Why hyper-personalisation is marketing data gold
Marketers realize en-masse that smart use of data moves the needle. It is great to have management back a data-driven philosophy - but we also need the infrastructure in order. That is the only way to take your data strategy beyond the Obvious stage. Let’s look at the appeal of hyper-personalisation and work our way back to the essentials that you need to get in order right now.
Customers never demanded an omni-channel marketing experience
First, a bit of grounding. Pundits will preach that customers demand an omni-channel experience (just read any introduction of any marketing article of the last 5 years). Pfff, I am not buying it. Customers aren’t actively demanding or expecting an omni-channel marketing experience.
Do you expect your hairdresser to know if you like gel or wax based on your site-purchases? I haven’t seen anyone throw a fit at the supermarket checkout, angry that their click behavior wasn’t analysed to get personal coupons - and that lack of omnichannel demand is more than fine.
Customers do show that it is worth the effort to use data. Through their behavior and attitude. The clearest signs are when you fail to meet the minimal expectations of relevance.
If you aren’t interesting to them, don't expect your audience to show interest. The implication of not matching communications and customer need is pretty apparent. They tune out, ignore your messaging, because, why would they stick around?
Taking Marketing from Obvious to Ingenious
The obvious match product-to-person is where a typical data-driven story ends. It leaves the organisation confused. A company is doing pretty well despite not being focussed on data. Making it a “Marketing luxury” to have their data strategy completely in order. Next to that, you see organizations doing a few ad-hoc campaigns, but without the expected customer data driven delight.
That is because it takes three steps to take the data strategy from Obvious to Ingenious.
Level 1: Obvious
Part of marketing presenting lagging results is a general lack of understanding of the customer needs (flawed product value proposition). Another part can be poormans messaging execution - eg: your messages are very boring - this can be fixed by getting these basics of marketing strategy and execution in order. Your data can already inform the analysis in this stage. The rest of the data strategy can remain set to “store and ignore” - and you would still have the results rolling as long as your Basics are strong.
Level 2: Serious
Next, you DO want to know what the interests of specific potential customers are. And even more what the best timing is for your messaging. It is where customers show Intent. And marketers to start using intent data. Segmentation and event-driven marketing requires the right marketing infrastructure already to be in place.
Level 3: Ambitious and Ingenious
The Ambitious to Ingenious level aims to meet expectation and then one-up on the Wow-factor. Marketers are excellent experience engineers. Put everything in place so the customer can experience “Impactful moments”. This is very selfish of those marketers though, because it has shown to have a business impact to reach this level.
This 3rd level requires something more from your MarTech stack. Marketing technology is a bridge and data needs to be able to flow from signals to execution. That can be quite a challenge when data is fragmented. Although research on vendor satisfaction shows that this doesn’t necessarily means choosing a all-in-one stack over Best-of-Breed.
Where Personalisation changes into Hyper-Personalisation
Personalisation is taking some of the data available and inserting it into the communication. For instance an email with a customer's first name or showing the nearest shop location. We call it Hyper-Personalisation if we also personalise other aspects like Timing, Messages based on preference, intent and channel. (the Hyper is added, just to show we aren’t kidding).
The main difference is that hyper personalisation uses data in a more advanced way.
For example, emails can use real-time data like app or site browsing behaviour. Imagine if you could send an email to a customer right at the moment they looked at a specific page on your website. Add in purchase data and dynamic content to tailor what is shown in that email person-by-person.
It is good to know that enterprise companies are starting to use Customer Data Platforms (CDPs) to tackle the data challenges that come with these higher levels of Marketing sophistication. It is easy to confuse CDP with other database and marketing software acronyms, I have written about it before, as you want to know how a Customer Data Platform is different to CRM, DMP.
With data, Real-time makes for a great time
Real-time is more than a buzzword in this case. You want to be able respond directly when a customer shows certain behavior that is generating data. this is called a signal, resulting in an action (or non-action). So we need to automate marketing campaigns to fire when ready. Linked to data that makes it possible to predict the right action in their context.
An example was presented by Dr. Markus Wuebben from CDP vendor CrossEngage, on how the German railway company Deutsche Bahn (DB) is creating amazing moments.
From research, DB learned that customers don’t like crowded travel and that their customers are more likely to upgrade to the first class if the train is full - if they were only given the chance. So they send a push notification directly to the Deutsche Bahn app with a discounted upgrade to first class when the train is too crowded.
The CDP combines the personal data with contextual data (how busy the train is on their trip), to make this happen. It is an Impactful Moment a customer will definitely remember, as well as direct profit generation for DB.
To keep customers active, interested and involved, you don't just want to know more about the person. More sophistication is added once you introduce product data to your marketing database. But this example goes even one step further. You also want to play to their (contextual) needs. Agile data as it is called. Data that can move quickly, is flexible and allows you to responds to changes.
And quickly plugging into new trends, emerging channels and fresh (Iot) devices. It makes for a lot of new opportunities, but also more data sources to manage. AI and predictive analytics need that data to work well. I’d say if you are looking to future-proof your marketing software and have a hyper personalisation ambition, you have to anticipate on the way data will be handled in the future and might take a CDP into consideration.
Sam Wong is a digital marketing consultant and previously hard-core World-of-Warcraft gamer. When he talks about hyper-personalisation, it doesn’t really fit with the definition we made, but you could say it is actually better. You see concepts of Meaningfulness and data infused. Creating experiences that a customer isn’t expecting and that a customer will actually remember. The customer (even if they have a marketing background) cares more about experiences, than marketers tend to think.
Where are you seeing the Data come together?
Hyper-personalisation sounds smart, even science-fiction like, but it is not fictional at all. The most familiar example of effective use is Amazon, a company that puts data at the center of their strategy.
Now this type of technology is becoming more affordable and within reach for mid-market and enterprise companies. Keep an eye out and you will notice how brands with a excellent customer experiences have done something smart with their data to take their data strategy from obvious to ingenious.
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