Using data to enhance customer journeys and increase conversions
You’ve developed your ecommerce site, and customers are coming through the funnel but the journey (excuse the pun!) shouldn’t end there. This is just the start. Finding out what is pulling people into the journey, causing them to drop out of it before they purchase, as well as what is driving conversions, are all key to any digital marketing strategy. That’s where customer journey analytics comes in.
We’ll take a deep dive into using data to enhance your customer journey and drive increased conversions.
- What is a customer journey?
- Customer journey analytics
- What is the data telling you?
- What’s customer experience data?
- How to use journey analytics to improve conversions
This seems like an obvious one but bear with us. It’s quite literally the ‘journey’ a customer has to go on to purchase your service or product (and hopefully repeat it). It should include all the steps they take along the way. Obvious ones like filling out their address details or specifying a particular size and quantity. However, it doesn’t just start with the purchase process, it should begin before that.
This data doesn't just start at entering your landing page and stop at the online checkout. This can be from the first touchpoint - which could be an app to future purchases. It’s an overarching term for the multiple touchpoints within the journey. With GA4 Reports you can now track across channels so you have a more joined-up view of your customer journey.
If you’re not funnelling users into the journey in the first place, you’ll be in trouble, so that should be where you start your tracking and analysis.
With so much data at your fingertips, what do you focus on? The key is to identify where the holes are. Imagine your journey as an inner tube - you’ve got punctures and you need to find them to be able to ride your bike again. A big caveat here, it’ll be different for every business but if you’re a B2C selling online then these rules generally apply.
Important stages to monitor and ensure you have the right tracking on (and what they mean) are:
1. Landing page to journey entry point
This shows you whether your landing page is doing a good job of getting your users to seriously consider purchasing. You should monitor this over time to see any fluctuations. Some will be normal seasonal changes, others might mean something you changed on your landing page isn’t working. Use Google Trends to match up data on what you’re seeing vs. the market at large.
Journey entries/landing page sessions - for example if you had 100 journey entries and 1000 landing page sessions = 0.10. Expressed as a percentage = 10%
2. Journey entries to basket
This gives you an overview of the whole journey that you can use to make a quick comparison if measured over time. If you see any differences, either way, you can then drill down into the individual journey steps.
3. Journey step 1 to step 2 (and so on)
A stepped journey will help you to identify drop-off points more easily. Gather data at every point and compare it to the numbers starting and you can see the ratios.
They’ll show you where the main issues lie so you can dig deeper.
4. Journey step (last before the basket) to basket
You can use this to compare with the journey entries - you can then see the total drop-off percentage. Again, any big changes in this and you know you need to investigate further.
5. Basket to sale
If you’re losing people here you need to look at the purchase process you have in place. Less is often more when it comes to the UI and UX in this area. Users tend to have a low threshold for any barriers - like a slow payment gateway.
6. Sales to repeat
This is a key one to measure. If users are coming back you’re doing something right but if you change something on the site or in your offering and less come back, it can be an indication of something not working for your customers.
That links nicely into looking at customer experience data.
Customer experience data refers to the analytics data that relates specifically to customer experience (within the journey). Metrics that indicate this include:
- Time on page/site: the longer they stay the more likely they are to be engaged and therefore purchase.
- Stickiness (new for GA4): slightly different from the retention data. It compares active users over a short period vs. a longer period i.e. active users in a day vs. active users in a month. It creates a ratio for you. The higher the percentage the more engaged users are and the better your site is at retaining them. This is useful because it shows you how engaging your site is overall. If your stickiness is bad you may not be funnelling as many users into the journey in the first place, as you could be.
- Conversion data: if they converted that doesn’t necessarily mean they had the best experience they could but it does mean it wasn’t bad enough to stop the sale.
This is part of the puzzle of data that helps us understand customers' behaviour, buying habits, and preferences throughout the journey. This data can help with developing online journeys as well as informing product development strategies.
The data you gather will help you identify areas of the journey that need improvement. For example, on the check-out page, there might be a drop-off at the address entry stage. A tried and tested route is to set up A/B testing for any alterations you’re planning to make to the journey.
A/B testing, also known as split testing, is a methodology for comparing two versions of a web page or app.
1. Write a hypothesis for A/B testing
You’ll need to begin with a hypothesis based on the data you’ve gathered to outline the A/B tests you’d like to run.
A hypothesis is a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation.
The hypothesis might be that if you simplify the address form, you’ll get more conversions. It’s made up of two variables, the cause (the action) and the effect (the outcome).
2. Create variants
You’ll need to work with your design team on different variants to test based on your hypothesis. Depending on the outcome, you may want to re-test with another variant but you’ll only ever use two per test - A and B - one of which is the control version (your existing page).
The variant will be shown to a percentage of users at random. The performance of each is then measured against a conversion goal to see which one wins.
It takes the guesswork out of optimisation.
Top tip: make changes small and do them one at a time so you can work out what action drove any shift you see in the data.
3. Run the A/B test
For this to be successful, you need enough data, otherwise, you might end up throwing the baby out with the bath water. That may impact the duration you run the test to gather a good sample size.
4. Analyse the results
The A/B testing software you’re using will give you the results - that might be more clicks on a particular button where you changed the colour. You’ll need this to be statistically significant. In plain English, for one to clearly be the winner.
If not, keep testing.
5. Implementing the change
Depending on the results of the test - let’s assume it went well - you’ll implement the change you hypothesised about. Your internal or external dev team will make the changes and you’ll then need to monitor the results carefully to check the positive prediction lives up it in reality.
Ideally, this will end up in more conversions!
For example: take a look at our post on a UX redesign of the Papa John's (old) journey we did to showcase just how powerful these kinds of changes can be.
6. Continued testing
Testing should be an iterative process, where you learn from the results of one and use them to inform the next. Take Amazon for example. Their check-out process is constantly evolving in tiny ways, every element within it optimised.
This isn’t a one-time fix.
Summary of using data to enhance your customer journey
Improving your customer journey to increase conversions is an ongoing process. Data. Data. Data. Then. Test. Test. Test. That’s our advice in a nutshell.
Make sure you’re tracking all the touchpoints and monitoring the data carefully to see where you could make improvements. Even a 1% uplift in add-to-basket could translate to thousands of pounds.
We've merely scratched the surface of what's possible using customer journey analytics to influence your journey design. This is something our development team works on for many of our customers, so if you'd like more help or advice, please get in touch.
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