We Are Retail
My Retail Nation. Our job is simple. Sell as many products to as many people as persistently as possible at the greatest profits. That’s it. It’s not that hard.
Yeahhhhh. Maybe it is a bit more challenging. Customer Lifecycles, Seasonal Lifecycles, competition for wallet, competition for attention, managing owned sales channels, a dozen plus marketplaces, developing & optimizing internal personas, driving Loyalty/Credit program take, managing media mix, optimizing merch mix (by persona, by sales channel)…it’s a lot. It’s also why we, as an industry, will spend $104B on Customer Analytics and Insights in 2025.
An Evolving Practice “Customer Analytics and Insights (CAI)”
Over the last two and a half decades, we’ve worked with top retail brands – from Macy’s to Chico’s, from Finish Line to Neiman Marcus, and from Ulta to Bass Pro. Every retail brand we work with has this practice – or was taking steps towards it. They have their data centralized, their version of a single customer view, and are working to align data science and business analysts together.
Customer Analytics (generates…) Customer Insights (that support a…) Customer Opportunity (that results in…) Value Realization.
Even by name, this work implies a “process”. Information gathering, analytical frameworks, model development all leading towards the goal of insights generation…or was it?
In much the same way that I never met an EVP or C-level executive who was after a “single customer view” or was desperately yearning for “more data”; I also never met one who believed that “insights” were a business outcome.
The Single Customer View. Analytics. Insights Generation – they were all waypoints in their path to optimizing profit / supporting an Opportunity.
Enter AI Opportunity Agents
Opportunity AI is the bridge between the data and value realization at a scale that no human (or team of humans) can replicate. The data science behind it is, at times, a bit overwhelming. That said, I like to view Opportunity AI as staff. In fact, that is the beauty of Opportunity AI.
AI Opportunity Agents align directly to CAI roles, noting that the best Opportunity AI Agents communicate with each other.
AI Opportunity Agents – much like the business analysts you would hire – are specialized (because they have to be). They focus on key areas of the business, stratactical opportunities to manage and optimize the customer across every category, product line, sales channel, season, and at every stage in their customer lifecycle. In fact, first-generation Opportunity Agents are delivering;
- Customer Lifecycle – managing and improving customer acquisition through win-back
- Customer Marketing – ensuring customer repurchase and new-to-customer category expansion
- Seasonal Lifecycle – pro-active planning and intra-season customer/sales optimization
- Sales Performance & Demand – identification and response to sales by channel, by category, by persona
- Bridging Forecast to Plan – bottoms up contact strategy optimizing for planned performance
That said – AI Opportunity Agents create value not through Opportunity identification but through the ability to act. For that to happen – they need a teammate.
Audience AI
If AI Opportunity Agents are the keepers of the “what/why/when/where” – the Audience AI is the keeper of “who”. The primary job of the Audience AI is to interpret AI Opportunity prompts and act.
Much like a small army of data scientists…when an AI Opportunity Agent reports that Store #8311 is underperforming primarily due to women’s running products – the Audience AI will interpret those prompts to identify anyone predicted to buy women’s running, in store #8311 in the following 60 days and those who were predicted previous 30 days (and didn’t). Whoalla – Audience! But we all know an audience is more than just a list of customers.
Effective audience AIs will not only create an Audience, but will enable individualized content specific to the opportunity.
Your Audience AI will also enable individualization. It understands each customer’s unique relationship to the opportunity, her brand affinity, price sensitivity, and predicted product lines within the women’s running category.
Audience AIs deliver “contextual individualization” uniting the individual to the opportunity through traditional personalization, merchandising individualization, and even Generative AI (both copy and images).
Some Audience AIs will go so far as to identify if a customer should receive paid media – and if their inclusion will drive top-line sales (i.e. conversion) or bottom-line profit (i.e. lift). That’s my kind of AI.
120M Reasons to Believe
I get it…it sounds like science fiction. Like we are literally living in the 2002 Tom Cruise classic – Minority Report. That said, we are. This is very real, and although still in its infancy, Opportunity AI is already driving massive value.
Opportunity AI, for one of our billion-dollar retail brands, has already driven over $120M (in the last 12 months)
In a recent article – we shared how a billion-dollar retail brand drove over $120M in incremental revenue with AI Opportunity agents delivering outcomes in paid media, earned media and print. While these programs were deployed in a reduced-scale “test” mode, this referencable retail brand believes AI Opportunities deployed at scale will produce a quarter to half billion in incremental upside – starting this year.
<Shameless Plug> Proofcamp: The Ultimate Proof of Concept
Google Cloud and OpenINSIGHTS have partnered together to provide qualified retail brands a complimentary Proof of Concept. It’s your data, in your cloud, with your own Opportunity AI, it’s your Audience AI…you even go program live – so yeah, it’s your profit too.
My Great Big Asterisk *
I want to be clear that I stand firmly on the side of my human cohorts, my carbon-based colleagues, and my bipedal brethren when I say the AIs near-term role is to complement vs compete with your efforts. Opportunity AI currently exists to scale specific use cases. The very human notions of discovery, validation, testing, and communicating knowledge – are absolutely critical and still very much at the core of Customer Analytics and Insights practices.