In recent years, there’s been a lot of talk about digital marketing and how businesses are collecting and analysing data to profile our interests, preferences and habits to create targeted content. If you think about it, it’s amazing how accurate an image you can create of an individual if you accumulate all of their data across the websites they visit, what they post and interact with on social media, and what they purchase online. In many ways, this is why the likes of Google and Facebook are actually increasingly known as data companies rather than a search engine provider and a social media platform. In fact, Facebook has even become very explicit about it, rebranding itself as Meta.
This conversation isn’t new, it’s been around for years now. But it’s recently been dug up again with a little extra vigour as AI has become a major talking point driven by the popularisation of ChatGPT.
It’s stoking a lot of fear. Like with any new technology or new sector, it is starting out with very little governance and control. The clear black and white lines of what is acceptable and what isn’t, simply don’t exist yet. And it’s causing a lot
of debate. What is an invasion of privacy and what isn’t?
But what all this means is that many companies are finding themselves at a bit of a fork in the road.
Most companies, regardless of their sector, are almost wholly dependent on delivering the best service possible by anticipating their customer’s needs. But customer needs are changing faster than ever before, and companies, again, regardless of the sector they find themselves in, are increasingly turning to advanced technologies like AI just to keep pace, let alone stretch ahead of the competition.
So, the fundamental principle of business, knowing your customers, hasn’t changed in the slightest. But the pace and medium for understanding their needs has changed drastically.
Today’s customers want intuitive websites and applications that know what they want before they even know it themselves. They want a transaction whether that’s making a purchase, making a service request or logging a complaint to be completed in no more than 3 clicks. And that’s worst case scenario, most of the time they want problems to be fixed before they even know about them.
But how on earth do you go about achieving that?
As I said at the start, data is at the heart of everything. Data is the currency of the modern world. But data for the sake of data doesn’t mean anything. If you want to leverage these advanced technologies like AI and machine learning, how that data is structured is massively important. The way I make sense of it in my mind is through the simple analogy, if I’ve got access to every book written in history, it doesn’t mean anything if I don’t understand the language it was written in. This is what has become known as information architecture, and the way I remember it is – you can’t have AI without IA first.
In simple terms, IA is simply the structure your data, or information, holds. This is what we mean by “data quality”. Is the data complete? Can we draw insights from it? And that leads to many practical uses. Yes, we’ve got the AI and machine learning use cases but it also extends far beyond that. For example, it's foundational to user experience, the better your information architecture design, the quicker and easier it is for your end users to find what they are looking for.
So how do you go about collecting that structured, quality data?
A simple example that comes to mind is the use of Loyalty Cards by supermarket retailers.
On the surface, we believe loyalty cards are there to buy our loyalty through discounts that are made available by accumulating points. The more we shop there, the greater the discounts we get. Don’t get me wrong, it’s simple and effective, and I’d be lying if I said my preferred supermarket hasn’t got me on the hook. But in reality, it’s so much more than that. For the supermarket it’s a ginormous data mining ploy.
Loyalty cards are a great way for businesses to collect high-quality data that they can use to profile their customers. Have you noticed how no two Sainsbury’s Locals or Tesco Expresses sell the exact same products? When customers sign up for a loyalty card, they typically provide their name, email address, home address, and other personal information. This information can then be used to track customer purchases and create customer profiles. They now know everything from which products are most popular in a specific location, how far people are travelling to use that specific supermarket based on their home address, how many times a week they visit, the size of their shop etc. etc.
This tells them which areas in the country to prioritise opening a new shop, which products to put on the shelves and so on and so forth. I.e. they are using high quality, structured data to inform them on exactly how to run their business. And going forwards they can use AI and machine learning to make decisions even faster and respond to changing trends almost before they even happen.
So, what does all of this mean to you?
Data is the next arms race. Those that are able to capture structured, meaningful data the best are going to be able to personalise services, respond to changing customer trends before they even know it themselves and ultimately outcompete any competitors that rest on their laurels. So have a think:
What data are you capturing today?
How meaningful is it?
How can you use creative solutions like Loyalty cards to increase both the quantity and quality of your data?