Personalisation is a necessary component of customer experience in today’s environment. Providing individualised experiences promises to drive sales and increase loyalty by making each customer feel as if the brand knows them and can anticipate their needs.
Yet many companies still struggle to get this right. What can you do to ensure that your efforts to provide individualised, personal experiences create that right connection in the moment, when customers want it and how they want it? Secondly, how can you avoid costly mistakes around personalisation that don’t fit the brief?
How personalisation became the standard
To begin, we have to examine what personalisation is and how it has become the standard in customer experience. According to customer experience consultant Shep Hyken, personalisation involves “…Creating an experience that’s just for them [customers] and no one else; recognising them and making relevant suggestions that you wouldn’t make to others, and appreciating them for their past business.”
To do this, we rely on data analytics and digital technology to deliver messages and recommendations tailored for each individual customer. Customers today now expect this level relevance to them and their lives, while trends in technology can quickly reshape those customer expectations for how brands should deliver these experiences. It’s a constant dance between data, technology, and consumer behaviour, with each one fueling the others.
What drives the consumer’s desire for personalisation? Ultimately, it’s that very human need to be seen, recognised, and known as an individual. Meeting this desire for a connection with others attracts customers to personalised experiences, and fulfilling it well keeps them coming back.
Of course, personalisation is not an invention of the technology age. In the luxury goods industry, companies would live or die based on their ability to create one-on-one relationships and curated knowledge of each customer’s preferences, tastes, and desires. What has brought this to the mainstream is the power of technology – in particular, the use of big data, and most recently, machine learning.
Online retailers were the first to set the current standards for personalisation in customer experience through product recommendations based on browsing and purchase history. At the start, the novelty of being recommended products was enough to create the impression that the service would know what you were interested in.
However, these initial impressions would wear off with poor recommendations that were not right. With small data sets to start with, any recommendation could be thrown off by an outlier or rogue purchase. Secondly, buying products as gifts would throw off the algorithms and analytics, leading to recommendations that were not personal to those buying.
Trust is foundational
The availability of more data – and more nuanced analytics – has led to a second wave of personalisation. These services appear to know us better, and make better suggestions for what we might want. According to our research, more than half of adults today would share data in order to get better service.
What all personalisation efforts have in common is a foundation of trust. Every marketer knows there is a fine line between how much data a customer is willing to provide and how much they trust the brand, how well they want the brand to know them, and their overall tolerance for being marketed to through campaigns that may or may not feel relevant to them.
If customers feel you value their data as much as they do, then they will be happy to continue trusting your recommendations. If you don’t provide good service – for example, if your analytics gets it wrong and recommends a product that clearly shows that you doesn’t really know the customer – then you risk breaking that trust.
Your data must get it right
One of the issues here is that many of today’s personalisation and recommendation strategies have been based on large, batch analytics projects. Batch analytics involves looking at huge volumes of data after the fact, rather than in real time. As these results don’t represent what customers are looking at right now, they can run the risk of being less accurate.
Instead, real-time analytics on customer behaviour has to provide recommendations in the moment, while customers are making their decisions. That’s when they need to feel that a brand anticipates their needs and desires, and saves them time by recommending or offering what is most relevant to them, right now.
How can you achieve this in today’s fast-paced, real-time online world?
New approaches based on machine learning gather customer data points and, through algorithms, use that data to predict what a customer would like. However, machine learning is not enough on its own. Today’s newest approaches to personalisation get closer to the customer in real time, so that all this information is brought to bear during any decision.
To achieve this, you have to look at how and where all this customer data is collected, stored and then analysed to create personalisations within any app and website when a customer is making a decision. It’s here that the power to provide near-instantaneous personalisation lies.
To get this right requires data systems that are designed for high volume, are always available and are highly flexible to meet these growing real-time data collection needs. In addition, a great deal of computational capability is also needed that is capable of processing billions of data points per second and customising outputs to each individual customer. By harnessing the power of an ‘active everywhere’ distributed database, you can create that personalised experience that happens in real time, right when the customer is most likely to appreciate the recommendation and make a purchase.
This real time experience represents the future for personalisation. By working in real time, you can overcome the issues in more traditional personalisation projects and exceed what customers expect from brands right now.
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