“Hyper-personalisation” and “searchandising” are not new terms.

The concepts underlying them have formed the bases for some critical e-commerce marketing strategies for quite a while. Old news…

So why are they suddenly a big deal again in mid-2021?

The answer is simple, actually. It has to do with the New Retail, the rapidly evolving marketplace drivers e-commerce must address, and most importantly, the altered expectations with which e-commerce consumers are now approaching online markets.

In fact, a survey from SuperOffice indicated that almost 46% of surveyed business professionals will be taking customer experience very seriously indeed for at least the next five years.

It also found that a richer customer experience was more important the price alone for 86% of consumers.

We don’t think e-commerce retailers should need more than that bit of information before they sit up, take notice, and start taking the latest CX developments more seriously.

SuperOffice’s Toma Kulbytė refers to CX as “the new battlefield.”

That sounds about right.

Hyper-Personalisation and Searchandising: Looking at the Lingo

E-commerce marketing has its own strange lingo, its jargon. That’s fine (as long as you’re not a consumer), but e-commerce evolves. As the rate of change increases, the jargon does present an obvious problem.

Meanings change right under our feet, and sometimes we can end up thinking we know what we’re talking about when we’re really a few steps behind the parade. That phenomenon occurs more frequently when normal market factors and practices get overshadowed by crisis-driven changes.

Like those we’ve seen globally over the last year and half.

So, in order to understand the relevance of hyper-personalisation and searchandising to e-commerce retail, we need to have a clear sense of what they are in 2021.

Hyper-Personalisation: One Size Need Not Fit All

The current and near-obsessive retail focus on improved CX, greater retailer transparency, richer personal relationships with consumers, and deeper consumer-brand connection are driving factors for hyper-personalisation in 2021.

All of these factors speak directly to retail buyer expectation and experience.

Hyper-personalisation is the customisation of CX based on real-time data collection and analysis to create increasingly personalised, targeted shopping experiences.

It’s of increasing importance in 2021 because of the surging growth of e-commerce retail – growth that’s shaped in large part by the changing sensibilities and expectations of consumers, rather than by industry-led initiatives.

It also reflects and incorporates new technologies, including AR, AI, and enhanced predictive analytics, that enable greater, deeper, and more focused retailer-customer relationships that have been possible in the past.

More specifically, current tech and tactics for hyper-personalisation allow vendors to target and control the solution or purchase options available to consumers much more accurately.
They also allow marketers to use developing data and machine learning to remove bumps and distractions from the customer journey and guard against “option overload.” Given the average consumer attention span, this is critical in getting consumers to their goals as quickly and simply as possible.

In a guest post on GetFeedback, Annette Franz of CX Journey Inc. makes clear that e-commerce retailers who want to move ahead with hyper-personalisation must be willing to address its critical requirements:

  • deep knowledge of your consumers, fed constantly by meaningful behavioral data you use for both predictive and corrective analytics, and the corresponding, consistent investment of your time in getting to understand your consumers more intimately);
  • the technological means to act on your behavioral data in order to bring focused new content, insights, and products to your consumers, engaging them personally, constantly and deeply in your brand story and their own customer journeys within it;
  • the understanding that hyper-personalisation raises the bar in terms of vendors’ obligations to protect and secure their customers’ data; and
  • the understanding that hyper-personalisation extends from marketing and content development into product research and design. In fact, it reaches all the way to omni-channel context creation for immersive CX, seamlessness, the role of empathy in your consumers’ experiences, and more.


The goal: to make the entire customer experience as individualized, as direct, as immersive, and as meaningful as is possible.
You don’t have to be a marketer or e-commerce professional to understand the allure of direct, inviting, and individualized relationships between a brand and highly engaged individual shoppers.

The Mechanics of Hyper-Personalisation: Retail AI Rising

Hyper-personalisation enables what most of us would consider good customer experience.

That experience is customer-focused, highly personalised, informative for vendors, and both rewarding and subjectively satisfying for consumers. It accounts for the traditional hallmarks of value and service for money, and improves acquisition of customer data and personal feedback for product refinement.

