Getting Personalisation Wrong

Connecting customers to relevant experiences is proving to be extremely powerful, enabling brands to leverage their rich data intelligence to deliver significant incremental gains in sales, engagement & service. As organisations take their second, third and further steps in personalised experience they increasingly ask challenging questions, interrogating and understanding what personalisation should actually ‘be’ for them. These questions come thick and fast as stakeholders across the business recognise that marketing, data and technology can now execute targeted and tailored 1-1 experiences and race to exploit these new capabilities.

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Remember Me

Have we met before? Let’s say we have.

Imagine I didn’t acknowledge that we had met before, it seemed as if I didn’t remember you and you had to introduce yourself from scratch. I expect that you would find me somewhat frustrating.

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Your baby ain’t special…

I’ve just had a baby, and I must admit, if someone said that to me, I’d be pretty mad.

But I’ve got to face facts; soon he’s gonna to be in a nursery with thirty or so other special little spuds. We’re going to have to make some concessions to conformity; he’s gonna need to be standardised!

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Why bother collecting clickstream data?

Why bother collecting clickstream data? Good question.

To answer a question with another question: What are you going to do with it?

“I’m going to store it in a big database, I’m going to collate it up and make metrics out of it etc.” Not nearly good enough.

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Optimisation vs Personalisation

In previous roles, I’ve been lucky enough to have been part of a fantastic optimisation team AND played a major role in delivering a real-time digital personalisation program.

Stakeholders have asked what the difference between optimisation and personalisation is. If you’re not familiar with the detail, they can seem pretty similar.

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Anyone for real-time data?

I’ve been to a couple of conferences recently where there’s been a lot of chat – both on and off stage – about real-time data.

I’ve got to get my data delivered in real-time (apparently), because it’s super-duper important.

I agree – it is super-duper important. What’s much less clear from all the seminars, keynotes and conversations, is why exactly.

Real-time analytics?

Nah, not for me.

A bunch of people spoke about real-time analytics; how they instigated expensive (and expansive) initiatives to get data into the hands of analysts instantly.

That feels like spending a million quid on a super-car just to sit in rush-hour traffic.

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Data Layers

At Loop Horizon, we talk about drawing a direct line between customer intelligence and customer experience – e.g. when the barman at my local asks: “The usual?”

It sounds simple, but the way his brain captures and processes my beer preference is immensely complex (let alone retains it, and makes decisions / takes action based on it).

Luckily for us it’s much simpler to capture and process customer intelligence from digital platforms. One of the best tools to do this consistently and accurately is a data layer.

I talk about data layers more than your average person, so I thought I’d best get some ideas pinned down and shared. Here goes.

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