Digital marketing trends food and beverage brands cannot ignore in 2026
The specific 2026 digital marketing trends most relevant to food and beverage brands, prioritised for visual discovery and retail media coordination.
5 min read
Product recommendation engines — 'customers also bought', 'you might also like' — have quietly become one of the highest-revenue-driving features on many e-commerce sites, often responsible for a substantial share of total revenue without receiving proportional strategic attention.
Why recommendation engines drive so much revenue
A well-tuned recommendation engine surfaces genuinely relevant products at exactly the moment a customer is already in a buying mindset — a fundamentally different, higher-converting context than a cold acquisition ad reaching someone earlier in their decision process.
- Recommendations reach customers already in an active buying session, not cold prospects.
- Genuine relevance, driven by real behavioural data, converts better than generic cross-selling.
- Recommendations can surface products a customer would not have found through search alone.
A recommendation engine quietly influencing a large share of total revenue, without ever appearing in a marketing budget conversation, is one of the more underappreciated assets on many e-commerce sites.
Practical implementation priorities
- Ensure the underlying data (browsing history, purchase history, product attributes) is comprehensive and current.
- Test recommendation placement across the customer journey — product page, cart, post-purchase — not just one location.
- Measure attributed revenue directly, not just click-through rate on recommendation modules.
Where recommendation engines commonly underperform
Thin product catalogues, insufficient behavioural data volume, or generic 'best-selling' fallback logic (rather than genuinely personalised recommendations) all limit a recommendation engine's real potential, regardless of how sophisticated the underlying algorithm is.
Frequently asked questions
Do small e-commerce stores benefit from recommendation engines as much as large ones?
The relative benefit can be smaller with a limited product catalogue and less behavioural data, but even a modest, well-tuned implementation typically improves average order value meaningfully.
How is recommendation engine performance best measured?
Attributed revenue and average order value lift, measured through proper analytics tracking, rather than just engagement metrics like click-through rate on the recommendation module alone.
Should recommendations be based purely on purchase history, or also browsing behaviour?
Combining both typically produces better results than relying on purchase history alone, since browsing reveals current interest that may not yet have converted to a purchase.
If you want help optimising your product recommendation strategy, talk to our team.
The takeaway
Audit your recommendation engine's actual attributed revenue share this quarter — many businesses underestimate just how much of their total revenue this quiet, background feature is actually driving.
Written by Fibvo — Bengaluru and Erode, India — working with clients in other time zones. Questions, or think we have it wrong? Tell us.
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