CMO roundtable: How machines will make or break your customer engagement success

At this Sitecore sponsored event, marketing and CX leaders discuss how they're working to personalise customer experiences, the role of data strategy and analytics in their success, and what machine learning will do to further CX aims

Getting a grip on AI and machine learning

The exciting news is artificial intelligence and machine learning present new ways of understanding and actioning a customer engagement strategy driven by data and focused on personalisation.

To kick off our roundtable, Sitecore strategist, Anthony Hook, explained the current and future state of AI and ML application and what marketing teams must have in place in order to harness their potential.

AI versus ML: A definition


CMO's Nadia Cameron with Sitecore's Anthony Hook
CMO's Nadia Cameron with Sitecore's Anthony Hook

Hype is definitely where we are at with AI/ML right now in terms of what’s available to marketers, Hook said, and understanding the distinction between both is important. He described ML as taking data and applying algorithms to derive outcomes.

“We may even know the answers we want, we’re just validating them. Or we’re looking for answers or questions and their subsequent answers,” he said.  

Where AI can be seen in marketing is in things like intelligent chatbots. “The technology behind it is machine learning, but it feels more AI because the human is having a conversation with a machine,” Hook said.

“The key word there is artificial as opposed to intelligence. That will grow of course as we achieve singularity, which is the point where a machine can have an intelligent conversation, and that technology is accessible to the people we have around the table today. But machine learning is where it’s at for us right now.”

Playing a role in martech, adtech and customer tech

Current applications of ML comes in two flavours. One is in productisation, or where vendors such as Sitecore provide features within their platforms. These include predictive sendtime optimisation for email marketing, and automatically and semantically tagging digital content and images.

The second way is process driven, or what Hook positioned as ‘bring your own algorithm’.

“That’s where we have this sea of data we’re collecting, often in different silos, and it’s about applying data scientists, machine learning and code to those processes, trying to drive that,” he explained.  “Productisation is accessible to everyone around the table; it’s usually a case of the more you pay, the better the technology. Process driven – that is way more complex.”  

Ultimately, productised implementation of ML helps marketers save time. Hook noted recent research that found the average time each marketer spends pulling data together to do analysis is 3.6 hours per week.

“The reality is the answers to the questions are all up in their head somewhere, and most machine learning in the process driven space is supervised. That’s how I think ML will really help marketers: Free their time up and let them focus on doing other stuff,” he said.  


Gleaning customer insight

Where the rubber hits the road in Hook’s opinion in utilising these technologies right now is customer segmentation and audience discovery and in creating “living personas”.

“We’re targeting all our active campaigns to a bunch of personas we believe are true and correct, but ML could well show us there are a huge number of microsegments that could be reprioritised or readjusted,” he said.  

To illustrate the point, Hook noted a number of companies using Invision’s prototyping software to tell the story and creatively plan around these living personas. These are then regularly updated based on data and fed back into audience managers and segmentation tools.

“If we can get into levels of hyper segmentation at the upper end of our buying cycles, that’s where becoming more personalised and relevant becomes achievable,” Hook said.  

Of course, using machine learning to free up time creates another time-based problem: Getting data into one place where it can be useful. With productised ML solutions, Hook said data is usually plugged in.

When you get into process-driven ML, however, most companies face an age-old barrier: Data silos. “Bringing all that customer experience data into one place does make a difference,” Hook said.  

“That might be in a data warehouse, where you have a team happy transcoding and translating data formats into one common format, or it might be a proprietary system where you have connectors in those platforms to pull things together. Once you have the data together, you have the entire customer journey in a big silo you can run data analytics over.

“The success of decisions you make in email automation are 100 per cent impacted by the point-of-sale, retail, offline and ecommerce journey. You need to centralise data. That is both a time and technology thing, but it has to be solved.”  

The second must if you’re going to start investing in ML is trust, Hook said. He noted many instance of ML being rolled out have been via incubation hubs within organisations, and are usually spearheaded by representatives from marketing, IT and data analytics.

One example is Valtech, which worked with Rotmans School of Management in Canada to analyse all data using ML in a proof of concept pilot without telling any c-levels they were doing it. Getting early results paved the way for scaling the solution.

“Getting c-level to trust the process you’re going through is right, and not to demand answers in the first month, is key,” Hook said.  

One local Sitecore customer investing in ML is RAC WA. Under a proof of concept, the motor insurance provider took customer data from marketing and digital platforms as well as other business datasets and pooled this into Azure ML in order to start delivering personalised exit journeys. As soon as a digital customer reaches a thank you or completion page, they’re presented with another product that is relevant to them. In its first month, exit rates on pages dropped more than 4 per cent.

For Hook, the next part of the journey where the model gets clever will see it continually learning to improve accuracy.

“If RAC WA sees a 1 per cent drop per month over the next 12 months on that exit journey page, that’s a massive success,” Hook said. “And it’s justified internally from a c-level perspective on how much work needs to go into delivering that.”  


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