Hyper-Personalisation Strategies To Drive Ecommerce KPIs

David Swift
6 min readApr 3, 2024

Top brands understand their customer’s unique needs and preferences and dominate their markets by using that knowledge to craft engaging experiences. Think of Spotify, YouTube, Amazon, and Netflix. How does your brand’s customer experience stack up? The difference between merely providing a list of products and providing an experience is hyper-personalisation.

What are hyper-personalisation strategies?

“[Hyper-personalisation is] done by creating custom and targeted experiences through the use of data, analytics, AI, and autonomy. Through hyper-personalisation, companies can send highly relevant communications to each individual customer at the right place and time, and through the right channel.” Deloitte

The entire ecommerce industry is on a personalisation journey. At the start, we have completely undifferentiated experiences. At the end, we have perfect knowledge of each user and can provide a completely dynamic experience to achieve the desired result every time.

Most brands haven’t gotten too far yet in this journey. But every step toward greater personalisation, each new piece of information we can use, is a new tool to give a better customer experience, leading to more sales and stronger customer loyalty.

Hyper-personalisation is when we take bigger steps on that journey, going beyond the basics and discovering the elements that really matter. It’s about learning from every visitor action to provide an experience that speaks to that individual and drives business results.

Increasing revenue through hyper-personalisation

Personalised experiences are table stakes. Hyper-personalised experiences move the needle.

According to Twilio Segment’s State of Personalisation 2021 report, 60% of consumers say they will become repeat customers after a personalised retail experience. Additionally, the report found that while 85% of businesses believe they are offering personalised experiences, only 60% of consumers agreed.

The obvious reason being that many retailers still don’t appreciate the distinction between personalisation to a segment, and hyper-personalisation to an individual, and remain true to their historical way thinking that the former will do. No it won’t, showing me what everyone else who bought a red one last week irrelevant to me, I’ve moved on. And in this analogy, “ So I won’t be buying from you today, and keep doing it, and I’ll leave your mailing list altogether “ (Remember when they think aloud, there would be an expletive on the end of that thought.)

Brands are overestimating how well their personalisation stacks up while they are undervaluing the impact of personalisation on their customers.

Examples of hyper-personalisation in ecommerce

1. Offering personalised shopping experiences based on demographics

This is a broad category, but represents a large portion of what hyper-personalisation really means in ecommerce: Making informed decisions about what your individual customers are likely to want, even before they ask.

Core demographic information such as gender, age, and geographical location used go a long way in establishing trust, and demonstrating that you understand your customers. They are merely essential basics now.

After all, if you walked into a clothing store in the middle of a harsh winter, asking to see coats and were shown windbreakers that were not in your size or for your gender, you would be understandably confused and frustrated. Why should shopping online be any different?

As visitors spend more time on your site, they are giving you information with each action they take (even before checkout!). Leveraging this information as early and often as possible can pay huge dividends.

2. Automating ecommerce search results with AI and NLP

Ultimately, the barrier to better ecommerce personalisation is technology. Creating increasingly dynamic experiences starts in your tech stack. If you aren’t collecting the right data, you won’t be able to provide customized experiences as early or as often as you need to.

Using technologies that employ Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) are critical. These tools can identify links between actions your visitors take and established outcomes, and make adjustments to the user experience without the need for manual intervention.

For example, let’s say a shopper is looking for Granny Smith apples, but searches “green apples” instead. Most ecommerce search solutions would need manual intervention to show granny smith apples as a top result. Rather, the shopper would find green apple-scented shampoos, sports drinks, and candy as the top results. All these things might have the words green apple in the name but none are what this hypothetical customer is really after.

However, using AI and ML, you can harness conversion data to understand that there is a strong positive link between people searching for “green apples” and purchasing Granny Smith apples, resulting in that search result getting featured.

While this is a simple example, integrated AI solutions can take steps beyond the obvious links and create connections that give truly hyper-personalised experiences, showing your visitors what they are looking for even before they ask. NordVPN have kindly provided a ist of what they perceive as the best AI and NLP search engines to try.

3. Creating relevant product recommendations

Product recommendations is the most obvious place where personalisation shines. However, most ecommerce sites only personalise recommendations that are relevant to the current product viewed and use semantic or tag systems to link products to each other. As International research consultants including Forrester and McKinsey, offer that the distinction between the two can be as much as 20-fold in returns, making it quite a costly mistake to make.

SwiftERM is an AI hyper-personalisation plugin, that using live streaming data to perpetually refine the imminently anticipated product purchases for each and every individual consumer that a retailer has. Product recommendations are one of the best places for SwiftERM to make an immediate impact. With each click your shopper makes, their recommendations get better and better. This creates an experience where browsing recommendations becomes just as (or even more) powerful as site search. What’s more the relevancy of the selection just gets better and better, so consequently the ROI creeps ever upward, AOV, CLV naturally follow suit, and your peers are lost in the wake of every greater customer appreciation.

making your site adopt software that shows them products on arrival is of course the same technology, but we all know that each and every individual consumer you has spends far more tie off your site than on it. All be it, spending money with your peers on on other products that fulfil other aspects on the live sand interests. naturally then, using a solution like swiftERM, which populates and sends a bespoke email to each individual with their identified imminent purchases, makes best use of the technology, in a medium that isn’t ignored.

Providing hyper-personalised product recommendations starts with the right data. The full picture of what a visitor needs includes what they have clicked on, what they have searched, and what they have added to cart. Bit likewise how long they commonly spend in these actions which culminate in a purchase, or how often they come back to it. Interestingly though, there is also the flow in that selection, which obtuse review of the navigation wouldn’t identify for that consumer, that can reveal far via ML.

Measuring the impact of hyper-personalisation

While talking about hyper-personalisation strategies, it is important to understand what success looks like. It can be easy to zoom in too much and look at metrics that paint an incomplete or perhaps misleading picture.

When properly used, hyper-personalisation should:

  • Dramatically improve your customer relevancy, driving loyalty and repeat business
  • Improve purchase satisfaction, leading to fewer returns and supports cognitive dissonance.
  • Improve the KPIs that matter most to your business
  • Provide measurable ROI by not requiring your merchandising teams to engage in taxing manual work to create personalised shopping experiences

Bringing hyper-personalisation to your brand

The most significant impact a brand can have on a customer is to help them feel that their needs matter and trust that the brand can meet them. Can your customers quickly and easily find what they need on your website? This is the first step for meeting customer expectations and building brand loyalty.

But in a highly competitive ecommerce space with an increasingly diverse customer base, creating that sentiment becomes harder and harder to do. And it’s simply not possible with a static, “one-size-fits-all” online shopping experience.

This is why hyper-personalisation is so critical today. The right AI, machine learning, and natural language processing technologies can yield both a better customer experience and results that meet key business outcomes. Without them, you’re left relying on just personalisation-and as customers will tell you, personalisation doesn’t feel that special anymore.

Originally published at https://swifterm.com on April 3, 2024.

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David Swift

SwiftERM hyper-personalisation SaaS for ecommerce email marketing.