversion rate) at a granular level. This allows you to ‘do no harm’ to what’s working, while you tweak what’s not.
3. Conversion rate for propensity
This is another riff on the step-by-step analysis we discussed above. Sometimes there’s more than one path for a person to take and looking at the CRs for each option all the way through to the final conversion, can tell you which is most successful. Here’s the formula:
To illustrate this, I’ve taken the bottom of the sample journey map we used above and added more detail on:
- Unique clicks on the landing page links.
- Which landing page links had been clicked on by those who completed the checkout journey.
Here it is:
If you look at the last row on this map you’ll see which links those who ended up purchasing engaged with on the landing page.
The split between those who clicked on the top-of-page CTA (roughly 24%) versus the bottom-of-page CTA (roughly 76%) is interesting. It shows that people needed the content between those two CTAs to motivate them to add the product to their cart and complete the purchase.
But more interesting and valuable is the percentage of those who purchased who had viewed the video — 71%. That suggests that those who view the video are more likely — have a higher propensity — to buy.
When this happened in real life, with one of my clients, we first tested moving the video higher on the page, to make it more prominent — and we saw a boost in bottom-line performance. Then we moved it to the email message and sent people who clicked on it here to watch, and got an additional lift.
Use case: By doing this type of conversion rate analysis, you can often identify elements of a journey, like watching a video or viewing a certain web page, which increase the likelihood of the final conversion happening. Once you know that, you can rework the journey to entice more people to engage with that element.
4. Conversion Rate with revenue-per-email and average order value
If the final action you’re looking for involves revenue, your key performance indicator should always be a revenue-based metric; I usually use revenue-per-email (RPE). But when I do the analysis, I make sure that I also include the global CR, which I define as:
I also include average order value (AOV). Here’s an example of the three in a sample reporting table for an A/B split test:
Using RPE as our KPI, both of the test versions beat the control. But when we include CR and AOV in the analysis, you can see that Test A bested the control due to a higher AOV, while Test B did it with a higher CR. So, which do you choose as the winner and new control? It depends on your business model. But I know what my next test is – it’s combining elements of Tests A and B to see if I can create a version that drives a higher CR AND a higher AOV to best whichever is my new control.
Use case: by looking at all 3 metrics, you can understand not just which version won, but also why it won. This knowledge can then be leveraged in the future, to continue to optimize performance.
Bottom line? Don’t underestimate the value of conversion rates.
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