There is a lot of chatter around Artificial Intelligence (AI) and machine learning (ML),
and while some brands are leveraging these tools, many still aren’t taking full advantage
of them. According to Brian Walker, e-commerce expert and CSO at Bloomreach, there
has been meaningful progress over the last three years, but there’s much work still to
do. He shared his perspectives on the use of AI and machine learning for commerce
Fulfilling the primary use case for AI
The primary AI use case for e-commerce and marketing focuses on data, according to
Walker. Companies are bringing together all their customer and product data in a
central location and leveraging AI to normalize that data. Customer Data Platforms
(CDPs) are a foundational technology that has helped make this happen. But now that
you have that complete and accurate view of your customers, what’s next? How do you
act on it and make it a routine part of your business process?
Walker argues that you need to get your people to think about first, leveraging data to
drive desired outcomes. That can be a hard thing to do. Many merchandisers and
marketers grew up thinking their job was to drive ideas, outcomes, and content. But that
approach doesn’t scale, especially when you start talking about creating personalized,
We’re on the cusp of (another) ‘revolution’ where AI will impact marketing and
commerce. That’s the revolution everyone is talking about today. You can’t read three
posts on LinkedIn without someone talking about AI-generated content and
images(ChatGPT, Dall-E). AI-generated content isn’t exactly new – many solutions are
available that help generate subject lines and ad copy using the power of AI and ML –
but this new breed of technology is pushing us further, faster.
That said, this isn’t the next use case for AI. Instead, the next use case focuses on
activating on the data companies have unified in their CDP or similar platform.
You have a unified data view – now what?
A lot of companies have invested in CDPs, but they have yet to put in place the
processes and solutions necessary to act on that data. Walker believes companies
aren’t using AI effectively in their marketing channels – not search, merchandising, or
digital experience content. That, he said, is the low-hanging fruit for most companies
So how do you take that data and activate on it in an automated way? You need to
leverage AI and ML, Walker said. You must continually test and optimize, generating
variations of content and experiences, and see what works. That’s where AI comes in.
Scale helps, but AI models have improved to the point where any size company can
take advantage; it’s no longer just for large enterprises with massive datasets.
Bloomreach has focused its product, Bloomreach Engagement, on enabling that
activation, which Walker pitches as a key differentiator for the company.. It
combines a customer data engine(technically a CDP), activation tools, and integrated
marketing channels. AI and ML are baked into the product and enabled through self-
service tools for marketers and merchandisers. Walker said Bloomreach has been
optimizing its algorithms and machine learning for twelve years across many commerce
verticals, so brands get the value of that effort without having to invest in writing
complex algorithms, investing in data scientists, or writing bespoke solutions.
Marketers and merchandisers get the controls and insights that enable them to
understand what’s happening, and they can make data-backed decisions to personalize
It’s a symbiotic relationship because you (the marketer) are telling the AI/ML what to
pay attention to, and you are fine-tuning experiments and testing ideas. The AI is
leveraging all your customer data to run those experiments and determine what
customers ultimately want.
Many are still trying to catch up
We know personalization is good, Walker says,, because customers respond well when
it’s done effectively (and lots of research says customers want a personalized
experience). And according to a CommerceNext study , 52% of companies plan to
continue investing in personalization this year, even as other investments are declining
due to a poor economic environment.Also, a study by Pecan found that 51% of those
surveyed said increasing personalization would significantly influence the decision-
There is a level of cynicism towards personalization because there have been (and
continue to be) many failed attempts at doing it. Many of these failures go back to not
having that unified dataset because you end up personalizing only parts of the
experience without it. Walker also mentioned the issues around data privacy and the
feeling of being stalked on the internet – not personalization challenges exactly, but
relevant to the over experience.
Walker concludes by saying AI is necessary to scale out and deliver end-to-end
personalization. And there have been a lot of improvements in the tools that help brands
get caught up.
Brands embracing personalization (and AI) are looking at how they can extend it
beyond marketing channels. Walker suggests there is a huge spectrum of important
insights that you can gain by paying attention to all parts of the customer journey. As an
industry, we’re only focused on the first few (marketing optimization and site experience
conversion rates being two). But Walker pointed to other areas where AI can have a
positive impact, such as returns, which greatly impact business results and the
This conversation focused on B2C commerce and marketing, but the implications for
B2B also ring true. Personalization is necessary, but it needs to be done well. That
requires a unified customer view (which itself requires some AI and ML), but that is only
the beginning. It’s what you do with that unified data that we need to talk more about
and understand how AI and ML are leading the way to deliver the experiences
And that revolution mentioned earlier? It’s bringing forward another set of innovations
that will impact copywriting, graphic design, and content creation, Walker said. We’re
nowhere close to commercializing it or impacting customer experience, but he said you
need to start preparing for it, including putting in place the processes and culture to
leverage these tools.
This article originally appeared in Diginomica
Featured Image by fabio