But it goes well beyond those hallmarks.

If done properly, hyper-personalised CX extends cross-platform and cross-channel, with a particular emphasis today on mobile-friendliness. It involves the increasing application of AI and machine learning to provide retailers with systems that learn their customers’ preferences quickly and accurately.

So, it’s not surprising that we’re starting to see new platforms and technologies that focus directly on hyper-personalisation to shape customer experience.

Hyper-Personalisation in Physical Retail: Hybrids Have It Right

In brick-and-mortar retail, brands have options for hyper-personalisation, especially with the advent of hybrid brick-and-mortar. The hybrid approach can successfully combine new technologies with the physical presence of conventional retail.

Brick-and-mortar’s options include, among other things, the incorporation of applications that extend point-of-sale functions to personnel equipped with tablets.

The result: sales staff extend their time with shoppers in the customer journey, establishing personal connections and providing real-time services on a one-to-one basis. Staff become their customers’ partners in the journey and the transactions it generates.

Brick-and-mortar retailers can employ digital kiosks incorporating AR (augmented reality) applications and artefacts that engage shoppers directly, on-site, and that enable personalised “brand connectivity,” product selection experience, and real-time data-gathering.

They can also take advantage of beacon technology that recognizes mobile users in a physical store. When shoppers opt in, retailers are able to gather data in real-time and present targeted offers and assistance to users, effectively extending the reach of brick-and-mortar brands beyond their physical locations.

This should be particularly attractive to consumers in our post-pandemic reality.

We expect that hybrid brick-and-mortar’s ongoing evolution will increasingly involve implementation of AR, AI, and developing tech focused on involving consumers more deeply in brand stories.

Physical retailers are realizing that their best path forward is through customer relationship-building that’s highly personalised and increasingly humanistic.

And that means hyper-personalisation in the world of physical retail will increase. It’s here to stay.

E-commerce: What Experience Can We Create for You Today?

The only limitations hyper-personalisation faces in e-commerce retail are those arising from the gap between what we can imagine offering to consumers, and the technology we need to turn what we conceive into reality.

Luckily, the technology is catching up quickly, particularly where AI-based data creation and categorization are concerned – critical, as hyper-personalisation depends on accurate data that’s acquired, categorized, and analysed in real time.

Developments in AI now allow for rapid machine-learning that refines consumer personalisation continuously. Online retailers can take advantage of AI-driven personalised search functionality that may involve voice search, data structuring and predictive insights.

The technology allows e-commerce brands to provide personalised product visualization and real-time manipulation of individual customer journeys based on increasingly accurate predictive and corrective data analyses.

We’re seeing growing competition in SaaS solutions and platforms that claim to enable hyper-personalisation. Some examples:

  • Syte

Syte is a “product discovery platform” that offers three suites or functionality clusters: one for visual discovery, one for hyper-personalisation, and one for searchandising.

The visual discovery functions include image search and item recommendations, with visual AI for related product offerings on an e-commerce retailer’s site. Syte’s hyper-personalisation functions include real-time solutions for marrying customer actions with visual information as bases for more accurate product recommendations.

The searchandising suite uses AI-enriched data to improve site search results for any specific customer.

Users manage every feature centrally through a single console offering complete data access, testing tools, analytics, and more.

  • Vue.ai

Vue.ai is a platform offering “AI-powered digital experience management for e-commerce.”

It offers vendors a personalisation engine, the intent of which is to enable them to create highly personalised experiences for each customer. However, that’s not all, as the company’s AI extends to matters of product discovery, catalog management, product image management, and cart abandonment issues.

Its five-module product suite provides solutions for e-commerce retailers in fashion, electronics, beauty, home furnishings, and groceries.

  • Customer Engagement Platforms

There’s a growing number of so-called “customer engagement platforms,” most of which offer functions designed to build stronger, more personal relations with consumers by enabling simple central control of communications across channels, self-service options for shoppers, marketing capabilities, real-time chat capabilities, and related features.

Such platforms include the likes of LiveChat, Freshdesk, and Sprinklr, though these platforms don’t necessarily focus on hyper-personalisation as part of their reason for being.

