Whether you’re just getting started with ecommerce or already a seasoned professional, understanding and improving your conversion rate is critical to growing your ecommerce business. There are a ton of buzzwords around conversion optimization (look, there’s another one!), so we’ve put together a post breaking down what conversion rate actually means and how to run tests to improve it.
The conversion rate formula
Want to know how to calculate your store conversion rate? Here is the simple formula:
Ecommerce conversion rate = (Total store transactions/Total visits to website) * 100
Here’s an actual example:
(10 transactions / 400 visits) * 100 = 2.5% Conversion rate
The simplest way to think about conversion rate is this: Your conversion rate is the number of visitors that come to your site that actually take an action you want them to take, whether that is signing up for your email list, filling out a form, or making a purchase from your site. If you have more than one of those are goals for your website, you can track them individually as well as on the whole.
- Subscriber conversion: Unique visitors / # of new subscribers
- Shopper conversion: Unique visitors / # of new customers
- Total site conversion: Unique visitors / # of people with any conversion*
To effectively grow, each of those rates is important to the success of your business. That means you should be putting strategies in place to improve each of those numbers individually through A/B testing or sequential testing. And as you get better and better, you can add metrics like the rate of subscribers who become customers, repeat visitor conversion rate, and more.
Note: If you are calculating a total conversion rate, don't add the totals of customers and subscribers. You should be looking at the number of people who have done one or both of those things, but be careful not to double count.
What is the average ecommerce conversion rate?
There is good news and bad news in answering this question. First, the bad news. Industry data shows that an acceptable conversion rate is between 1% and 3%. That means that the vast majority of people hitting any website leave without taking any meaningful action. It’s a little sad, but also provides great context. If you’re expecting a 50% conversion rate, you’ll need to reset your expectations, but it’s not unheard of to make significant leaps and bounds by investing in conversion-focused strategies. In fact, we see it all the time.
One example of this is a company called Project Repat. Their business is pretty cool. You ship them all of those old t-shirts that don’t fit but you refuse to throw out, and they turn them into a cozy, blanket full of nostalgia.
After being in business for a while, they realized that their service was difficult to sell in one site visit. But they were paying an arm and leg to drive traffic so they built some educational nurturing emails and decided to make their primary first visit goal an email capture. When they shifted that focus, and targeted each referral channel with relevant messaging, they started turning 12% of traffic into subscribers.
They didn’t stop there. When the nurturing emails drove subscribers back for purchase, they added some additional on-site displays based on how much value was in a visitor’s shopping cart and were able to reduce the number of carts that would normally abandon by 10%.
That's the type of conversion rate improvement that's possible when you start systematically testing your way to success.
How to measure conversion rate (in Privy)
Once you've invested time in building out your onsite conversion campaigns in Privy, you'll want to see how each individual campaign is performing and how you are performing across all of your campaigns. Below are six of the metrics that our users find most helpful in improving their on-site conversion.
- Sign up rate: This shows you what percentage of people who see your campaigns are signing up. If your rate is low, you may want to check your offer, adjust your template, or modify your targeting.
- Redemption rate: This metric shows the percentage of people who have received a discount code and actually made a purchase. To improve this number, you may want to test multiple offers and see what performs best.
- Sign ups by display type: Not sure whether you want to be using pop ups, flyouts, banners, or bars? Comparing the sign ups of each display type can help you decide what type of campaign to build next.
- Sign ups by trigger type: Wondering if exit intent is driving more sign ups than a timer or scroll trigger? Using this metric, you can see which triggers are most effective at driving sign ups across one or all of your campaigns.
- Link clicks for each campaign: If you’re using “no form” campaigns or just including links in your displays, you can see which links are performing best for one campaign or across all of them.
- Sign ups by source: This metric shows you where the most qualified traffic to your site is coming on based on the UTM Source. If you’re spending money to drive people to your site, you can use this information to invest more or less based on the results.
Conversion testing basics
Before you jump into the deep end of the testing pool, there are some important things to think about that will help you understand your results and what to do next.
Develop a hypothesis
The first thing to do before launching any test is to come up with a hypothesis. Basically, what do you think will happen and how will you go about proving it?
