AI is evolving at a tremendous rate, especially in its applications for ecommerce marketing. Only a few years ago, enhancing personalization with AI was considered cutting-edge. Today, with the rate of innovation we’re now seeing, leveraging AI in ecommerce marketing has become an expectation.
However, while 80% of CMOs are planning to increase their investments in AI in 2024, not all marketing organizations are set up to harness AI to its full potential.
One of the biggest hindrances to utilizing AI successfully is internal disorganization. When marketing organizations have fragmented data sets, disconnected AI workflows, and varying levels of AI adoption across functional teams, it can be difficult to develop a holistic AI strategy and translate insights into business value.
When marketing is able to align internally and unify their AI resources, however, they can leverage AI more effectively while also improving data security and operational efficiency.
In this post, we’ll explore the reasoning for centralizing AI strategy within a unified platform and share guidance for how organizations can get more out of their AI investments.
The Explosion of the Modern Martech Stack
If you’ve worked in marketing for any part of the last decade, you’re likely at least aware of the big yearly martech landscape map, which compiles all the various vendor solutions available. Equally impressive to the size of the map is the rate of growth since the map’s inception. The 2024 map contains 14,106 vendors, while the inaugural 2011 map contained just 150 (a compound annual growth rate of 41.8% over 13 years!). And, as the options on the martech buffet have grown, so have the number of tools marketing organizations have adopted, with the average enterprise now using as many as 90 different tools.
But while the 2010s was marked by the increased adoption of martech solutions, the last two years have been marked by a different focus: the adoption of AI. Although every marketing organization is now looking to incorporate AI, the reality is that the expansive, disjointed martech stacks that many teams have developed over time are hindering their ability to leverage AI successfully.
The Problem With Disparate Solutions and AI
The foundation of accurate AI-powered predictions and analysis is high-quality training data — and lots of it.
The amount of data needed will vary based on your brand’s product catalog, your customers’ purchase seasonality, and other factors; but generally speaking, a larger, cleaner data set will allow machine learning models to generate more accurate outputs.
When your marketing technology stack is composed of numerous disconnected tools, each with its own data model and AI functionality, every tool will train its AI using its own, limited data set, leading not only to lower accuracy but also to varying outputs across your tech stack.
For example, your CRM may make predictions on customers’ product interests based on how they’ve interacted with your website, while your live chat tool makes predictions based on customers’ purchase history. As each system is training its AI model on a distinct data set, they may end up generating different outputs, leading you to recommend handbags to a customer in an email while recommending shoes to the same customer via live chat.
Developing your AI programs on a consolidated platform, however, enables you to generate more accurate predictions and activate those insights more effectively.
Maximize AI Impact With a Unified AI Strategy
To succeed in the age of AI, marketing organizations need to rethink their technology stacks, optimizing for AI performance. When AI is developed on a unified platform, key stakeholders across the organization are able to access the same, highly reliable AI outputs and leverage them across channels to deliver a seamless customer experience.
This has several benefits across the marketing organization:
Enhanced Efficiency
One of the great promises of AI is its ability to increase the efficiency of every stakeholder across the org. So the last thing that you want is for your AI workflows themselves to be inefficient.
A challenge of leveraging AI capabilities across numerous marketing tools is the amount of maintenance required to operate tools holistically. Different tools often have different data models, privacy restrictions, and functionality. When bi-directional integrations aren’t available, marketers need to step away from strategic initiatives to perform manual list transfers — or worse, wait for IT or dev teams to do it for them.
These delays are particularly detrimental to AI programs, as manual work can increase complexity, risk of errors, and time-to-market.
Centralizing your AI resources, however, simplifies management and reduces complexity. With all your AI capabilities consolidated, you can optimize workflows, eliminate redundancies, and improve overall efficiency in leveraging AI to drive your business forward.
Cost Effectiveness
While ecommerce CMOs are currently focused on empowering their teams with AI, they’re also mindful of costs. Gartner recently reported that marketing budgets have fallen by 15% on average in 2024 and that CMOs feel that they need to “do more with less.”
At first, it may seem that these initiatives — investing in AI and reducing costs — are mutually exclusive. Nearly every martech vendor in your stack is offering their own AI capabilities, many of which actually overlap with one another.
By developing your AI program on a unified platform, however, you can leverage best-in-class AI capabilities while also reducing costs — you can eliminate redundant licensing fees, streamline maintenance costs, and optimize resource utilization for a more cost-effective AI strategy.
High-Value Insights
At the end of the day, the most important factor in your AI strategy is being able to generate accurate, high-value insights.
As noted earlier, the foundation for a successful AI model is high-quality data, and lots of it. While various tools across your tech stack may offer AI insights of some kind, those insights are often generated based on distinct, limited data sets, resulting in a wide range of AI effectiveness.
By unifying your AI resources in a centralized platform, however, you’re able to generate insights based on all customer and product data, not just data from a particular channel. This makes it possible to access a more holistic view of your operations, customer interactions, and market trends so that you can make informed decisions. And with all key stakeholders empowered with the same AI analysis, teams will be able to engage in more advanced collaboration and translate AI insights into business value.
Unify Your AI Strategy With Bloomreach
As we transition into the age of AI-driven personalization, it’s important for ecommerce marketers to partner with a platform that enables them to harness the power of AI without headaches. Bloomreach’s Composable Personalization Cloud does just that.
Bloomreach empowers ecommerce marketers to engage customers with impactful personalization across the entire customer journey, whether that’s on the marketing side with channels like email and SMS, or on the site experience side with areas like search or product recommendations. Deeply woven through all of Bloomreach’s products is Loomi AI, Bloomreach’s proprietary AI.
While most martech solutions have only introduced AI functionality in the last couple of years, Bloomreach’s Loomi AI is trained on a high-quality customer and product dataset developed over the last 15 years. Loomi AI is also engineered specifically for ecommerce, meaning that in addition to supporting personalization, it’s also designed to support use cases around merchandising and product discovery that general-purpose AI is not built to solve.
With access to the same, high-quality insights across marketing automation, product discovery, and merchandising, your team will be equipped to leverage AI holistically and deliver a seamless ecommerce experience.
To learn more about unifying your AI strategy with Bloomreach Loomi AI, request your personalized demo today.
