Personalisation, individualisation, customer experience. Whatever you call your approach to customer engagement, it’s all about demonstrating mutual value exchange so that you can drive loyalty and give your customers a reason-to-remain.

 
This is always a lively topic at roundtables, such as when we were once joined by Forrester guest speaker Rusty Warner, an analyst specialising in cross-channel campaigns, real-time interaction and marketing resource management.
 
The discussion highlighted the core challenges facing customer experience-focused marketers in a data-driven world. A long-standing hot topic, personalisation was central to much of the debate, especially when it comes to delivering overall customer lifetime value – a measurement criteria heavily intertwined with emerging technologies. Here are some key take-outs…

Data: it’s not what you know, it’s what you do with it…

 
“They sent their client an embroidered cushion with the pet’s name on it for Christmas. What they didn’t realise is the pet had passed away the week before”.
 
This was just one of the many anecdotes Rusty shared as an example of collecting irrelevant information about a customer (in this case the name of their dog), but more importantly, not understanding the appropriate way to put such information to use – or even, if such detail should be considered in the first place.
 
This poses the question: what do you need to know and what do you do with it?
 
The answer wasn’t a one size fits all approach. However, the same common issue of having the majority of information they require to personalise the customer experience, but lack the technology and resource to execute effectively or to measure their success was highlighted across all industries and sectors.
 
Traditional methods of individualisation include the ‘personalised’ offers you receive from your preferred grocery store, they have used transactional data held against a loyalty card to produce a direct mail campaign containing coupons for you to redeem. Great, right? Not always.
 
As you plan what data you are going to collect about your prospects and customer ask yourself, “what is my business trying to achieve and what is the desired customer action?” Your answer will provide the basis of your data strategy…
 
Using the example above, if the grocery store considered the purchase channel they would not only provide a better experience for their online only customers, for example, but also reduce their direct mail costs.

Invidualisation done right, doesn’t look like personalisation… it’s just a great customer experience

 
Individualisation comes down to not necessarily knowing every tiny detail about your customer, and using it all, but instead understanding them and their preferences as an individual.
 
Acceptance (and, indeed desirability) of an individualised approach is built upon best use of technology at hand – with ease-of-uptake crucial in conversion.
 
By understanding your customers’ behaviours, you can make sure you’re delivering value to them, without them having to put work in by changing their normal habits. A much more effective way of engaging them for future interactions.
 
A good way to assess what works in terms of offers and personalisation is not to focus on the uplift in sales of a product sold via a particular campaign mechanic, but rather look at how many of the people targeted in this way didn’t buy.
 
As a consumer we are relentlessly asked by brands about our experience, but what do they do with that data?
 
One RedEye client has already led the way and incorporates survey answers into their personalisation strategy, allowing them to communicate with their customers not only based on interactions but preferences and previous experiences with their brand.

AI-driven marketing automation is the future

 
Embracing new technologies early is crucial to keep ahead of the competition and deliver on customer expectation.
 
Artificial Intelligence (AI) and machine learning is rapidly playing a major role in automation and digital communication strategies, due to the massive volume of data available. Correct use of AI can inform a strategic campaign like nothing else.
 
However, we cannot hand over marketing to the machines; AI-derived insight needs to be used by a marketer for human interpretation.
 
From there an individualised campaign strategy can be developed to create a positive interaction that increases loyalty throughout the customer lifecycle and a long-lasting relationship that delivers on their needs.
 
But it isn’t just AI which provide a basis for personalisation. Nike are a great example of using customer data across multiple touchpoints to deliver an individualised experience, such as VR apps to advise and store the recommended size against the customer profile for ease of future purchasing, both online and in store.
 

What to do next?

Consider what data you already hold and how you currently use this to provide a personalised experience (if at all) to your customers.
 
You should then use this as a basis to build a long term strategy to include additional data required, content to support personalisation, identify channels to include in your marketing mix and lastly the technology required. Then you’ll be ready to move in AI-driven predictive modelling.