• Ann Njuguna

How to do Conversion Rate Optimization: The 6 Steps of CRO for Online Businesses

In the sequel to the previous article, we’ll focus on how to do Conversion Rate Optimization in more detail. But before we get to the interesting part, here is a recap of the previous article where we had an introduction to Conversion Rate Optimization.


We learned:

  • What Conversion Rate Optimization is,

  • The types of businesses it can be applied to

  • How to know if your business needs Conversion Rate Optimization

  • How to get started with Conversion Rate Optimization

In case you missed this introduction, catch up on this quick read.



The 6 step CRO process

In the previous article, we mentioned six steps that are involved in a comprehensive CRO process, these are:

  1. Analytics Configurations

  2. Analytics Audit

  3. Website Audit

  4. Data Analysis

  5. Behavior Analysis

  6. A/B testing


Let’s go through each step in more detail to learn Conversion Rate Optimization.




1. Analytics Configurations


This step applies to online businesses that are yet to set up their analytics tracking. Because Google Analytics is widely used, I’ll use it for demonstration in this article. However, other platforms such as Segment.io, HEAP Analytics, or Mixpanel can also be used for data collection.


In this step, the most important thing is to create a tracking plan that aligns with the business key performance indicators.

For an ecommerce store, these include but not limited to:

  • Sales

  • Average Order Size

  • Gross Profit

  • Number of Transactions

  • Conversion rate

  • Shopping Cart Abandonment

  • New Customer Orders vs. Returning Customer Orders

  • Cost Of Goods Sold (COGS)

  • Product Affinity (Which products are bought together)

  • Product Relationship (Which products are viewed together)

  • This information helps in cross-selling products

  • Customer Lifetime Value

  • Revenue Per Visitor

  • Churn Rate

  • Customer Acquisition Cost


Once the tracking plan is set up, then analytics implementation can be done.


Note:

To get all the above KPIs tracked, more than one analytics tool may be required.


Recommended tools

For this step, the most common tools to use are:

  • Google Analytics

  • Google Tag Manager

With Tag Manager, you can also integrate data from other platforms like marketing-based platforms.




2. Analytics Audit


This step applies to users who have already configured their analytics and are collecting data.

For those who start the process at step 1 (analytics configuration), it is best if they also audit their setup as they implement the configurations. In this case, you cover both steps 1 and 2 of the Conversion rate Optimization process.


You might be wondering why this is important while data is already being collected. This is because we need to make sure that we are collecting reliable data before we start optimizing. If we have insufficient or unreliable data as a result of poor analytics configurations or, in some cases, missing data, we will base our decisions on the wrong data and we also won’t be able to track progress effectively.


Since this is an essential step in getting everything we need for better decision making, we’ll focus on how we can audit the Google Analytics configuration and also some of the issues that can be resolved as a result.



A few cases where Analytics Audit is required

Example 1 - Duplicate transactions

This refers to cases where a single transaction is tracked more than once. The challenge with this is that it inflates the revenue and number of transactions, which gives a false impression on the performance of the business. One way this can occur is if the page users are redirected to after purchase, which also has the tracking code, loads the code more than once. It could also be caused by another reason entirely. Therefore, investigation to determine why this is happening is important.



Example 2 - Incorrect shopping funnel data

We have cases where some steps that appear later in the shopping funnel have more sessions than those that appear early in the shopping funnel. For instance, in the screenshot below, we have more checkout sessions than the add-to-cart sessions. This is strange because usually, this doesn’t happen.



On further investigation, we discovered that at some point, the add-to-cart event stopped working, causing a drop in the number of add-to-cart sessions for the selected duration.



An audit process helped identify when this issue occurred and further investigation was done to resolve the issue.


Note:

It is advisable to add an annotation in Google Analytics to explain cases where data was compromised or when a UX change is done during the Conversion Rate Optimization process. This helps explain the changes in data.




Example 3 - Bot traffic

Google Analytics has a feature to filter against known bot traffic. When this feature is not enabled, we have cases where data consists of bot traffic, which inflates the number of sessions, affects bounce rate/exit rate, and conversion rate. Due to this, we’ll never clearly know the website’s performance where these metrics are concerned.




Example 4 - Filters for internal traffic

For all online businesses, a number of people are involved in maintaining the website and these people frequently access the website to resolve things or check that everything is alright. If their data is not filtered, we’ll have cases where we have sessions that are not from real users, and this also affects the real performance of the website. Google Analytics has a filter setting option to filter users by IP address. Also, in cases where the online business deals with clients from only one country, then a filter can be created for users from only that country since they are the real target audience for the business



There are many more cases and an audit document guide is required so that everything is sorted. Here is a document you can use to guide you through the audit process.




3. Website Audit


Now that we have clean and reliable data, let’s focus on the 3rd step of the Conversion Rate Optimization process.

In this step, we look at two things:

  1. The low hanging fruit

  2. Hypothesis for A/B testing


Low hanging fruit

The low-hanging fruit is about the conversion rate optimization guidelines or recommendations for the different online businesses. For an ecommerce website, we have things like a sticky menu, consistency in colors for all the call-to-action buttons across the website, floating add-to-cart buttons, a search module among other things.


These are among the things that are a standard for ecommerce websites and they can be implemented even without the need for an A/B test. In this case, we go through the entire ecommerce website to figure out if all the recommended standards have been implemented.


Hypothesis for A/B testing

A hypothesis is a prediction you can make before running a test. It states the kind of change you want to make, why you want to make it, and its expected result.

In the website audit step, we also assess potential funnel leaks, what the user experience is, a product story, and how it is being told.


Here is an example of a hypothesis:


Problem

The mobile visitors have a greater scrolling distance and view little information above the fold. It could be that mobile users are not accessing the most important information as easily as desktop users.


Solution

We could have the most important elements above the fold for the product details page, these include:

  • Product name

  • Product image

  • Product price

  • Rating

  • Add-to-cart button

Here is how you can visualize the solution if you are not using wireframes to create the variant design.


Result

The expected result is an increase in the number of mobile users who add items to the cart, which results in more users moving further down the funnel and finally, increasing revenue.


With this hypothesis, you can then use data to get more information to support it.


More examples like this can be hypothesized at this step, then, we get to step 4 (Data Analysis) here we get more data to support our hypothesis.