Why You Need A CRO Roadmap? A Complete Guide

1.Define your goals

Before running a test, ask yourself–what are you looking to achieve through this experiment. Instead of vague goals like ‘improve CTR’, go for SMART goals–Specific, Measurable, Achievable, Relevant, and Time-bound. 

For example: 

❌Increase conversion

✅Increase conversions by 10% within the next three months

❌Reduce abandonment rate

✅Reduce abandonment rate by 20% by optimizing for mobile UX

Also remember, your goals should align with larger business KPIs. For instance, if you need to boost your revenue by 20% in the next 6 months, your experiments must be targeted around the same; you must avoid wasting time on random vanity metrics like click-through rates and more in such cases.  

2.Audit past experiments

You don’t have to start from scratch to build a good CRO strategy or roadmap. Analyzing past experiments should give you a good start most of the time. 

Pull reports from your A/B testing platform and look for trends. Did personalization tests outperform generic messaging? Did reducing form fields boost conversions or hurt leads? 

Further, you can classify your past experiments into three categories for better analysis

• Winners: Implement immediately and look for expansion

• Losers: Analyze why they failed–poor process or wrong hypothesis

• Inconclusive: Likely resource exhaustive, best to avoid. Can rerun with better processes

Remember the goal is to learn from previous tests rather than reinventing the wheel each time. 

3.Gather data and identify bottlenecks

Your best ideas won’t come from guesswork–they’ll come from data. Start with quantitative data–

• High exit rates on the pricing page? Users may need more clarity

• Low CTA engagement? CTA may not be visible or misplaced

Once done and identified, look for qualitative data–

• Heatmaps–To see where users are hesitating

• Session recording to analyze user behavior

• Customer feedback for friction points

Now, combine both datasets to identify problematic areas and optimize for them. 

All of this and more is made easy by Fibr AI’s experimentation expert–Max. With Max, be rest assured that your page is always performing best–without any manual effort. 

Whether it’s data driven optimization, or on point hypothesis generation, Max optimizes for element 24/7, delivering better engagement and CRO numbers. Don’t wait, try our CRO agent Max today and forever transform your experimentation process. 

4.Prioritize tests based on impact

Some tests will move the needle while others won’t. To avoid the latter, you can use a scoring model like ICE (Impact, Confidence, Effort). For instance, take a look at the table below–

| Hypothesis                          | Impact (1-5) | Confidence (1-5) | Effort (1-5) | Score |
|-------------------------------------|--------------|------------------|--------------|-------|
| Changing the color of the CTA button| 3            | 4                | 1            | 2.6   |
| Add video on the product page       | 4            | 5                | 4            | 4.3   |
| Add testimonials                     | 3            | 3                | 2            | 2.6

Your aim should be to find the combo of low effort and high impact and maximize it.  

5.Create a testing timetable

Without a clearly mapped-out timetable, you risk overspending resources and experiments getting delayed. Define clearly:

• Start and end dates, budget

• Hypothesis

• Traffic allocation 

• Metrics to track (conversion etc)

• The main team/person overseeing the experiments

The timetable is also important to understand conversions under situations like holiday sales and more. For instance, redesigning your entire landing page during peak season sales can result in distorted results; so plan accordingly. 

6.Set up reliable testing infrastructure 

Ensure that you have a solid testing structure in place–think tools, tech, and everything else required for clean and neat experimentation

A mini, starter checklist would be to–

• Ensure goal tracking is set up correctly 

• Testing for flicker effect (a brief flash of the original content before the variation)

• Confirming sample size is statistically significant. We speak about it in our blog here. Do check it out. 

This is a small list, but you do get the gist, right? Fix your infrastructure before running tests. 

7.Establish clear hypothesis

Your hypothesis is literally the pillar of your testing processes. Avoid random guesses. Make use of the data available to you to establish a strong hypothesis. 

Example of a bad hypothesis:

❌ Abandonment rate is high due to high price.

Example of a good hypothesis:

✅ ‘Heatmaps show that drop-off is high around payment options. Let’s add a 10% cashback offer to encourage action. Can also add a few more payment options to enhance customer experience.’

Do you see the difference? A good hypothesis uses data, user behavior, and logical reasoning, ensuring the experiments are fruitful. 

8.Run tests and monitor results

Don’t set up tests and forget about them. Monitor each move to catch anomalies early. Some key things to check could be:

• Traffic behavior

• External influences (holidays or anything else interfering with the results)

• Early trends (Are conversions fluctuating widely? Is it more during the night than day?)

Let the test run at least 2-5 weeks, unless your traffic is extremely high. 

9.Analyze, document, and learn

Once the tests are done, dig deeper into the data, Try and understand the ‘why’ behind the results 

• Was the hypothesis right? Why?

• Did the variation outperform the original version? Why?

• Were there any unexpected results? Why?

Analyze and store your results to build a knowledge bank. Over time, you’ll notice that this process speeds up decision-making and improves experiments.  

10.Iterate and optimize

Winning tests are not an endpoint–they are in fact the starting points for the next round of experiments (refer to step 2). So when a variation works, ask

• Can this be improved further?

• Does this work for all user segments

• What happens if we test another element alongside this? 

For failed tests, figure out what went wrong and why–maybe the hypothesis was not right, or the execution was off. Adjust and try again.