Instead, they enable e-commerce retailers to build closer bonds with their customers by streamlining communications, data acquisition and management, service, and customer journey refinement.

There are some e-commerce players whose enter online presence is an exercise in hyper-personalisation: one notable example is Stitch Fix.

Stitch Fix is a fashion styling service for men and women that allows users to shop based on recommendations from personal stylists. Shoppers pay a modest styling charge and then select from the items their stylists recommend. They get to try before they buy, at home, and the styling charge is deducted from the price of the items they decide to keep.

It’s ALL about the customer, the stylist’s personalised recommendations, and the creation of a friendly, personalised, “easy shopping” relationship.

Clearly, there will be more of this. We suspect that “the doors of hyper-personalisation will never close.” In other words, the trend will continue and intensify.

We also suspect that a day will come when a shopper will deal with a personal AI through which he or she will engage fully and personally with favored brands and retailers, and the relationships involved will seem normal and very close – even intimate.

Time will tell.

Searchandising: The Haystack Needle Is Growing

Searchandising is the practical union of search functionality and merchandising.

The intent behind it is to refine and speed up a shopper’s product search so as to minimize the time he or she spends trying to locate desired items – a sort of faceted search on steroids. Proper searchandising tools can help retailers prevent frustration and abandonment, and bring potentially desirable items to a buyer’s attention more quickly, thereby improving conversions.

Not coincidentally, the sort of real-time, AI-driven tools and tactics hyper-personalisation requires can also drive retail search functions to provide better, more meaningful results for every search a given shopper performs. The growing emphasis on NLP (Natural Language Processing) to improve the accuracy and comprehension of search algorithms is improving the effectiveness of retail search solutions.

However, let’s be clear about something.

The selection (or development) of an onsite search function for a brand or retailer website is a little more complex than it might seem.

Mathias Duda’s post of March 18, 2021 over at the searchhub.io blog makes this clear.

Among other considerations in revamping or buying a search solution, e-commerce retailers should pay attention to things like the scope and relevance of search algorithm functionality, semantic issues, and keyword discrimination.

Then there’s the ability of a search solution to deal with issues of price differentiation, availability, and discernment of the criteria to be used in guiding a sale or making recommendations to individual shoppers. There are also questions of integrating a brand’s site content to control product information and other brand content, as well as customisation for specific requirements.

It’s not simple, but that doesn’t make the implementation of personalised search solutions any less critical.

The good news: there are some workable searchandising solutions available.

Obviously, some searchandising is relatively simple, at least conceptually. But current searchandising solutions like Constructor.io from ObjectEdge implement AI to leverage more than just basic shopper preferences.

Celebros offers “intelligent site search” customised to the retailer’s business, combining merchandising, meaningful real-time data from its “heatmapping analytics” function, and faceted search capabilities.

Clerk.io offers another searchandising option based on natural language processing, real-time predictive analytics and data display, and “pre-built recommendation logics.” The searchandising components are part of Clerk.io’s broader attempt to help retailers achieve far greater personalisation in omnichannel shopping.

We know there’s been reluctance on the part of many brands to embrace searchandising as part of their approach in making their sites more attractive, friendly, and personal to shoppers. They seem to view it as an added layer of complexity they would rather do without.

But that’s simply not an option for those who want to attract shoppers, engage them before they bounce, and then hold on to them by making product discovery fast, friendly, and fun.

And if you recognize that searchandising and hyper-personalisation are bound together, you really have no choice but to embrace both.

Final Thoughts: Take It Personally

Whatever else you take away from this post, please take this: your brand or e-commerce site is no longer “just business,” with apologies to Don Corleone.

It’s personal.

And it’s going to get more personal as the supporting AI and related tech get better. E-commerce is no longer just (or even primarily) about selling product; it’s about creating long-term relationships with customers who want to feel they can trust their favorite vendors, rely on them, and feel confident in ongoing personal relationships with them.

If CX is really the new battlefield where brands compete against each other, none of us can afford to ignore the tools and tactics most likely to lead to brand victory in online markets.