November 21, 2017
5 minute read
Back in May, we looked at in-store customisation and how high-street retailers are developing ever-more sophisticated techniques to give their customers a unique shopping experience. Now, we want to shift the focus to the growing area of hyper-personalisation and its uses in marketing.
Hyper-personalisation is another way to improve the customer experience (CX), and as the name suggests, is several steps on from the kind of personalisation consumers are now used to seeing in marketing communications, and perhaps even expect. But we’re now well beyond simply addressing email marketing campaigns to each individual recipient; hyper-personalisation takes data further to give customers the very best and most tailored online service.
With the myriad of new online marketing terminology around today – omnichannel marketing, artificial intelligence, machine learning etc – it’s not surprising there can be confusion about what’s what.
Hyper-personalisation may be thought of as interchangeable with customer profiling, but they are actually two different things. Both approaches enable you to create customer archetypes, but profiling is based on simple demographic info and lumps customers together in groups or segments. Unlike hyper-personalisation, profiling doesn’t take into account customer behaviour, history and real-time context on an individual level across different channels, and so doesn’t give such a detailed picture of idiosyncratic consumer activity.
Marketing methods have been moving steadily towards hyper-personalisation for several years, which is perhaps why consumers are so open to it. In their 2017 Trendspotter report, LoyaltyOne stated that 82% are likely to shop at a retailer that provides them with personalised offers, and 65% prefer personalised communications based on their shopping history. This is kind of consumer experience hyper-personalisation can help companies achieve.
However, there are right and wrong ways to approach hyper-personalisation, and doing it the wrong way can seriously risk irritating or alienating the very consumers you’re looking to attract. Let’s look at some of the companies that are doing it right.
Hyper-personalisation is all about putting the consumer at the heart of your online offering; adapting your web-based interface to reflect the unique interests and preferences of each individual consumer. Unsurprisingly, the term ‘me-commerce’ was quickly coined to sum this up, and there are several major brands using this to great effect.
Photobox is one such company. Like many other online retailers, Photobox sends an automated email to consumers who put items in their virtual shopping carts but for whatever reason, don’t complete the transaction. Shopping cart abandonment is a real problem for online retailers, in fact in 2016 the global average abandonment rate was approximately 77 per cent, meaning that a little over three quarters of shoppers who added items to their carts ended up not making a purchase (barilliance.com).
Photobox’s approach gently reminds customers of the products they had selected, and offers a simple one-click method of reinstating the transaction. Offering a discount on the consumer’s chosen products makes a shopping cart recovery email even more effective.
Looking at how a customer has ended up on your website and adapting your content accordingly is another way of hyper-personalising the consumer experience. If you click through to a Microsoft webpage from one of their tweets, you’ll get a landing page optimised for Twitter users like you, complete with one-click ‘Tweet this’ links.
And then there’s Amazon. As the market leaders in virtually every aspect of me-commerce CX, Amazon is the master of behavioural recommendations. Every Amazon user will find a completely personalised and up-to-date interface each time they access the site, full of products Amazon’s algorithms has identified as likely to appeal based on their shopping history.
It’s not just direct sales that hyper-personalisation can help generate; it can also help to strengthen a service-based brand’s relationship with its consumers and encourage them to keep coming back. Spotify sets itself apart from other music streaming services by creating unique ‘Discover Weekly’ playlists for every user, based on their listening and favouriting habits.
It’s scary how well @Spotify Discover Weekly playlists know me. Like former-lover-who-lived-through-a-near-death experience-with-me well.
— dave horwitz (@Dave_Horwitz) October 27, 2015
Spotify’s “Discover Weekly” algorithm knows me better than I know myself.
— Black BEEN King (@SylviaObell) January 25, 2017
Using a deep learning form of artificial intelligence (AI), Spotify’s algorithm takes into account personal preferences, songs skipped and replayed, and even micro-genre music preferences to curate the best suggestions for each listener. It also can’t hurt that many Spotify users log in to the system through their Facebook accounts, another rich source of musical preference data. It’s working; as of early 2017, Spotify had 40 million paid subscribers, compared to Apple Music’s 20 million paid subscribers (nectarom.com).
Social media is a veritable treasure trove for companies looking to discover more about their customers, but concerns have been raised that marketers have crossed the line between uncovering data that can create a bespoke experience and plain old cyber stalking.
Where data-led services are seen to add value for consumers, they’re far more likely to accept the trade-off of the personal data that enables it. LinkedIn’s Connected app is a good example of this; it cross-references your phone contacts with those in your LinkedIn network to keep you informed of your connections’ updates and activities, in order to develop professional relationships. You can decide whether or not to participate and this element of choice, plus the useful information the app provides, means users are largely happy to opt in.
If companies do use social media as a market research tool, it must be done with care. When Burberry launched a new mascara in 2016, they partnered with Pinterest to create personalised beauty pins – when users answered three questions and submitted their initials, Burberry generated them a personalised, and very shareable, Pinterest board. As the first luxury brand to personalise on Pinterest, Burberry aimed to raise its profile on a major beauty platform and discover more about its target market at the same time.
If you’re already using some form of personalisation in your marketing, it may well be part of your email marketing strategy. You may have databases split into segments based on your customers’ demographic profiles and/or key interests.
But hyper-personalisation can take this even further, for example, have you considered personalising the emails that confirm your customers’ purchases? Emails containing receipts and/or transactional confirmations are the most opened by far, so take advantage of this by taking the opportunity to suggest related products at the same time.
Or, rather than sending email campaigns all at once, try sending your marketing emails at times more personalised to your recipients. By examining past opening behaviours and taking into account your customers’ time zones, you can greatly increase the likelihood of them opening your email and engaging with its content, which should also click through to a personalised landing page.
And have you considered the weather? Good and bad weather can have a surprisingly significant impact on how your email marketing is received, in terms of how it affects your customers’ mood. It’s not hard to see how sunshine can boost the open rate of emails about staycations, but email marketing platform Pure360 also found that B2B campaigns were twice as effective when the sun is out. Conversely, emails about property had double the open rate when the weather is bad (weatherunlocked.com).
With historical weather activity available via open data, marketers can apply past weather patterns to previous campaign data to see how the weather has affected recipient mood and engagement levels. Using predictive modelling techniques, it’s then possible to pinpoint an optimum send time according to weather forecasting and how it’s likely to influence how your customers feel.
As effective as each of these individual techniques can be, hyper-personalisation is most fruitful when implemented as part of an omnichannel approach. This way, you can gather useful data about your customers from a number of different sources – social, mobile, email marketing etc – and put it to use seamlessly across all platforms in highly targeted and hyper-personalised campaigns.
By always providing relevant and beneficial communication, a unified and hyper-personalised approach to your marketing can help to inspire trust and loyalty from your customers. To offer a truly customer-centric marketing strategy, you need to interact with your customers when and how it suits them, and with so many moving parts, automation is a must.
At Quant, we can help your business master hyper-personalisation across all your marketing channels, so talk to us today to find out more.