In the following article, I will introduce you to what Conversion Rate Optimization is in e-commerce, how tests work to boost the conversion rate and how to use the dedicated Google Optimize for this. The conversion rate boosting concept is integral to online sales. With rising marketing and CPC (costs-per-click), we are dreaming of increasing revenues without continually raising marketing expenditures.
Conversion rate formula for e-commerce
The conversion rate is the quotient of the selected action (in e-commerce, transaction completion) to page views multiplied by one hundred per cent.
CR (Conversion rate) = (number of purchases / number of users) x 100%
Conversion Rate Equation
What is E-Commerce Conversion Rate Optimization?
Analysing the above equation, to increase the conversion rate, the number of purchases from one hundred user sessions should be higher. Thus, Conversion Rate Optimization is an operation aimed at ‘convincing’ users who are already on the site to complete a transaction.
At first it may seem unnatural if we have 100 users on the store and only 10 of them have a strong intention to buy, how can we convince that 11th customer to complete the transaction?
This is why the discipline of CRO (Conversion Rate Optimisation) has emerged, which, through a series of streamlining of the entire purchasing process, drives customer purchase intent and thus boosts the conversion rate.
E-Commerce Conversion Rate Optimization Strategies?
Here are some of the key conversion rate boosting strategies. These are general, repeatedly recommended, and tested items of an online store. The following examples will address trust, price, social proof, facilitated transactions, content marketing and online store technology.
1. Local payment implementation
2. Deferred payments (e.g. Pay later with Klarna)
3. Quick buttons payments (np. PayPall, ShopPay, GooglePay, ApplePay)
4. Payment icons (To instil a sense of security during a transaction)
Delivery and returns
5. Local delivery option (e.g., InPost parcel lockers – Poland, DPD – UK)
6. Fast delivery lead times (e.g., 2 – 3 working days)
7. Pro-consumer return option (e.g., free, and extended returns, high complaint acceptance rate)
Trust and social proof
8. Adding product reviews when adding a product to a shopping cart
9. Store security (SSL certificate, payment icons)
10. Customer feedback module
Checkout transaction processing
11. Tested and very easy in Checkout transaction processing
12. Adding a progress bar to Checkout
13. Option to process transactions as a guest
14. Abandoned shopping cart (email or exit-pop)
15. Optimized page refresh rate (e.g., headless technology)
16. Optimized website for mobile (mobile index first, mobile traffic boost)
17. Enhanced and UX friendly filtering options (facilitated search for interesting information)
18. Tested design and placement of CTA (Call To Action) buttons, mainly ATC (Add To Cart) and Checkout buttons
Marketing and content
19. Valuable website content (authentic video, photos, original and valuable content)
20. Collection of e-mail addresses (e.g., newsletter sign-up with discount)
21. Loyalty programme (customer loyalty building)
22. USP (Unique Selling Proposition)
23. Customised messages targeted to a group of users or an individual user (e.g., a marketing automation tool)
Google Optimize for E-Commerce Conversion Rate Optimization
With the criteria above met, it is time to take the next steps to boost the share of current traffic in sales. Google Optimize will assist us in this. The main idea behind Google Optimize is to test and benchmark selected elements of a website to increase the conversion rate. The tool enables the creation of 3 main tests: A / B tests, multivariate testing and redirect tests (we will discuss all test types in more detail in the following sections).
Moduł Progress bar w Checkout
Example of a positive A / B “Progress bar” test in Google Optimize increasing the number of transactions by 5%
Generating hypotheses for testing in Google Optimize
Before testing with Google Optimize, it is crucial to prepare a list of potential enhancements that we would like to implement. In terms of the Conversion Rate Optimization, this is even more crucial than simply testing random or moderately effective modules. To this end, the following tools can help us:
- Google Analytics
- Satisfaction Questionnaires
- List of online stores of industry leaders
HotJar in the preparation of hypotheses for the A/B tests in Google Optimize
HotJar or equivalent traffic verification tools are one of the key sources of information about a website. This enables recording individual user sessions or creating click maps. It is the observation of user behaviour that is a potential source for preparing the A / B tests. Locations where users are confused or abandon a website indicate points that need to be looked at or tested with Google Optimize.
Google Analytics in developing hypotheses
Google Analytics is a mine of knowledge about traffic and user behaviour. Reports, such as product performance, purchasing behaviour, bounce rate, behaviour at transaction processing will be helpful in analysing traffic and user behaviour.
