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4 Data Analysis Techniques to Come up with more Winning A/B Test Ideas


In conversion marketing, the hard part is to test the right things with the right treatment. Discover what really matters and make the most of your existing traffic.

Conversion Marketing, AB test idea

Driving targeted traffic through digital marketing is hard, but converting visitors into customers is even harder.

As every website has a set of unique goals and optimizing the experiences based on these propositions is still the challenge.

This results in is~90% of the website visitors never return (as per Statista)

That’s why conversion optimization is important; because it’s not just about traffic; it is about conversion.

Conversion is an action that moves a site visitor towards becoming a customer, and your conversion rate is the percentage of a visitor who makes a conversion.

So if you or your team member overlooks optimization opportunity involve in conversion funnel — you’re missing the chance to make the most of your website traffic.

But getting started with CRO is tough because the key to successful testing is to come up with good ideas (strong test hypothesis)

Good test ideas can come through better conversion research -either through a Quantitive way or a Qualitative way.

With quantitive analysis, the first thing you should do is take a 10,000 ft look at your website and get a good sense of what the critical metric is essential for your business.

In this article, I’m going to put my utmost effort into describing the best ways to utilizing customer insights through data analytics and conversion research.

From data analysis tools like GA drive better conversion research — determine which the KPI is that needs to look for and identifying data-driven ab test ideas.

While quant ITive analysis has both the strengths and weaknesses over qualitative analysis- I’ll overview on different quant methods and you can decide which one you should use for your hypothesis.

To map out the high-level stages of your customer journey.

Step 1: Identify the shopping behavior

The first step in conversion research starts from understanding your analytics data is in the form of sales funnel — or what we called as growth/ marketing funnel.

Whatever you call it the center truth- is the core of selling online or generating business online.

It is the same sales funnel/growth funnel which you have seen usually everywhere –when you land on some website, and they ask to enter credentials by offering some discounts or membership.

Yes, they wanted you to enter you in the sales funnel.

To optimize the sales funnel, you need to identify the problem and pain points that customers are experiencing.

Pain points can be established by understanding the entire customer journey from the first touch to conversion. And through all the experiential data that customers navigate through.

Evaluating customer journey will help you to see which areas of the customer journey to improve, like site design, navigation, content layout, product affinity, and conversion rates.

To know the entire customer journey from the first click to conversion, you need to map out the high-level stages & KPI across each of them.

Conversion Optimization : Sales Funnel for e-commerce
  • Awareness: Top of the sales funnel. These are the set of customers- Hear about your business for the first time? Can they find and trust you? There are multiple KPI’s, i.e., visit/visitor/Return% & others.
  • Interest:
    Visit under the interest group represent customers involved in the product research; they might not have more engagement than the awareness visit. Still, the way they come to your site can be a good indicator of this stage.
  • Decision:
    In this stage, visitors express some level of engagement by performing some action, i.e., add to cart, add to Fav & others. Multiple micro-level goals lead you, visitor, to the broader macro goals for the next stage.
  • Action:
    The main conversion Stage. It doesn’t limit only Conversion Rates but involves KPI’s like Revenue, AOV, and RPV (revenue per visitors)
  • Loyalty:
    At this point, the visitor becomes customers. They’ve already performed the main goal and returning to re-purchase to convert again.

They must exhibit and advocate your brand (i.e., social sharing, leaving reviews, etc.).

The next step is to identify these KPIs in web- analytics. If you’re stuck, try using the below analytics template mentioning KPIs across each of the KPI segment.

Step 2:- Product Performance

Product performance reports provide valuable insights and show how customers are interacting with products.

(You can find product performance data in GA under
Conversion < Ecommerce < Product performance section.)

With tools like Google Analytics — you have detailed segmentation capabilities that transform the way you measure product performance, enhance conversion rates & optimize marketing spend.

The framework for measuring product performance

The report shows a high-level metric such as product Revenue, Unique purchases order quantity, and refunds.

From here, you can further drill down into and find opportunities to optimize the product performances.

