The marketing industry has a new hot topic: hyperpersonalisation! In this guide, we’ll explain what is meant by ‘hyperpersonalisation’, and why there could be real substance behind the idea of making marketing not just personalised, but hyperpersonalised.
Hyperpersonalisation is the use of technologies such as data analysis, automation and A.I. to provide highly advanced forms of personalisation in the customer experience of individual customers.
In a hyperpersonalised customer experience, factors such as a customer’s real-time engagement with a brand and their inferred psychographics (e.g. their values, goals, desires, interests) are used to decide when and how the brand interacts with the customer. Common features of hyperpersonalised customer experience include personalised product recommendations and tailored special offers/promotions.
It would be easy to dismiss ‘hyperpersonalisation’ as a buzzword. It does sound a bit like something out of Mary Poppins. But the term is useful, in that it expresses a distinction from industry-standard examples of personalisation..
“Regular” personalisation usually groups customers into audience segments or customer personas, and then serves each customer marketing tailored to their segment/persona. The person doesn’t really get unique, personalised marketing; they get targeted marketing based on the brand’s segmentation. Hyperpersonalisation is different: it tailors the customer experience to individual customers, based on real-time, data-driven knowledge about that person (although it may still look at similarities between customers as well).
Let’s put it this way: whereas personalised marketing might offer a person a croissant on the pretext that they’re from France, hyperpersonalised marketing might offer a person a croissant because their customer history shows that they often buy flaky French pastries at around this time, on this day of the week – and by the way, they usually say hold the butter, s'il vous plaît.
In a nutshell, you might say that the real meaning of hyperpersonalisation in true personalisation.
Hyperpersonalisation of customer experience is done using a combination of technologies, typically including data analysis, automation and A.I..
The brand continually gathers data on its relationship with each customer. In a full-scale hyperpersonalisation, data is gathered from all available customer touchpoints to create a single customer view (SCV). This gives the brand a standalone, real-time set of data for each individual customer, detailing their interaction with all relevant marketing channels/platforms. Or, in a more limited hyperpersonalisation project, data is gathered across relevant channels.
This data is leveraged through automation to give each customer a hyperpersonalised experience. Personalisations such as adjustments to the landing page a customer sees, timings of the emails they receive, or selection of special offers and discounts, are done automatically by automation tech.
A.I. is often used to continually improve hyperpersonalisation, learning from each interaction to enable more effective personalisation in future. The technology monitors and interprets how the customer responds in each situation to precisely determine their preferences.
Many brands which use hyperpersonalisation will set it up with the help of one or more partners specialising in areas of digital customer experience. Such providers include:
In recent years, several mainstream martech providers have started offering hyperpersonalisation features. For example, Shopify offers e-commerce brands the capability to automatically personalise CRO options for individual customers, based on the customer’s observed historic behaviour.
So, hyperpersonalisation is not only an approach that’s being implemented by individual brands; it’s also a principle that we can see at work in the fine detail of current marketing technology.
Hyperpersonalisation is being used at varying scales by a diverse array of brands and platforms.
To date, some of the most prominent users of the approach have been very large companies – especially those with a digital/online offering. For instance, Instagram is renowned for its hyperpersonalised ad targeting, while Amazon is known for highly effective personalised product recommendations.
The high rate of hyperpersonalisation among big tech brands makes a lot of sense. By definition, these brands have numerous customers who collectively generate a lot of user data. The more data you have, the more reliable that data becomes for personalisation purposes. Furthermore, the likes of Amazon, Instagram and others have their own proprietary apps, and this gives the brands a reliable route to obtaining consent for processing of the user’s data.
Case study: Starbucks ‘Deep Brew’
There can be few better examples of hyperpersonalisation in marketing than Starbucks’ ‘Deep Brew’ project.
Starbucks has long since been a pioneer of digitalisation in a cafe setting. The chain’s app launched in 2011, and is now used to pay for around a quarter of the brand’s ~100 million weekly transactions.
In 2019, Starbucks took its data-driven innovations further with the launch of Deep Brew – an initiative to introduce elements of machine learning into the company’s operations. This included the addition of hyperpersonalised product recommendations for customers, which take into account real time variables such as the customer’s preferences, the weather in their location and stock levels at local branches.
If you’re interested in how A.I. and machine learning can play into hyperpersonalisation of marketing, we strongly suggest you watch the video above.
The key driver for implementing hyperpersonalised marketing is improved customer experience – which, in turn, can support marketing objectives such as increasing customer retention, conversion rate and sales.
