AI Recommendations: Brand Influence in 2026

Working through the field of AI recommendations presents a unique challenge for brands, as these systems increasingly mediate customer interactions and purchasing decisions. Understanding how AI recommendations operate and, critically, how to influence their output is no longer optional for maintaining market relevance. Brands must actively engage with these platforms to ensure their offerings are presented effectively and authentically. The question becomes, how do you actively shape AI recommendations to reflect your brand’s strategic goals and customer understanding?

Step 1: Accessing and Configuring Your Recommendation Engine

The initial phase involves locating and setting up the AI recommendation engine within your chosen marketing automation platform. This process varies slightly by provider but generally follows a similar logic.

1.1 Locating the Recommendation Engine Module

Most enterprise-level marketing platforms, such as Salesforce Marketing Cloud or Adobe Experience Platform, centralize their AI capabilities.

In Salesforce Marketing Cloud, navigate to “Journey Builder” from the main dashboard. Within Journey Builder, look for the “AI & Analytics” tab on the left-hand navigation pane. Click on “Recommendation Studio.” This module is where all recommendation logic resides. For new users, an initial setup wizard will guide you through connecting data sources.

For users of Adobe Experience Platform, the path is slightly different. From the main console, select “Journeys” and then “Offer Decisioning.” This area manages personalized content and product recommendations. You will find “Recommendation Models” listed under “Components.”

1.2 Selecting a Recommendation Model

Once inside the recommendation module, you will be prompted to select a model type. This is a critical decision that dictates the underlying logic of the AI recommendations.

Common options include:

• Collaborative Filtering: Recommends items based on user similarity. For example, “customers who bought X also bought Y.” This is effective for broad product catalogs where user behavior patterns are strong.
• Content-Based Filtering: Recommends items similar to those a user has previously liked or interacted with. This works well when product attributes are rich and diverse, allowing for fine-grained matching.
• Hybrid Models: Combine elements of both collaborative and content-based filtering. These often yield the most accurate results by mitigating the cold-start problem (where new users or products lack sufficient data for pure collaborative filtering).
• Sequence-Aware Models: These models consider the order of past interactions, useful for predicting the next logical step in a customer journey, such as recommending accessories after a primary product purchase.

Pro Tip: Do not simply choose the default model. Evaluate your product catalog size, the depth of your customer interaction data, and your primary recommendation objective (e.g., cross-selling, up-selling, discovery). If you have a relatively new product line with limited historical data, a content-based approach might perform better initially. Conversely, a mature product with extensive customer purchase history benefits greatly from collaborative filtering. I have seen brands default to collaborative filtering only to find their niche products remain invisible simply because they lacked the initial interaction volume. This is a common pitfall.

1.3 Initial Data Source Connection

After selecting your model, connect the necessary data sources. This typically involves product catalogs, customer profiles, and interaction logs (purchase history, browsing behavior).

In Recommendation Studio (Salesforce), navigate to “Data Sources” and click “Add New Data Source.” You will usually upload a CSV file or connect directly to your product information management (PIM) system via an API. Ensure your product data includes attributes like category, price, brand, description, and relevant keywords. Missing or inconsistent data here will cripple your AI’s ability to make relevant suggestions. A common mistake is uploading product descriptions that are too short or lack descriptive keywords, which limits the effectiveness of content-based models.

Step 2: Defining Brand Influence through Product Attributes and Segments

This step is where brands exert direct control over the types of recommendations generated. It’s not about overriding the AI, but rather guiding its learning process with intentional parameters.

2.1 Enriching Product Data

The quality of your product data directly impacts the AI’s ability to understand and recommend items aligned with your brand values.

Within your platform’s “Data Feeds” section (e.g., Salesforce’s “Product Catalog” or Adobe’s “Offer Library”), ensure that every product entry is comprehensively tagged. This includes:

• Brand-specific keywords: Beyond generic product names, include terms that reflect your brand’s ethos. For a sustainable fashion brand, this might mean tags like “organic cotton,” “recycled materials,” or “ethically sourced.”
• Sentiment tags: If your brand emphasizes a particular emotional connection (e.g., “comfort,” “luxury,” “adventure”), integrate these as custom attributes.
• Lifecycle stage: Tag products as “new arrival,” “bestseller,” “clearance,” or “seasonal,” allowing the AI to prioritize based on inventory or strategic promotions.

