If you’ve ever noticed an email from a brand addressed directly to you or based on your prior purchases, then you’ll be familiar with marketing personalisation. This was an early broad approach, and we have seen personalisation evolve as organisations have brought new types of data into the mix, including contextual and location specific information, followed by analytics and machine learning for more real-time customer data.
This one-to-one approach was cited as having the power to enhance brand loyalty by delivering a better customer experience (CX). Now, with artificial intelligence (AI) moving into the mainstream, connecting with customer journeys is becoming a reality and is proving a boon for real-time personalisation, or what is now being termed as hyper personalisation.
The benefits for businesses include a positive boost to both customer experience and the bottom line. However, hyper personalisation isn’t as widespread as it should, or could, be. Like the adoption of all new concepts there are challenges, opportunities to be realised and some critical initial steps that need to be put in place.
What is hyper personalisation?
Hyper personalisation is more than just putting the right name on a marketing email. For personalisation to be truly ‘hyper’, it requires multiple types of technology and data to come together to adapt the customer experience specifically for each customer, where they’re engaging, what they’re buying and how they want to experience your service.
Hyper personalisation requires not just data but most importantly the artificial intelligence to make use of it.
Personalisation in real-life
Hyper personalisation is more than just a concept with limited real-world applications. Consider the example of a quick service restaurant. Hyper personalisation might mean a dynamically adapting menu which changes in real time depending on how long the queue is, whether it is breakfast or dinner time, and which ingredients are in the kitchen. Additionally, because the restaurant has a number plate recognition system in place, it can recall your recent orders and offer a quick choice menu to repeat the last thing you bought. It might also be able to up-sell by recommending new items to try that those with similar tastes have also bought.
The impact for both the business and customer is positive. Customers don’t have to swipe through a multi-page menu to find the same thing they order each and every time. Simultaneously, the restaurant can create a more efficient system and increase sales, while the data they gather can be fed into other marketing and operational processes.
As the customer experience is frictionless, customers can feel the significant benefits without realising it. Solutions and pilot ideas are rapidly being implemented into leading marketing and automation platforms. The financial services sector is a prime testbed for this, where cross-selling is an important part of the business model. Consumers who take out a mortgage, for example, may find themselves with personalised offers for relevant services, such as contents insurance.
Similarly, chatbots are an area within which predictive personalisation has thrived. Applying AI has allowed marketing teams to anticipate customer needs and respond with tailored offers, dynamic pricing and more relevant content.
Of course, none of this would be feasible without the consumer demand to support business investment. The principles of customisation and recommended picks, pioneered by the likes of Netflix, are prime for other industries too.
Putting hyper personalisation into practice
Given how quickly consumers have embraced personalisation as offered by services such as Netflix, we know demand exists. The concept also makes business sense; the technology is available, and the platforms are there to integrate and support it. However, as with all new technology there are barriers and challenges for marketers and customer service teams to overcome to successfully adopt a hyper personalised approach.
Integrating data from sources across a business, both physical and digital, internal and external, is an important first step. Algorithms are needed to filter and understand the data and feed it back into the customer touch points. It’s likely that you’ll need multiple iterations and tweaks to get the process right.
Artificial intelligence, real results
Building an effective customer experience strategy using artificial intelligence to deliver hyper personalisation offers significant potential. Aligning with the needs and expectations of consumers is crucial. Organisations set to maximise these opportunities are those that put AI as a strategic priority within their business, scale up AI projects from pilots to business-wide best practice, align to consumer expectations and keep the customer experience central.
The way that consumers buy today is unrecognisable from a decade ago. The concept of personalisation which brought new marketing and customer experience strategies is on the verge of another, even more significant change as we move from personalisation to hyper personalisation.
Customers know their experience is changing. It started with the content suggested by streaming services, and they’re ready to embrace the change more widely. Aided by a boom in AI startups, analytics and machine learning are now being applied to customer data in real time, and organisations leading this revolution are well placed to reap the rewards. Will you be one of them?
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