For example, imagine testing out a button color. You may have the hypothesis that the blue button will drive more conversions than the green one. This is important because it forces you to have a reason to run a test instead of saying, “it would be interesting to know…” You’re going to reach a firm point of view once you have the data in hand.
With that hypothesis in mind, you can then have clear decisions that you know you’ll make ahead of time depending on the results you get. For example, if the blue button wins, you will change all of the buttons to blue in your pop-ups.
When you’re thinking about any test, it’s also important to limit the number of variables to one. If
you change a bunch of elements in your campaign all at once, you’ll never know exactly what drove the change in results. We then need to dive into multivariant testing. For example, if you’re running that same button color test, but you also change the copy of your pop up, how will you know whether it was the button or the copy that drove the difference in results?
Does that mean you can’t test full sets of creative (image + color + copy) against each other?
No. It just means you need to be conscious of what you are learning and how you apply it to other related items. So, you can now that one full pop up performed better than another but you’ll want to avoid taking a single element of the pop up, like a button color, and making the leap to site-wide changes.
Data purists will tell you that the only reliable tests are ones that are statistically significant. That basically means that enough people have been a part of the test to make the results relevant to a broader sample. You can read way more info about that here. The important thing is that whenever possible, you want a large sample set that can reach the point of being trustworthy.
Unfortunately, most of us don't have the volume of web traffic that make running those types of tests practical. That's totally fine—you can run directional tests instead that can still be incredibly valuable, even if not 100% reliable.
Think about it this way. Would you be better off asking 30 friends a question to see what a majority of people think, or would you rather just trust your gut? While the results of that questioning might not be bullet-proof, they certainly should help shape your opinion about what to do next.
A/B tests vs. sequential tests
If you’re new to the testing game, you might be wondering what an A/B test is. It’s actually very straightforward. In an A/B test, you create two versions of something, like a pop up or landing page, ideally with only one variable changed, and split your web traffic at random to send a certain percentage of people to one page vs. the other. Then, you evaluate which version drive more conversions and pick a winner.
The great thing about an A/B test is that it automatically accounts for all other factors because the only difference between one set of visitors and another is what they are seeing on your site. The time of year is the same and your offer is the same. The weather is the same. You get the idea. You’re limiting the outside influences that impact the results of your test.
Sequential testing, on the other hand, means that you are doing one thing for a period of time.
You make some changes and leave them for the same period of time. Then, you compare the results. This is easy to execute, but harder to correctly analyze, because any number of other things could have impacted the results that are out of your control.
SO, WHICH IS BETTER?
In a perfect world, we would all be running statistically significant A/B tests and we’d be learning and improving rapidly. The next best scenario is to run directionally valid A/B tests. The last choice (that's still way better than nothing) is to run sequential tests. You can still learn a lot if you combine your instincts with the results.
Six ecommerce conversion rate tests worth running
Whether you are running A/B tests or sequential campaigns, there are a number of campaign elements worth testing to figure out what works best for your site. The six below are just examples of common tests you can try right away. By continually testing variations of your campaigns, you can optimize them even further.
- Trigger timing: For example, test whether a 10-second time-based trigger converts at a higher rate than 30-second time-based trigger.
- Display type: For example, test whether a pop up converts at a higher rate than a flyout for your mobile visitors.
- Offer: For example, test whether free shipping on orders of $50 or less converts at a higher rate than a 10% discount.
- Headline: For example, test whether a straightforward headline converts at a higher rate than a light-hearted one.
- Button color: For example, test whether a blue submit button converts at a higher rate than a red submit button.
- Background image: For example, test whether using a background image converts at a higher rate than a plain color background
Whether you start with one of these tests or something completely different, it's a great idea to map them out for the next few months so you're always learning and improving your site.
How to increase your conversion rate for ecommerce
Taking what we have discussed above to continually test your hypotheses will lead you down a road of increased conversion rates.
Note that increasing your conversion rate may take time, and that is part of the process. You can start at one part of your marketing funnel that you feel is the most important and continue this process for each part. Allow yourself and your business the needed time to experiment with tests, see results, and then make adjustments.
Utilizing Privy as your conversion rate tool with the tests listed above can provide you an easy and quick start to optimizing your store for more sales.
Ready to get started improving your conversion rate?