Example: I worked with a specialty coffee brand in Atlanta that struggled with AI recommendations suggesting generic coffee makers when their focus was single-origin, artisanal beans. By adding custom attributes like “roast profile: light/medium/dark,” “flavor notes: citrus/chocolate/floral,” and “region: Ethiopian Yirgacheffe,” their recommendations became significantly more targeted, driving a 12% increase in average order value for those specific beans. Without these detailed attributes, the AI simply categorized all coffee as “coffee,” which was insufficient for their brand’s nuanced offering.

2.2 Segmenting Customer Audiences

Tailoring recommendations to specific customer segments is a powerful way to ensure relevance and reinforce brand messaging.

In your platform’s “Audience Builder” or “Segmentation” module, create granular customer segments. These segments can be based on:

• Demographics: Age, location (e.g., customers in the Buckhead area of Atlanta versus Midtown).
• Psychographics: Interests, values, lifestyle (e.g., “eco-conscious shoppers,” “tech enthusiasts”).
• Behavioral data: Past purchases, browsing history, engagement with specific content (e.g., “repeat purchasers of luxury goods,” “cart abandoners”).

Once segments are defined, link them to specific recommendation strategies. For instance, you might configure the AI to prioritize “new arrivals” for your “early adopter” segment, while “complementary products” are recommended to your “loyal customer” segment. This targeted approach ensures that the AI’s suggestions resonate with the individual customer’s profile and your brand’s communication strategy for that segment.

Step 3: A/B Testing and Performance Monitoring

Deployment is only the beginning. Continuous testing and monitoring are essential for refining AI recommendations and ensuring they align with brand objectives.

3.1 Setting Up A/B Tests

Most platforms offer built-in A/B testing capabilities for recommendations.

Navigate to “Experiments” or “A/B Testing” within your recommendation engine module. Create at least two variations:

• Control Group: This group receives either no recommendations or a baseline, generic recommendation.
• Variation A: Receives recommendations generated by your primary AI model (e.g., collaborative filtering with enriched product data).
• Variation B: Receives recommendations from an alternative model or a model with different configuration parameters (e.g., content-based filtering, or a collaborative model prioritizing higher-margin products).

Define your success metrics clearly, such as “click-through rate on recommended items,” “conversion rate,” or “average order value.” Run these tests for a statistically significant period, typically two to four weeks, to gather sufficient data. I generally advise clients to aim for at least 1,000 conversions per variation before drawing firm conclusions. Anything less can lead to misleading results.

3.2 Monitoring Key Performance Indicators (KPIs)

Regularly review the performance of your AI recommendations in the “Performance Dashboard” section of your platform.

Key metrics to track include:

• Click-Through Rate (CTR): The percentage of users who click on a recommended item.
• Conversion Rate: The percentage of users who make a purchase after clicking on a recommendation.
• Average Order Value (AOV): The average value of orders that include a recommended item.
• Recommendation Coverage: The percentage of your product catalog being recommended. If only a small fraction of your products are ever recommended, you might have an issue with data sparsity or model bias.
• Recommendation Diversity: How varied the recommendations are for a user over time. Overly repetitive recommendations can lead to “recommendation fatigue.”

Editorial Aside: One of the biggest oversights I observe is brands setting up recommendations and then rarely checking their performance. AI models can “drift” over time, meaning their accuracy degrades as customer preferences evolve or product inventory changes. Without consistent monitoring, you could be serving irrelevant recommendations for months, actively harming the customer experience.

3.3 Implementing a Feedback Loop

To continually improve AI recommendations, establish a feedback loop that integrates customer interactions back into the model.

This can involve:

• Implicit Feedback: The AI automatically learns from clicks, purchases, and browsing behavior.
• Explicit Feedback: Implement “Rate this recommendation” or “Was this recommendation helpful?” features on your site. Integrate this survey data into your AI model’s retraining process. For instance, in Adobe Experience Platform, you can configure schema extensions to capture explicit feedback, which then informs the “Offer Decisioning” engine.

Schedule regular retraining of your AI models, typically monthly or quarterly, using the most recent data. This ensures the recommendations remain fresh and relevant. According to a 2023 eMarketer report, retailers who actively personalize customer experiences, including recommendations, see conversion rates up to 2.5 times higher than those who do not. This shows the financial imperative of a strong feedback loop.

By carefully configuring your recommendation engine, enriching product data with brand-specific attributes, segmenting audiences, and maintaining a rigorous testing and monitoring schedule, brands can ensure their AI recommendations are not just generic suggestions but powerful extensions of their brand identity. Monitoring the AI metrics is key to understanding the return on investment. This proactive approach is important for e-commerce AEO in 2026 and beyond.