What is optimization of conversion frequency?
Conversion frequency optimization (CRO) is the process of increasing the percentage of visitors taking a desired action (purchasing, signing up for a free test, booking a demo, etc.) on your website or mobile app.
This is important because converting more visitors to leads and customers can grow your business. And can be calculated by sharing your number of conversions with the number of visitors and then multiplying it by 100.
This is how the formula looks like:
Cro often involves:
- Tracking where visitors come from how they navigate on your site and where they drop off
- Conducting surveys and A/B tests to know your audience needs, preferences and pain points
- Improving site design, content and call to action (CTAs) to create a better user experience that drives conversions
Let’s say you’re running an E -trading store with the following statistics:
- Monthly visitors: 100,000
- Current conversion frequency: 2%
- Average Order Value (AOV): $ 50
With these numbers your current monthly revenue: 100,000 x 0.02 x 50 = $ 100,000
Now imagine that you are increasing your conversion frequency from 2% to 2.5% by implementing some CRO techniques.
Your new monthly revenue will be: 100,000 x 0.025 x 50 = $ 125,000
It’s one 25% increase in revenue—En extra $ 25,000 per Month—Tres without spending extra money on acquisition of traffic.
How to make conversion frequency optimization in 6 steps
Here is a six-step process that helps you optimize your conversions:
1. Set your conversion goals
Start by identifying important conversions that are in line with your business goals.
These may vary based on your niche.
For a company software company, increased demo requests can be a conversion target. While an E trading business could look at orders as a conversion target.
When you have a clear idea of your target conversions, add them as key events in Google Analytics 4 (GA4) to create conversion tracking. (Read our detailed guide to setting up GA4 conversion tracking to learn more).
Then check your current performance.
If 1,000 people visited your site and 50 of them converted your conversion frequency: (50 /1000) x 100 = 5%
Now compare your current conversion frequency with the industry’s benchmarks to get a sense of how to manage.
For example, the average conversion frequency for E trading stations is 4.2%. But for legal and financial services, it is 6.3% and 8.3% respectively, according to UNBOUNCE’s conversion -benchmark report.
If your conversion rate is below the industry’s average, there is probably room for improvement.
2. Identify where the users will let go of
Then use Custom Trag Reports in GA4 to analyze your users’ travel, the steps they take to perform specific tasks on your site, and the number of visitors falling in between these steps.
This will help you identify the pages or steps of your funnel where users leave without conversion.
Some examples of pages that you may want to be aware of include your website, pricing pages, checkout pages and landing pages.
Also analyze these key metrics in your GA4 to understand the important factors that affect your conversion frequencies:
- Bounce Rate: This is the percentage of visitors traveling without engaging. Pay attention to the rejection speed on your landing pages and product pages. High rejection speeds on these pages may indicate that you need to improve your messages and/or user experience.
- Carriage Degree: This tells you how often users leave their shopping carts without buying. Analyze League Rates and Kassing Steps to identify friction points (eg high shipping costs or long forms).
- Time on page: Make time on the site could indicate that users do not find your content valuable. Or that they are distracted by other elements on the page.
- Ridge: Tracks how far users roll to see if they reach important elements such as CTAs or forms. If not, consider adjusting the layout.
It is a good idea to use tools like Hotjar or Crazy Egg to more fully understand how users interact with your site. And how accurate they face problems.
You can use these insights when creating hypotheses for your CRO experiments later.
3. Collect data about your users
Once you have identified your delivery points, collect user data to find out what could cause users to leave your site without converting.
You can get this information through online studies or feedback forms. Ask questions such as:
- What prevented you from ending your purchase today? (To get direct feedback on questions such as unreasonable pricing, lack of confidence or bad usability.)
- Was there something confusing or frustrating about the process? (To uncover pain points in navigation, payment gateway, etc.)
- Was there anything you were looking for today that you couldn’t find? (To identify holes in your content or product offer.)