The product performance report mainly divided into two areas — sales performance and shopping behavior

The sale performance metric mentioned below:-

  • Product list views: Visitor viewed a product number of times in a product list in the online store, including referrals, form search engine and other.
  • Product detail views: Number of users click on viewing a product on a product detail page.
  • The product adds: User added product added to the cart.
  • The product removes: Users remove the product from their cart.
  • Unique purchases: Number of individual purchases as per the overall sales.
  • Cart-to-detail rate: Product added to the cart/ product detail views.
    Higher the better
  • Buy-to-detail rate: Number of product purchases per number of product detail views. Higher the better.

Optimize your store based on the Shopping behavior

The product KPI showed above, making a valuable tool in your product conversion research.

You can also group product performance based on the below possible strategies to maximize your conversion

i. High View Low Conversion

High view low conversion refers to SKU’s, which receives a lot of views, but the CR is very less or negligible.

Metric like the low cart to detail means that product pages are being viewed regularly, but the product isn’t being added to the cart / being purchased. Or if your site standard CR is 3%, but SKU’s CR > 1%.

Then these SKU will fall under “High view low conversion”

There can be many reasons for the poor conversion — Price, in-stock, or other.

To optimize, here is the amazon example. This demonstrates

Here is the Amazon example. To optimize the performance of the “High view low conversion” product — it shows a recommendation above the fold that helps the customer to infer the lowest prices in the respective categories.

ii. Promotional Flags

Remember the Pareto principle- 80/20 rule. The same applies to your product performance — 80 % of the sales driven by 20% of the SKU.

How do you let your customer know the same?

The best way to do it is simply flagging them — based on the promotional flag- i.e. best seller reduced price or clearance.

This will ultimately help in building the promotions around them to nudge customers toward an actual conversion.

Brand Example: Walmart.com

Walmart.com displays best seller, clearance and reduce the price.

Walmart structures its messaging to highlight the product, i.e. “Best Seller”. “Reduced price”, “Clearance”. This influence customer purchase decision.

iii. Optimizing Out of stock (OOS) product page

Out of stock is a major challenge for most of the industry. For some of the business, OOS product views compromise 10–20% of the total product views.

But unfortunately, the experiences of these OOS% SKU’s is the same as the normal SKU; this is results in many customer drops or exits the website.

So when the product goes OOS, apart from surfacing notification — how do you optimize the performance?

The example I have shown is Urbanladder.com.
When the product is not available for the stock — site display recommendation of available products at the top of the page above the fold to encourage the customer to complete a purchase with the current possible SKUs at a time.

Optimizing out of stock product example from Urbanladder.com A/B test ideas.

Step 3:- Audiences

The customer expects you to know who they and their likes/dislikes, interest areas.

You should capture their behavior and provide a customized experience to each of them.

If you have 10M customers visiting your store then you should have 10 M experience cater to their needs, not just 1.

This can be achieved by capturing the user behavior — down to the last details

Customer analytics provide a lens in capturing these different traits of customer behavior and with mapping, the journey helps you to connect dots

Through brilliant data visualization & journey, analytics reveal a higher resolution 360 degree vie that uncover opportunities to identify different customer needs and group them based on their needs

There are four ways to identify critical customer behavior by utilizing customer analytics (shown below)

  1. Customer Involved in CLM
Customer life cycle management refers to customer-related metrics which, when analyzed for a period of time, indicate the performance of a business.

i. New customer:- Never registered, never ordered.
ii. Lapse customer:- Customers who haven’t bought from in over the past 6 months.
iii. High-value customers:- Customers spend £x per month

Only a few of them listed out.

2. Based on the behavior

Behavioral segmentation is about understanding customers not only by who they are, but by what they do. This allows you to divide your customers into groups according to their behavior, attitude towards, use of, or interaction to a product, service, or brand.

Further presents an opportunity to personalize customer behavior based on their action.

Many interactions can be measured or captured — for example, customers interacted with Fav, or comparison tools, and likewise, many others.

3. Category affinity:- Categories the customer most interested in

Successful online businesses should gain affinity from customers to help them achieve rapid growth.

Likewise, through categories affinities-business can connect personal or emotional connections between consumers and a specific category.

If you are personalizing customer behavior, you need to ensure that engaging your customer by showing the categories they are most likely to act.

