Setting the stage:
The field of Machine learning and AI has evolved rapidly in the last few years, especially in fields where large quantities of data and quick response times to queries are crucial. But given lots of these techniques and methods have been around for a much longer period, why has it taken so long for other industries outside of small start-ups and ambitious tech giants to leverage these methods in similar ways?
CRM is an essential component of any company’s strategy. The ability to communicate with and understand customers is more important than ever due to the low barriers to entry in highly competitive global markets. Companies have only brief moments to convince customers that they are the right choice for shopping, spending time, or engaging. Optimising these initial and subsequent contacts is paramount to success.
Beyond just expanding your customer base and attracting new clients, CRM is vital for any company’s retention strategy. The most advanced cutting-edge models in the world are utterly useless if we don’t know how to activate and capitalize on the value they represent.
ML Foundation:
In the CRM space our main goals are increasing consumer retention or spend, and we do this via figuring out the most effective ways to communicate with people. This can be broken down into when to speak to them, how to speak to them and why to speak to them.
Recommendation engines lie at the core of many of these architectures, models that are designed to figure out what you want before you even know you want it. Broadly they work by looking at the kind of customer you are, then at customers like you, then finding things that they’ve bought recently that you haven’t.
You can even simplify this down into just looking for customers who have an identical purchase history to you. Maybe a laptop you can buy on Amazon doesn’t come with a charger, so commonly when people buy this laptop their next purchase is a charger!! (You can often see this simple logic in the “People also bought” section of Amazon). But even these simple implementations are incredibly powerful in some ways, an educated guess is always going to be better than a random one.
So how do these methods relate to CRM? Well, the general structure can be pulled away and applied to any subject. When we think about how to engage with a customer, we’re going to look for ways we engaged with similar customers and how these performed. The customer who likes Sabrina Carpenter will probably need to be spoken to in a different way to the Motorhead fan.
This is simple stuff, right? Well exactly, but it’s a method to show that the underlying AI processes in these platforms aren’t really all that complicated – there’s a lot of room for improvement especially when implementing bespoke solutions with larger data sets.
The next (generative) step:
So, we already have ML methods that can tell us when and why to talk to people, great! But what’s the next step?
All that’s left of our final stage is how to talk to them and what to say, stages which can and are currently being revolutionised by the advent of enterprise grade Generative AI.
A current pipeline for devising CRM processes may involve creating template communications that are then populated with more specific information, for example customers in a certain segment defined by age and tenure are assigned one template and differing segments are shown another.
This approach can be time consuming if it needs to be completed for each campaign, and may miss a level of personalisation that people will respond to, feeling as though each message is tailored to them rather than being an email blast they just happen to be caught up in.
Skilled AI engineers armed with LLM’s can create a unique voice for each consumer, ensuring that quite literally all communication they will ever receive are exactly personalised to them and their engagement habits with your brand.
Imagine attempting this even a few years ago, assigning a team of people to trawl through millions if not billions of rows of data to ensure that each customer got the perfect messaging for them would have been completely impossible.
In practice this level of granularity in communications is probably unnecessary but it speaks to the potential these models have in this space – the sky truly is the limit.
Even starting off small with these steps, giving a small part of a communication a generative component, allowing for large scale A/B testing and continuous model training, the effectiveness of these comms will improve over time.
Freeing this time up from your CRM team will give them more time to tackle more involved problems that can’t be automated.
How can we help you on this journey?
Don’t get left behind. Partner with CACI and our experienced in-house data science teams to integrate cutting-edge ML and AI into your CRM processes and experience unparalleled growth and customer satisfaction. Contact us today to learn how we can help you stay ahead of the curve.