- Have you experienced technical problems during your visit? (To identify errors that may interfere with the buyer’s journey.)
- On a scale of 1-10, how easy was it to finish your goal on our site? (To quantify the overall user experience on your site.)
Then collect information about your larger audience.
Collect this data easily using the One2target tool.
Start the tool, enter up to five domains in your niche, and click “Analyze. “
(Your target audience is probably very similar or the same as your main rivals).
It, ”Demographics“The tab shows information such as age groups, gender and location.
Go to “Socio -economy“The tab to find details about your audience’s average household size, employment status, income level and level of education.
Click “Behavior“The tab to gain insight into users’ favorite devices, websites and social media platforms.
Use this information to refine your messages.
Let’s say that data shows that most of your audience members fall within the age group 35-44, lives in a household with three to four people and works full time.
This probably indicates that a large part of your audience consists of working parents. So you need to tailor your messages to meet their needs and preferences.
4. Develop hypotheses
Based on the insight you have gathered, identify a problem you want to solve and create a hypothesis to serve as the basis for your conversion speed optimization experiment.
Here’s what you need to create a hypothesis:
- Problem statement: “Users abandon wagons because the box since requires them to register first”
- Suggested solution: “Add a Guest Check -Setting on Schect -Side”
- Expected result: “This change will improve purchase conversions by 2%”
Based on the above information the hypothesis would be:
“Offer of a guest check option along with the standard registration process during checkout, the shopping cart To increase the purchase conversions by 2%.”
Here are some broad ideas to consider for different pages:
- Website: Communicate better your value proposition and provide easy navigation
- Landing pages: Hold landing pages relevant to users’ search intention or your ad campaign, and minimize the form fields
- Product or service pages: Highlight benefits in terms of features, use high quality visuals and show customer reviews to build trust and credibility
- Pricing page: Simplify the layout, clearly outline the price levels and features and includes trust signals such as testimony and warranties
- CTA buttons: Make sure buttons stand out and test the text, color, size and location
- Blog pages: Add targeted CTAs (eg
5. Run an A/B -Test
The next step is to perform an A/B test (also known as a shared test), which allows you to compare two variations of a page or element to see what works better.
You have to design a test page with two variations (ONE and B) to see the impact of your proposed solution.
Variation A (control) is the existing live page on site. And variation B (test) is the new page.
If we use the same example hypotheses from before, these will be:
- Variation a: The existing checkout process that requires users to register or log in to complete their purchase
- Variation B.: A changed checkout process that includes an opportunity to buy as a guest without registration
Push both sides live and implement a 302 redirection that randomly redirects users to each of the two versions (you may need to work with a developer for help).
Alternatively use a tool that takes care of the technical details for you. Like the app Landing Page Builder.
This tool makes it easy to create a destination page and duplicate it to run A/B tests.
Run the experiment for a few weeks – depending on the size of your audience and expected traffic to that side.
For smaller places with fewer visitors, you may be able to extend the test period to collect enough data for statistical significance.
If you have a high -traffic site, you may be able to collect adequate data in a shorter time frame.
6. Sporer results, iterere and improve
Monitor your A/B results to see how conversions are affected.
If the results are in line with your hypothesis, you can consider your A/B test as success. And apply what you learned.
If the results do not support your hypothesis, analyze other measurements to gain insight into what is going on. And use these insights to refine your hypothesis for the next test.
Note that Conversion optimization is an ongoing process. Which means you will continuously test.
It is also a good idea to keep an overview of your experiments and the results to inform future optimization of conversion frequency and marketing efforts
Get started with Cro by understanding your audience
Optimization of conversion frequency requires continuous iteration and improvement on your site to customize it to your audience’s wishes and needs.
Which means thorough audience survey is the key to a successful CRO strategy.
Semrush’s One2target tool provides information about your audience preferences, behavior and pain points to help you gain insight that you can use to operate more conversions.