Are you talking to me?
We’ve all seen it, the email in our inbox or the clever ad that’s tailored to you, or even the LinkedIn InMail. You know the one, the email that’s informal and claims to have talked to your colleagues which is quite obviously fake, it starts off nice with some comment from your LinkedIn or website and then quickly goes into copy and paste mode. The ad that carefully jumps onto something that you looked at and then assumes you are in the market to buy. The friendly LinkedIn message which is followed by a tirade of Sell Sell Sell! We all know because we all see it and we are all people. And very quickly we can see that personalisation in the wrong hands is doing more damage than good. But this is personalisation with a small ‘p’.
If we take a step back and think about what works for us as individuals, where we see value, what is useful for us, then we can see that personalisation is useful. But it’s not the “Hello Michael, we noticed you liked our Metaverse trainers” (a whole new topic) it is the ‘relevancy’, the ‘timing’, the ‘context’ and the ‘personalised’ approach that all work together in harmony to deliver Personalisation, with a capital ‘P’.
The problem now is that personalisation is hard work, it really is. You need to understand everyone, identify where they are in their customer journey and tailor both the content and approach to help them fulfil their next steps. For some people quality will be important, for other speed to implement, and still for other people price will always be the critical factor. We need to build out our personas, build all the multiple paths and variation and then work out which one will work for each individual. If we spend enough time and money, then we will achieve the personalised approach… but at what cost?
In steps AI (Artificial Intelligence) from stage left to solve all our personalisation problems. Afterall, this scenario is perfect, just let the machines do all the data crunching and let the machines build the perfect response. It’s all around us, AI will help us with this and solve all our problems… If you believe the marketing hype from the top players you might be led to believe that it is already here and ready to use at the press of a button, just buy their software and let’s go.
The reality is that most AI platforms are really ML (Machine Learning) platforms. Don’t get me wrong, this is good, but I think it is important to understand the difference. With ML we need to build rules and scenarios, whereas true AI just does it. The reality of today is that AI still needs a human prompt, something to work from. But ML is amazing, it can crunch the numbers, it can work out all the scenarios, it can do things in minutes that would take humans days or even years to complete. ML is delivering the personalised solution to you today… but you still need humans, and you still need to put in the hard work.
Let’s look at some real-life examples to see how ML can really work for us today. Acrisure (https://www.acrisure.com/) is a large insurance company based in North America. They sell a range of insurance products but matching the right product variant to the individual based on what the customer valued was becoming a nightmare. So, as a forward-thinking company, they invested in a good inhouse team of data scientist, data analysts and developers using ML. They are now able to create millions of product options, review these around the induvial and their personal requirements and then present the top three matches to the customer. This process would have normally taken weeks and is now done in real-time and so that customer can purchase. So, no matter who you are, what your personal requirements are or even if you are a quality over price (or vice versa) person, there is an individual product for you. ML has enabled the creation of the millions of variations and the selection of the top three, robots (AI) is not yet in charge it is still over seen by rules and humans. Most importantly, this has taken away the tedious work from the humans and let them focus on what matters.
Similarly to Acrisure, Figs (https://www.wearfigs.com/) have created an inhouse team to focus on ML. Figs are a clothing retailer dedicated to catering for healthcare professionals, and in doing so deliver more than just clothes. Figs ML approach is around marketing and focuses on enriching the user experience, not collecting data. Yes data collection is vital in personalisation, but it is how you do it and the value you provide in exchange for the data. Figs are able to ‘know’ you and deliver the best experience for you, base on millions of variations. They are then able to use this data and ML to reach out with targeted and personalised ad to both prospects and customers. And what’s best about both Figs and Acrisure is that it just works, you don’t know they have all this tech in the back because they add value and deliver what you need and want.
As a side footnote, AI/ML is very expensive and it takes time to develop and it take time for behavioural change (especially in the case of the Sales team within Acrisure), but the value pays-off in the long-term, just ask Figs and Acrisure… these companies are flying! But remember you need good data: garbage-in, garbage-out. Time to get that meeting with CEO and start the 3 year investment plan!
In conclusion, sales and marketing personalisation is here. ML is driving the way and AI is coming. This enables us to create millions of variations and deliver the best match based on the individual for a true ‘Personalised’ experience. But in reality, most of us don’t have the money or resource to deliver true ‘Personalisation’, so most we must be content with ‘personalisation’ delivered through thoughtful segmentation, remembering the importance of context and relevancy, more than pretending we know the person.