What’s the Return on Investment Like for Recommendation Engines?

In the world of modern retail and e-commerce, recommendation engines have become nearly ubiquitous – from the “Customers also bought” suggestions on Amazon to the personalised show picks on Netflix. These systems use customer data and AI to suggest products or content that each individual user is likely to be interested in. But do these recommendation engines actually pay off for businesses? In other words, what is the return on investment (ROI) like for implementing a recommendation engine?

The short answer: Recommendation engines, when implemented well, tend to deliver a very strong ROI. They can drive significant increases in sales and customer engagement relative to their cost. Many companies report that recommendation systems essentially pay for themselves many times over, by boosting conversion rates, average order values, and customer lifetime value. For example, Amazon attributes an estimated 35% of its e-commerce sales to its recommendation algorithms – a massive chunk of revenue that underscores how valuable these tools can be. Netflix’s recommendation system is similarly impactful, influencing around 80% of the content viewed on the platform and reportedly saving Netflix about $1 billion per year by improving user retention. These headline figures hint at an impressive ROI, but to truly answer the question we need to dive deeper into how ROI is calculated and the specific ways recommendation engines add value.

In this article, we’ll explain what recommendation engines are, how to understand and calculate ROI in this context, and what real-world results and benchmarks tell us. We’ll also explore why recommendation engines boost key metrics like conversion rate and average order value, how to measure their impact, and best practices to maximise ROI. By the end, it should be clear why so many retailers and brands consider recommendation engines a must-have despite the investment involved.

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What Are Recommendation Engines and Why Do They Matter?

Recommendation engines (also called recommender systems) are software tools that provide tailored suggestions to users based on data. In a retail context, these are the algorithms behind features like “Recommended for you” product carousels, “Frequently bought together” add-on suggestions, or personalised content feeds. Rather than showing the same generic best-sellers to every visitor, a recommendation engine analyses each customer’s behavior, purchase history, and preferences (often using AI and machine learning) to present products that that specific customer is more likely to buy. It’s akin to having a personal shopper or sales assistant who knows your tastes – except it works automatically and at scale, across websites, apps, emails, and even in-store screens.

These personalised recommendations have exploded in popularity because they directly address a core challenge in today’s shopping environment: information overload. Customers are inundated with choices online. A good recommendation engine cuts through the noise by presenting relevant options, making the shopping experience easier and more engaging. This improved customer experience translates into tangible business value. Shoppers are more likely to discover products they want, to make purchases, and to return for more. In fact, research shows that customers often respond very positively to personalisation – the majority of consumers say they are more likely to shop with brands that offer relevant recommendations, and many will even share personal data in exchange for a more tailored experience. Simply put, recommendation engines matter because they simultaneously enhance customer satisfaction and drive incremental sales.

For businesses, the strategic importance is clear: done right, recommendations increase key performance metrics across the board. They turn more browsers into buyers (raising conversion rates), encourage each customer to spend more per transaction (raising basket size and average order value), and build loyalty so customers stick around longer (raising repeat purchase rates and lifetime value). All of these improvements contribute directly to higher revenue – which is why recommendation engines are so closely tied to ROI when we evaluate retail technologies. Next, let’s clarify what we mean by “return on investment” in this context.

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Understanding ROI for Recommendation Engines

Return on Investment (ROI) is a common financial metric used to evaluate how profitable an investment is relative to its cost. ROI is typically expressed as a percentage and calculated with a simple formula:

ROI = (Net profit from the investment ÷ Cost of the investment) ×100%



In the case of a recommendation engine project, the investment cost would include everything you spend to implement and run the system. This can range from software licensing or subscription fees (if using a third-party recommendation platform) to development costs (if building in-house), as well as integration expenses, infrastructure or cloud costs, and ongoing maintenance or staff time to manage the system. It’s important to tally both the initial setup cost and any recurring costs.

The return (benefit) side of the equation is essentially the additional net profit generated thanks to the recommendation engine. This usually comes in the form of incremental sales that would not have happened without those personalised recommendations. For example, if a customer was browsing your site and wasn’t planning to buy anything, but then a “You might also like” suggestion convinced them to add an item to cart, the revenue from that purchase can be attributed to the recommendation. The same goes for an upsell where a customer buys a higher-priced item that was recommended, or a cross-sell where they add an extra product. All these recommendation-influenced purchases contribute to the “return” generated by the system. We would also include the profit impact of improved customer retention (e.g. if fewer customers churn away because your experience is more engaging) as part of the return.

