According to a recent article by Retail Week, 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 countless possibilities of 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 “why” is actually pretty simple. The objective of AI is to 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 place to start is with a specific business problem that AI could help you solve. Consider challenges that not only impact your team but also affect the individual customer’s experience.


AI projects can quickly become fancy tech for the sake of earning media attention or ticking a box. But by keeping your focus on a specific challenge you want to address, 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. AI - specifically machine learning - is perfectly suited to help solve this problem.


A machine learning 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.

And the measure of success is clear – a reduced churn rate and higher conversions from your re-engagement comms.
 

Still, if you’re not a programmer, applying machine learning to your marketing can seem like an impossible task, if only from a technical point of view. And many applications of AI do require big budgets and teams of in-house data scientists to pull off.
 

Fortunately, though, this is beginning to change, as more products come to market with AI and ML tools built in. Many of these tools are even designed around a specific marketing challenge - like writing the perfect subject line copy or brainstorming ideas for new content.
 

The next step for marketers looking to capitalize on this tech and get ahead of the curve is two-fold. It starts, as we’ve discussed, with identifying a problem to solve.
 

Once you’ve done that, you’ll have a much better idea of the data you’ll need in order to address that problem. AI depends on data to function - without input, there’s no output.
 

And that’s the second step in preparing for an AI project. There’s little use investing in an AI platform if you haven’t got enough of the right customer data.
 

But with good data and a clear purpose, you’ll have what you need to choose the best software and support for your AI project - and keep it on track in the long run.

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