Example Brand: RS component has set up a method for categorizing users based on their preferences and display personalized recommendations by showing the product from the categories they like.

Enable category affinity data which allows user to study affinity data for two categories to see how that category pair is performing.

With category affinity automatically captures the categories a user visits and then calculates the user’s affinity for the category so it can be targeted and segmented on. -Adobe

Category affinity: RS Component

4. Post-purchase advocacy

Customers expect to notify them on “delivery-status”, “back in stock” and “website down status, and the post-purchase behavior can be measure by customer satisfaction score and NPS

Step 4:- Site section Performance

One thing which is very clear in this multi-screen world is that users do not move in a perfectly linear fashion.

i.e you can’t expect your customer to start the journey starting from HP to the product page and end with checkout.

There are many micro engagements in the customer journey that enable users to make mind for purchases.

That’s the reason it is essential to measure the site section performance in a way that is measure micro-interaction and success metric among each.

While looking at the site metrics below are the few consideration that you need to do to enhance the performance

1. Product page (PDP) Engagement score

How much your customer engage with the product page?

There is no single engagement score that you should benchmark. But in a nutshell, the higher the score, the higher the score of engagement.

The best way to identify is:-

i. Add to cart%:-

What percentage of customer visiting product detail pages hitting on add to cart. i.e. 3% is a better conversion rate :)

ii. Engagement with Recommendations:-

The concept of recommendations has been around since Amazon first added: “inspired by your shopping trends”.

From then most of the e-commerce store surface different recommendations or cross-sell/ up-sell with the product page.

But along with the Add to cart %, it is worth checking the engagement w.r.t customer visiting product detail pages.

iii. Research engagement:-

The customer often researches a lot before they purchase finally. And thus, they interact with a tool like — “Add to fav,” “technical sheets,” or “Compare.”

All these are classified as “research engagement,” and these should be measured the same way.

2. Search Performance

Search is an essential part of the customer journey, and ideally, for any major e-commerce site –>70% of the revenue participation is driven through search results

There are many ways to measure the performance of search results:-

i. Site search CR & % of users with the site search

Visitors with the search convert 2X with the average website conversion rate, no matter how poor your search is and 5x versus without search

ii. Null searches & Exit Rate:-

The percentage of total searches performed that return zero results. If that rate is above 6%, there is likely room for improvement.

Exit rate:- Search accuracy, relevancy, business rules all matters for quality of site search results. And your search result pages should lowest exit rates compared to other site sections.

If it is high, it will impact your conversion rate badly.

iii. Revenue attribution and revenue participation.

Attribution and participation are two different metrics. And yes, it makes sense to calculate separately for measuring search performance.

Here is why:-

Attribution will tell you % of revenue contributed through search result pages while participation credit of search results of the overall revenue generation.

3. Cart & Checkout behavior

~75% of users abandoned the shopping cart (as per Statista)

Through the “Checkout Behavior Analysis” report, you can figure out how your users moved from cart to checkout funnel and at which step they exited

According to statistics, six to eight out of every ten customers on your eCommerce site will abandon their basket and leave without making a purchase.

Cart abandonment rate is largely unavoidable, but engagement within the cart can be achieved.

Below example, Amazon uses a saved basket feature on the cart page & with an option to move it to the cart.

When I’ve run the test on the client website, I found that the saved basket has excellent engagement with over 8% user interaction with this on the shopping cart.

Conclusion: Importance of data analysis in your Conversion Research

The benefits of conversion research through data analysis can make all the difference when it comes to optimizing customer experience through the CRO strategy.

With data analysis through tools like GA or Adobe Analytics-you can take conversion research to the next level to identify better AB or XT test.

Consider each strategy- shopping behavior, audiences, site section, traffic sources, and product performance — to see how your firm approaches each. What about how your competitors approach them?

Can you spot opportunities in these data analysis strategies to capture competitive advantage?

Do you have any other strategies that work better please let me know in comments, It would be a great win-win for all us.


4 Data Analysis Techniques to Come up with more Winning A/B Test Ideas was originally published in UX Planet on Medium, where people are continuing the conversation by highlighting and responding to this story.