By 2029, IHL has reported that retail brands will realize $4.9 trillion in direct sales impact driven through the adoption of AI*. The bulk of those dollars won’t be magically created in the economy- they will be moved from one retail brand to another. This sets the stage for massive change/opportunity within retail markets…your markets.
AI’s impact on retail can be compared to the birth of ecommerce
– there will be winners & there will be losers.
Many retail brands are testing technologies, running experiments, and beginning to form opinions on where this technology will go. We get it—it’s a natural path to follow when confronted with something as potentially game-changing as Generative AI, Neural Networks, and “thinking systems.”
The issue is that while some brands are exploring paths – others are executing plans.
We’ve spent the last 10 years helping brands do just that: deploy technologies that mitigate risk while accelerating plans to seize their AI opportunity. We’d like to offer the following 5 tips gleaned along the way.
Tip 1: Choose Your Focus (Ours is the Customer)
Supply chain risk modeling, predictive store replenishment, bottoms-up forecasting – let’s be honest, there are many needs. Our focus is on the customer. Why? Unless you plan on radically changing your business model, the customer is the core to success – and is readily articulable.
Acquire better customers, sell them more products more frequently, more profitably
– as long (and wherever) you can.
Re-read that pull. Beyond the obvious customer acquisition, every single word speaks to competitive wins. From every category expanded or season that the customer participates in, every turn of inventory, and every incremental transaction tacked on at the tail of the customer’s “lifecycle” – one brand’s gain is another brand’s loss.
This is the unfair competitive advantage AI drives.
This is the $4.9 trillion dollar opportunity.
Tip 2: Recognize AI Is Already Disrupting Your Customer Lifecycle.
AI is here, and it’s winning. Modern customer learning systems don’t look at behavioral, transactional, loyalty, or even appended data—they create predictions based on all of it. Your competition is using those predictions to both retain their own customers while taking yours.
AI-driven audiences are outperforming your “best effort” marketing programs
across every media channel – consistently.
And it’s not just execution. These “graph-based” neural networks predict well into future fiscal windows and seasons every time they run. Competitors aren’t guessing on circulation support for 2024/2025 print programs/catalogs, who they can expand into their new Spring 2025 product lines, or what Holiday is really going to look like – they know it.
Your competition isn’t guessing about their future – they are owning it.
Tip 3: AI is the Sum of All Parts (And Its Value Compounds)
Those experiments we mentioned – Generative AI, Neural Networks, Etc…yeah, it’s not about any of one of those systems – it’s about all of them. Your AI strategy should be step-based, introducing new systems as you realize value from the previous.
Our strategy is based on storytelling. We advocate automation, using advanced analysis combined with neural network prediction and prompt engineering. We believe that this not only creates immediate value but also positions brands for what will be the next evolution of marketing/advertising automation.
Many of you will experience AI-assisted semi-autonomous marketing within the next 2 years
(and some of you will experience it much, much sooner).
Tip 4: Behind? Catch up! (No One Will Slow Down For You)
Innovation isn’t slowing down, either. New methods combined with new technologies (such as Energy Entropy-based Neural Networks) allow learning systems to create their own understanding of your customer data universe. This will become the foundation of a “retail-centric” AGI and will serve to further distance the AI-enabled from the pack. But many of you have been working towards this (at times without even knowing).
The next major leap in your AI efforts is 100% founded in your
“best practice” work of yesterday and today.
Your internal identity graph. Your enterprise data warehouse. Your efforts to create global taxonomies. Your projects to operationalize appends. The time spent on persistent customer metrics (price sensitivity labels, spending to HH composition, and more). These are foundational ingredients to your future success.
That foundation a little shaky?
Well, there are answers out there…but they won’t come from the CDP market.
Tip 5: This is Customer Analytics and Insights Modernized
What does all of the above have in common? Most mid-to-enterprise retailers have a customer analytics and insights (CAI) team. Recognizing AI’s role in this practice is essential to understanding it’s value to the practice.
As a core service to the business organization, these data science-focused groups have two primary missions: tactical “evergreen” management & optimization of customer value and supporting more strategic, one-off projects that their business stakeholders request.
These “Predictive Customer Insights” platforms automate “evergreen” customer insights
while empowering a retailer’s data science teams to achieve more – faster.
Predictive Customer Insights Platforms look and feel like CDPs—if the CDP knew it had a job to do. Many of these technologies are delivered as “Infrastructure.” They deploy software as a service and are managed like distributed technology, but they run in your cloud (delivering massive acceleration within months, not years).
Shameless Plug: OpenINSIGHTS and Google Cloud
OpenINSIGHTS is full-stack predictive customer infrastructure that runs on your own Google Cloud. Trusted by some of retail’s largest brands, it takes you from source system/legacy data warehouses to industry-redefining AI capability in weeks—not years.
OpenINSIGHTS delivers digital transformation with predictive destinations
for the retail and DTC market. pwc
We can prove our claims – all of them. OpenINSIGHTS has worked with our friends at Google Cloud to create a Proofcamp – a chance to experience real AI, with your own data set, in your own cloud, with your own use cases – delivered within 90 to 120 days. Reach out to our team today – as space is limited – your future is not.
