For marketing leaders, especially in high growth spaces like retail, the pressure to keep up with the latest tech is high. Be more data-driven. Be more customer-centric. Put AI in everything. It’s a lot to keep up with, and it’s enough to drive any marketer mad.

On the one hand, you know you need to be paying attention to new tech – because your competitors definitely are. On the other hand, your data is all over the place and your team is drowning in day-to-day tasks. The destination is important, but getting there is hard work.

Here’s the good news: you’re not alone. A recent survey found that 77% of businesses are struggling to advance their data and AI projects.

In fact, the percentage of organizations that considered themselves “data-driven” actually went down over the last year. Execs are waking up to the fact that this kind of transformation won’t happen overnight.

And there’s even better news. Things like AI and machine learning are gradually becoming more accessible – even to companies that don’t have a full team of data scientists on hand. That means you’ll be able to implement this new tech faster and cheaper than ever before.

But before you go out on the hunt for a shiny new AI platform, there’s some prep work to do. Smart, user-friendly software is important, but it’s not a quick-fix. Before you can effectively implement AI or become more data-driven, you need a solid strategy and plenty of data.

AI vs Machine Learning: What’s the difference?

The terms artificial intelligence and machine learning get thrown around a lot these days, and they’re often used interchangeably. Which likely contributes to some of the confusion many marketers have around what tech they actually need, what data is required and how it all fits within their existing tech stack.

Let’s start with AI. This one is harder to define as it’s a pretty broad discipline. Generally speaking though, it refers to software that can complete a range of tasks without needing specific instructions for each.

Rather, the software can adapt to different scenarios based on incoming data and previous experience – much like a human would.

Machine learning is one way of “achieving” artificial intelligence. Essentially it’s about using data to train an algorithm (or “model”) do a specific task, like making a prediction or sorting data into categories. The key is that the model you start with is generic – it “learns” how to complete its task from the data it receives.

As further data comes in, the model can adapt its process automatically to get better results. In other words, ML is about computers learning, through data, to make more accurate decisions or predictions.

And while it may sound futuristic, ML is already embedded in our daily lives. It’s behind your Netflix recommendations, the (sometimes helpful) autocorrect on your phone’s keyboard, and the suggested search terms on Google.

As consumers, we’re becoming more accustomed to personalised, ML-based recommendations and content everywhere we look.

Understanding the “why”

According to a recent article by Retail Week (gated), the majority of retailers have already bought into the idea of AI – with 74% investing in pilot projects. Yet when asked why they wanted to pursue this new technology, most retailers couldn’t offer a clear answer.

In a separate survey, marketers in a variety of sectors reported that competitive advantage and fear of disruption were their main reasons for investing in AI and data projects.

There’s definitely merit in keeping up with the rest of your industry. But “because everyone else is doing it” isn’t the best reason to pursue new tech.

This lack of clarity is understandable, though. With the hype around artificial intelligence – and the myriad of possibilities from it – it’s difficult to know where to start. When AI can do so much, how do you choose where to apply it?

Fortunately, for CX-driven marketers, the objective for AI is actually pretty simple: create a truly personalized and engaging customer experience.

Of course, that’s still a broad remit and leaves room for plenty of possibilities. So the best advice we can give is to start by addressing a specific business problem – one that matters to your customers.

AI projects can quickly become fancy tech for the sake of earning media attention or ticking a box. But by keeping your focus on the specific problem you want to solve, your team and your customers are more likely to see value from the project. Plus, it should give you a clearer idea of what success looks like.

For example, let’s say your business has a customer retention problem. Too many people are buying once and then disappearing for good.

And although you’re reaching out to those at-risk of churn, identifying them takes time and your re-engagement messages seem to have little impact. Machine learning is perfectly suited to help solve this problem.

An ML model could identify at-risk customers much earlier, spot common characteristics and use this information to reach out automatically, at the first sign of a problem.

With the right data, the algorithm could even predict the offer, content, and channel most likely to re-engage each individual, giving your follow-up messages more impact.

With your ML project focused around this specific problem, you’ll have a better idea of what customer data you’ll need from the outset. And you’ll have a clear metric for success – a reduced churn rate and higher conversions from your re-engagement comms.

If this all sounds a bit overwhelming, keep in mind that with an off-the-shelf solution you won’t need any coding expertise or data scientists on hand. Simply look for a platform or software add-on that addresses the problem you’re facing.

Tackling the challenge of data

Any AI or machine learning project is dependent on data. Without plenty of data points to sift through and analyze, it’s hard for the algorithm to make any sort of accurate prediction.

However, it’s also not just a case of “the more data the better”. To get useful insights, you need plenty of the right data, in a useable format.

The first thing to do, then, is to get your data into shape. Typically, that means getting all the relevant information into a single platform or database, so it’s unified and easily accessible. That will make it much easier to get it into the ML tool you eventually choose.

You may also want to look for a platform that incorporates both AI tools and data management. Some vendors are able to help you walk all the way through the process, from data chaos to AI-powered insight and decisions.

Once your data is in good shape, and you’ve got a handle on what you want to achieve with your ML project, you’re ready to start mapping out a proper strategy and exploring solutions. Of course, it’s not all smooth sailing from this point – there are still some challenges to consider, plus the task of dreaming up some use cases that suit your business.

We’ll cover those topics in the next few posts in this series, so check back soon.

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Looking for help getting your data sorted? Our Horizon platform is built for smarter data management (among other things) – get in touch to find out how we can help.