The idea is to make your brand’s relationship with a customer more personally relevant and emotionally intelligent. The customer should be made to feel recognised and understood as an individual.
Some of the tangible examples of hyperpersonalisation benefitting both the customer and brand are exceptionally relevant product recommendations and promotions. Serving the customer the perfect promotion at just the right time caters for customer wants and needs, while securing sales, revenue, sign-ups or other desired conversions for the brand.
Another benefit to hyperpersonalisation can be decreased customer service costs. Very effective hyperpersonalisation will preempt some customer service cases before they create a problem for the customer, and this may remove the need for long-winded resolution by a customer service agent.
Above all, hyperpersonalisation can make a brand seem more relevant to customers and increase brand loyalty. Strikingly, Financial Express reports that 96% of B2C businesses say personalisation increases brand loyalty. Most marketers will be able to recognise this effect from their own experiences with platforms that use hyperpersonalisation. For instance, you might have found yourself using an app like Instagram and being struck by how relevant the platform’s advertising is to your personal taste. This is hyperpersonalisation at its best: delivering helpful, relevant marketing that gives the customer a reason to keep using the brand/platform, while securing brand loyalty and conversions for the brand.
As we’ve discussed, there are some great potential benefits to hyperpersonalisation. However, it is not without good reason that some commentators have said hyperpersonalisation “couldn’t and wouldn’t work” for most brands.
There are currently some pretty stubborn obstacles to implementation of hyperpersonalisation – including:
Problem #1: access to skills and technology
Although some ambitious smaller brands use hyperpersonalisation, the trend remains most prevalent among very large companies such as Amazon and Meta.
Most organisations have been held back from adopting hyperpersonalisation due to a few particularly challenging factors. First, hyperpersonalisation technology has not yet reached a level of maturity that would make it accessible to non-specialist users. Against the backdrop of a data and automation skills gap in the marketing industry, many marketing teams simply haven’t had the right team members to implement and join up the advanced technologies that underpin hyperpersonalisation.
Problem #2: data privacy and security
Hyperpersonalisation relies on real-time awareness of a customer’s context. This requires a balancing act between meeting a customer’s needs, and respecting their privacy. One person’s idea of hyperpersonalisation could be another person’s idea of surveillance capitalism, and it’s clear that marketers will need to work to understand what is a comfortable level of personalisation for their own customers.
There’s no escaping the fact that hyperpersonalisation is data-intensive. The brand needs to gather, store and process a lot of data on each customer, in order to facilitate personalisations. Meanwhile, social, legislative and technological forces continue to place new obstacles in the way of brands’ capability to track individual users – from Apple’s changes to third-party tracking in iOS14, to the emergence of martech providers and advertising platforms championing a “privacy-first” approach to marketing.
It is no coincidence that some of the brands best known for hyperpersonalised customer experience – the likes of Instagram and Amazon – have their own first-party tracking infrastructure in forms such as mandatory user accounts for apps and web-based services. If the customer wants to use the brand’s platform, they have little choice but to allow it to track them, at least while they’re on-platform. Further, these brands tend to take a bullish approach to cookies consent, using tactics such as situating cookies controls in a settings sub-menu, which makes it harder for users to opt out of the tracking tech.
Even if a brand does have the right strategy to gather the customer data necessary for hyperpersonalisation, all that data brings its own challenges, especially with regards to data storage and security. Any brand considering hyperpersonalisation should ensure they have sufficiently strong capabilities in data security and compliance.
Inevitably, some customers will reject a brand’s requests for consent to data gathering in any form. Brands with a hyperpersonalised marketing approach should also have an awareness of these tracking-averse customers, who will require a separate marketing strategy.
Problem #3: stakeholder buy-in
Many digital marketers will feel like they’ve only just climbed the mountain of persuading key stakeholders in their organisations to sign-off on a digital transformation of their brand.
Now, they have to pull off the same feat for automation and hyperpersonalisation of customer experience.
Getting stakeholder buy-in for hyperpersonalisation is understandably a daunting prospect for many marketers – but if you feel that this could be a key strategic move for your brand, it may be worth your while to make the effort. Study up on your stakeholder management, and compile some resources on hyperpersonalisation which you can show to key stakeholders on your team – we recommend Deloitte’s guide, ‘Connecting with meaning: Hyper-personalizing the customer experience using data, analytics and AI.
Incidentally, if you’re a hyperpersonalisation advocate in your organisation and your colleagues aren’t convinced, you may find that it helps to present your plans as a natural evolution of an existing digital transformation project – a truthful framing which avoids stressing the transformative scale of hyperpersonalisation.
