Personalisation: The 3 best places to improve your customer experience

By Leah Anathan

The personal touch

Personalisation is a hot topic right now: Gartner are about to release their first ever Magic Quadrant for personalisation, fully recognising it as distinct from the A/B testing and optimisation spaces, although there are still points of overlap.

It’s a subjective theme, based on knowing your customers, and, as such, is different every time. The lack of a universal method to tackling personalisation can make it seem like a difficult area to break into.

However, we’ve identified the three core places where every company should begin when personalising their customer experience:

  1. Customers are on mobile—you should be too.
  2. The techniques that work address customer behaviour and intent.
  3. Automation and machine learning can get your program off the ground, fast.

Why does personalisation matter?

For many businesses, personalisation and the technology underpinning it can seem daunting and complicated. However, for anyone seeking growth in these highly competitive times, it can make all the difference. That’s because the basic premise of personalisation is as old as commerce itself: give the customer what they want.

Kicking off a new initiative is always hard, but success is built on knowing your customer and prioritising the techniques that provide a great return on investment. For this reason, we encourage teams to start with reliable tactics that deliver value and are easy to scale.

The priorities in personalisation

1. The modern customer is mobile

Smartphone penetration in Europe has exceeded 70%, with mobile traffic peaking above computer traffic for the first time in early 2017, and accounting for double the traffic of computer by the end of the year. We’ve studied billions of customer journeys, and where users were signed into their accounts on both mobile and computer, we can see that mobile accounts for 1.5x as many sessions as computer.

This is big news for retailers, particularly since revenue from mobile has not grown at the same rate. The conversion funnel looks similar on mobile and computer for attracting and engaging shoppers. However, 28% fewer mobile shoppers select products they want to buy, and 44% fewer convert.


Checkout is an easy scapegoat, but not the cause for this drop-off in the mobile funnel.  The blame can clearly be placed elsewhere: when asked what would encourage them to buy more on mobile, 47% said faster browsing, 44% said if it was ‘easier to find what I want’, and 35% pointed at better discovery.

If customers are mobile, and personalisation is about giving the customer what they want, then personalisation programmes can have a huge initial impact by making it easier for customers to discover what they’re looking for on your mobile site.  And this doesn’t just improve mobile revenue: in 2017, 19% of computer purchases were influenced by mobile (up 93% from 2016).

2. Focus your efforts on the techniques that deliver value

So, that’s mobile. But what about the rest of the customer experience? In our analysis of 2bn customer journeys we identified the top-performing techniques, as well as proving that many techniques, such as UI changes, can be unreliable or ineffective. Based on revenue per visitor (RPV) uplift, and the probability of uplift, these are the top performers:

The most powerful personalisation tactics are successful and reliable because they change customer behaviour, rather than the cosmetics of your site. They change behaviour in a natural way that supports the customer journey, helping individual customers get what they want.

Encourage customers to explore your catalogue with recommendations

Product recommendations are a method of ranking content, making it easier for customers to find relevant products from within your product catalogue. Customers are bombarded by information, competing brands, and competing products within brands. Making a decision can sometimes be overwhelming: the famous paradox of choice, or choice overload.

Product recommendations can alleviate this by using machine learning based on millions of customer journeys to narrow down options, streamline browsing, and create additional relevancy through a personalised experience.

Create purchase impetus with social proof

Scarcity, social proof and urgency are all highly effective. Broadly speaking, this is because evidence selection techniques resonate so well with shoppers. They cut through the noise to differentiate products on a page, justify why site visitors are seeing them, and provide a reason to purchase.  

This sort of experience works because that’s how the human mind works: while it’s important to make use of cold, hard facts and logic, we need to remember that our brains take shortcuts. Biases and behavioural heuristics help us make decisions quickly, like the von Restorff effect — when there are many items in a list, the one that’s differentiated stands out — or the bandwagon effect — the tendency to do things because others do the same.

And this is before you even get to personalisation: targeting these techniques at specific visitor segments. For example, you might show new visitors the most popular products, whereas visitors returning to the site will see what’s increasing in popularity before going into even more nuanced groupings. Segmenting experiences not only delivers a more personal, and therefore more compelling, customer interaction, it also delivers a 3x boost to their impact. You can read more about this in our guide Shortcutting the Path to Purchase.

3. Scale your efforts with automation and machine learning

The final recommendation for delivering a high-impact personalisation program is in scaling your efforts: employing automatable, set-and-forget experiences that require limited ongoing maintenance. In other words, turn on your desired personalisation tactics, and sit back while the technology takes care of the rest.

That means harnessing time-savers like reusable templates, which marketers can update as their needs change, without having to wait on developers to get to them on the queue. Rules-based personalisations are another valuable efficiency aid for teams — like being able to update underlying strategies for individual experiences, or switch out strategies on a site-wide basis.

A notable tool in the hunt for scale is machine learning (ML). ML does require vast quantities of structured data, but, integrated properly, can enable 1:1 personalisations with the customer. Through this, individualised products, offers, and categories can be curated based on data about what a customer is most likely to be interested in looking at next.

Discovery, exploration, and reassurance

Key takeaways:

1. Help customers engage with your products to discover something they will love with a compelling mobile experience.

2. Encourage customers to look deeper into your catalogue, using product recommendations to explore more of what’s available and to narrow down their selections.

3. Reassure customers when it is time to transact, using social proof to validate and support them in their purchase decisions.

And just remember that no matter what business metrics you’re tracking, your ‘North Star’ in personalisation is to give the customer what they want. If you want to dive a little deeper into this topic, I encourage you to read our new guide called The Path to Personalization.

Leah Anathan, CMO, Qubit

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