Questionnaire or assistance from a friend
Another way to gather hypotheses could be to ask a group of friends to process a transaction from the moment they enter a website to an order completion. Then collecting a detailed history of whether they had any problems at any stage in processing the transaction.
When it comes to questionnaires, we can send post-purchase questionnaires to customers asking them to share their shopping feedback or place a widget on the page asking them to briefly rate the website performance. To encourage users to share their feedback, we may offer, for example, a discount on subsequent purchases.
Leaders in E-commerce
In all likelihood, industry leaders are using proven solutions to boost conversion rates. It is therefore worth observing which solutions have been implemented among such online stores. This will save time in testing ineffective solutions and probably focus on tested solutions.
Implementation of Hypotheses in Google Optimize
Once the hypotheses have been selected, it is time to implement the tests in Google Optimize. To implement the first test, you can use the Google guide available here. There are 3 main test variants in Google Optimize: *
A/B Test in Google Optimize
This is a test that involves subjecting at least two variants of the same website to examination. Variant A is the original while Variant B contains at least one modified element from the original (for example, a change in the CTA colour). The A / B tests also include a completely different website version.
To determine the test results as best as possible, each variant is displayed at a similar time of day, to the same target audience. This minimises errors resulting from external factors. We can also limit the tests to a specific audience, their country of origin, the device they are using.
Multivariate Tests in Google Optimize
The multivariate test examines variations of two or more items at the same time to verify which combination gives the best result.
Rather than showing which variant of a website is most effective (as in the A/B test), the multivariate test (MVT) identifies the most effective variant of each item and also analyses the interactions between these items. Multivariate testing is useful, for example, for optimising multiple aspects of a landing page simultaneously.
Redirect Test in Google Optimize
The redirect test (also known as split URL testing) is a type of the A/B test that allows different websites to be tested against each other. In the redirect tests, variants are identified by URL or path instead of items on w website. The redirect tests are useful when you want to test two very different landing pages or to completely redesign a website.
How to analyse data in Google Optimize?
The 2 main locations for setting up tests and their subsequent analysis in Google Optimize are: details and reporting. In the details tab, we configure the test, while in reporting we display the results.
Details in Google Optimize
This is where the test targeting is set. Options we can use:
- What percentage of the overall traffic is expected to see the test.
- Weight. The default setting is 50%. In the case where we want to target more traffic, we can increase the weight on any of the variants. On the other hand, if a winning variant needs to be implemented straight away, simply set the weight to 100%. (Note that in the context of the A / B test, its duration is 90 days. Until then, the winning variant should be implemented directly on the website).
- Behaviour selection of the traffic source directing to the winning page
- Geopositioning (Geo) selection of city, region, or country
- Technology, choice of operating system, type of hardware (mobile or desktop)
Reporting Google Optimize
This is the location to verify the test results. In e-commerce, 2 main reports are most commonly applied: transactions and revenue but we can also test other metrics, such as Bounce Rate.
Key Report Metrics In Google Optimize
Experiment Sessions — number of recorded sessions for variants A and B
Experiment Transactions — number of transactions recorded
Calculated E-commerce Conversion Rate – conversion comparison for variants A and B
Google Optimize Analysis
Probability to be Best — the likelihood of which variant turns out to be the best,
Modelled E-commerce Conversion Rate — modelled conversion rate based on the test
Modelled Improvement — median test results (the main information about how much the number of transactions increased or decreased based on this test)
Conversion Rate Optimization is a broad topic covering behavioural factors, changing buyer habits, changing technology or trends. The conversion rate value depends on our audience group and many, many other factors. It is virtually impossible to attempt to define all the indicators that affect conversion rate improvement. And thanks to the technology and the tools, such as Google Optimize, we can test and implement enhancements to boost e-commerce conversion rates.
Regarding the choice of hypotheses, it is worth to be guided by e-commerce leaders who have already tested a considerable number of enhancements. This will help save time and budget for verifying ineffective hypotheses.
Conversion Rate Optimization for high-turnover online stores is now an integral part of the development, mainly due to high marketing costs or lurking opportunities arising with current website traffic. Conversion Rate Optimization well implemented may generate hundreds of thousands in extra transactions processed from the same traffic within a year. This is why this is such a crucial area for development in an online store.
eCommerce Manager at Paul Rich – multimillion jewellery premium watch brand. Shopify & Shopify PLUS consultant, blogger and ex-founder of napnell.pl