In today’s hyper-competitive marketplace, customer expectations are evolving faster than ever. Marketing leaders are under pressure to deliver personalized, meaningful engagement across every channel, and they must do this while navigating an increasingly complex technology landscape.
In particular, artificial intelligence has quickly transitioned from the Early Adopter to Late Majority phase. Today, AI powers everything from smarter recommendations and predictive analytics, to adaptive campaign management, agentic automation, and Segment of One at a scale of a hundred million customers. But why then, are so many omnichannel brands still struggling to leverage AI?
There are 5 key reasons why these brands might leave money on the table, despite huge investments in enterprise-grade software. They are:
Neither you nor your technology partners want these problems, but they happen within even very expensive enterprise-grade environments that claim to have implemented advanced AI capabilities.
How can that happen?
There are two key reasons. The first is technical debt associated with legacy and interdependent architectures, which can slow down model deployment and iteration. As a result, platform roadmaps are forced to devote a sizable share of engineering time to technical debt service, rather than new capabilities. Second, corporate governance and risk controls raise guardrails related to data exposure and acceptable use. Although such controls are prudent and necessary, R&D efforts within such platforms are slower, as compared to the agility that AI-first startups enjoy.
In short, there are structural reasons why big legacy systems lag behind the AI-First segment – sometimes very significantly. AI-First challengers are able to test and ship faster. So if you’re competing with a brand that’s leveraging such capabilities, then it might be a bad idea to patiently wait your turn for the next release from your current stack. You might underperform the market.
To better understand why this is, let’s take a look at how SOLUS.ai works.
SOLUS.ai was a first mover, and is currently a market leader in the AI-First category. Notably, SOLUS has been able to implement the aspirational goal of Segment of One – acting as a system of intelligence that sits between users’ existing data sources and their existing engagement channels, generating individual customer-level nudges that combine recommendations, propensity scores, and stacked models, rather than broad segments or static rules.
SOLUS is designed to empower brands to drive more revenue from their existing customer base, operating as a nerve center between systems of that generate data and systems that communicate with customers – using data to enable far smarter and far more targeted customer nudges, with individual-customer level precision, even on a database of tens of millions.
Notice how this approach leaves existing systems in place, working exactly as before, albeit smarter.
Firstly, SOLUS does not capture or generate data about any customer. It simply ingests data from existing systems, such as a data warehouse. Or it ingests data from multiple systems, such as POS transaction history, plus customer demographics, plus product catalogs, plus loyalty program data, and digital engagement data (Delivered, Opened, Clicked, Read etc.), or any other relevant data, leaving those systems in place, as before. However, if desired, SOLUS can also push data back to a data warehouse or other system, in order to enhance that data with intelligent custom variables.
Also, SOLUS does not send communication directly to customers. Instead, it integrates with your existing Martech platforms that already do that, such as Clevertap, Moengage, Adobe or Insider, or with stand-alone channel partners like Gupshup, Route Mobile, SendGrid, and so forth, leaving all those systems in place as well (albeit MUCH smarter than before).
How much smarter?
Under the hood, SOLUS runs more than 10 recommendation systems in parallel, with a meta-learner that makes the final recommendations selected from those competing models. That’s a lot smarter than legacy systems can be.
Also, there’s a flexible journey builder that enables multi-channel campaign orchestration, supporting always-on lifecycle campaigns that dynamically optimize themselves at the n=1 level using contextual multi-armed bandits (CMABs).
Aside from this, SOLUS is able to achieve remarkably fast speed-to-value, enabled in part by an array of out-of-the-box propensity models, including 120 always-on campaigns and fully-automated control groups, plus same-day turnaround on new models.
So, what kind of impact can you expect from this type of n=1 relevance?
Brands typically see a 3-7% topline increase from incremental sales, when transitioning to recommender-led campaigns. This delivers 4-5x higher ROI, compared to segment-led campaigns. And conversion rates improve by 20-30% with n=1 campaigns. Also, KPIs like repeat, retention, and win-back improve by 5-10% year-over-year.
If you’d like to see a demo or know more about SOLUS, feel free to get in touch. AI Master Group is a B2B channel partner for SOLUS. Full implementation takes one week.
Jim Griffin is a faculty member at the University of Texas, Austin, in the Masters of Business Analytics program. He’s also the founder of AI Master Group, which delivers high-impact consulting and resources related to AI. Jim has more than 15 years of project experience in North America, Europe, the Caribbean and Asia Pacific, with projects involving AI, analytics, machine learning and CRM. He also has a popular YouTube channel and podcast devoted to AI.
Jim can be reached at jim@aimast.org