We’re sure you’ll agree by this point that hyperpersonalisation is a challenging topic. On the one hand, it can facilitate a close relationship between brand and customer that brings benefits for both parties. Done right, hyperpersonalisation feels like a paradigm shift for marketing.
Yet on the other hand, hyperpersonalisation remains held back by data privacy concerns, a skills and capability gap among marketing teams – and more fundamentally, by the feeling among some consumers and marketers that hyperpersonalisation is just too… personal.
Whether or not a brand should consider hyperpersonalisation can be seen as a question of brand fit. It is not enough for a marketer to have an interest in using hyperpersonalisation – the approach should also suit the brand’s relationship with customers, its customers’ preferences, and the business’s capability to undertake a transformative project on the scale of setting up hyperpersonalisation.
Option #1: Test hyperpersonalisation at small scale
A great option for those interested in hyperpersonalisation but not yet convinced, would be to try simple, small-scale hyperpersonalisations and measure the outcomes.
For instance, if you have an online store and you’re a Shopify user, you could use the platform’s plug-in-and-play personalisation features. Or, if you do email marketing with MailChimp, you could use the platform’s ‘Send Time Optimization’ feature, which uses all of the platform’s data on a recipient’s email address – including when they opened emails from other senders as well as your own brand – to identify the best time of day to send an email to an individual. Many digital marketing tools and platforms have added features such as these in recent years – and they are the quickest route to trying out hyperpersonalisation tactics at a small scale.
Find easy ways to set up mini hyperpersonalisations, and measure the results to see whether the approach fits your brand and customers.
Option #2: Maximum hyperpersonalisation
For some brands, hyperpersonalisation is clearly a winning strategy. Despite its challenges, the approach suits the brand’s relationship with customers, and could provide clear benefit to brand and customer alike.
If this sounds like you, then a large-scale hyperpersonalisation project could be appropriate for your brand. This means applying hyperpersonalisation to many aspects of customer experience, in order to create a highly automated, highly personalised relationship.
For most brands, the best approach to rolling out wide-scale hyperpersonalisation is to partner with a CRM provider specialising in A.I. and automation. Working with a specialist partner is a relatively accessible (albeit expensive) way to equip your team with the right knowledge and technology to deliver hyperpersonalisation.
Alternatively, you could hire hyperpersonalisation specialists to join your own team and spearhead the project. The only trouble with this option is that there aren’t many professionals on the job market who have the relevant skills.
Option #3: People-powered personalisation
For brands which are unsure about implementing hyperpersonalisation, one alternative would be to invest instead in good, old-fashioned, human-to-human customer service.
As we’ve discussed, A.I. and automation are key to hyperpersonalisation. Some benefits of hyperpersonalisation – such as sending emails with perfect timing, or cutting the cost of customer service at scale – rely heavily on these technologies.
But in other ways, human team members are better equipped to provide a truly personal customer experience. Think of the barista at your favourite cafe who knows your order by heart, or the Savile Row tailor who learns all about her client’s lifestyle and preferences – as well as taking their measurements – before making their suit. Excellent in-person customer service is the forerunner of hyperpersonalisation, and many of the benefits are the same, from increased brand loyalty to reduced complaints.
People-powered personalisation is undoubtedly an option best-suited to smaller, premium brands, and some businesses can no longer afford to provide this level of personal service. Nonetheless, it’s worth asking the question: is the best way to make customer experience more personal really to lean more heavily on technology; or should we invest in our people instead?
Focusing on manual customer service doesn’t necessarily mean losing all the benefits of digital. In fact, CRM software can be used to digitally record customer histories, providing team members with the information needed to tailor their approach to each individual.
Successful hyperpersonalisation can be done in many ways, and to various extents within a brand’s customer experience.
However, whatever specific approach you take, there are a few key requirements that should be met. The customer experience should be:
You can identify the right solution for these three points, relative to a particular customer, by looking at the information you have about that customer. What messaging got the best response from them, at what times/on which days of the week are they most responsive, and which types of communication did they interact with in the past? Answering these questions is key to hyperpersonalisation.
Digital B2C or B2B relationships give the marketer unprecedented opportunity to quantify their customers’ needs and preferences. Effective hyperpersonalisation – despite its potential flaws – can provide a way to systematise how your brand acts upon these customer insights, creating desirable outcomes for all parties.
In today’s episode of the Digital Marketing Podcast, Ciaran interviews David Allison to better understand what we really need to be paying attention to, to understand and influence people’s buying decisions.