To calculate ROI, you compare the net profit from these incremental sales against the cost of the recommendation engine. For instance, imagine you spend $10,000 on a recommendation engine in a year (licensing, implementation, etc.), and you determine that the personalised recommendations generated an extra $50,000 in net profit (after cost of goods, etc.) from additional sales. Then the ROI would be:

• Investment cost = $10,000
• Net profit from rec engine = $50,000
• ROI = (50,000 ÷ 10,000) × 100% = 500%

In this hypothetical scenario, a 500% ROI means you earned back your initial investment five times over. For every $1 spent on the recommendation tool, you got $5 back in profit. This is obviously a fantastic return. In reality, ROI calculations may not be quite so straightforward – it can be tricky to precisely isolate which sales were caused by recommendations. Often businesses will run controlled experiments (like A/B tests) to measure the “uplift” from recommendations, or use analytics to attribute sales to recommendation clicks. The key idea, however, is to figure out how much extra revenue (and profit) the recommendations are driving, and compare that to what you spend.

The good news is that a well-implemented recommendation engine tends to produce a substantial uplift in sales relative to its cost. Industry benchmarks and case studies regularly show ROI figures comfortably above 100%, meaning the systems more than pay for themselves. In fact, in advanced personalisation programs (which include recommendation engines as a central component), companies have reported returns as high as $20 for every $1 spent – that’s a 2000% ROI. Those are top-tier results, but even average programs see strong ROI; one study found the average ROI increase from personalisation initiatives is in the range of 5x to 8x (500–800%). Clearly, the potential returns are significant.

Why are the returns so high? The simple answer is that recommendation engines directly drive revenue by improving conversion, basket size, and retention while their costs are relatively modest in comparison (especially if using SaaS solutions or existing platforms). Let’s break down the specific ways that recommendation engines boost the metrics that matter for ROI.

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How Recommendation Engines Drive ROI

To understand the ROI of recommendation engines, it helps to see how they influence customer behaviour in ways that benefit the bottom line. There are several key mechanisms through which a recommendation engine adds value:

1. Increasing Conversion Rates and Sales

One of the most immediate impacts of personalised recommendations is a higher conversion rate – turning more visitors into paying customers. When shoppers are presented with products that genuinely match their interests or needs, they’re simply more likely to make a purchase. Think of a customer browsing an online store; if the site surfaces a few highly relevant items that catch the customer’s eye (based on their browsing history or similarities to products they’ve liked), that customer is much less likely to leave empty-handed. The recommendations can nudge an indecisive browser to click “Add to Cart.”

There’s data to back this up: analyses of e-commerce behavior show dramatic jumps in conversion when recommendations are involved. For example, according to Salesforce, shoppers who clicked on a recommended product were 4.5 times more likely to complete a purchase during that session compared to those who didn’t engage with any recommendations. In another study, users who interacted with product recommendations had a 70% higher conversion rate in that visit than those who saw recommendations but ignored them. Even a single recommendation click can significantly increase the chances of a sale, and multiple recommendation interactions further raise the probability. This is powerful evidence that recommendation engines create additional sales that might not have happened otherwise.

Even if you don’t track the granular per-session stats, many retailers observe an overall lift in their site’s conversion rate after implementing a recommendation system. For instance, if your conversion rate site-wide rises from say 2.0% to 2.5% after adding personalised recommendations, that half-point increase is directly contributing extra revenue from the same traffic. Over time, that can translate to thousands or millions in new sales. The bottom line is that recommendations help capture sales that would have been missed, which boosts total revenue and therefore ROI.

2. Larger Basket Sizes and Higher Average Order Value

Recommendation engines don’t just help make the first sale – they also encourage customers to spend more per transaction. This comes from effective cross-selling and upselling. By suggesting complementary products or premium alternatives at the right moment, a recommendation engine can increase the items or value in a customer’s cart.

Common examples include the classic “Frequently Bought Together” suggestions (which might prompt you to add the matching case for the laptop you’re buying, or the belt that goes with the dress in your cart) and the “You might also like” carousel that introduces you to other products you weren’t actively looking for but find appealing. There’s a reason brick-and-mortar stores put the impulse buys near the checkout; a recommendation engine is doing a similar thing, but in a highly targeted way for each shopper.

The impact on Average Order Value (AOV) and basket size can be substantial. One retailer reported that after deploying an AI-driven recommendation engine, customers started adding more items to their orders – resulting in a 21% increase in average order value and a 31% increase in the number of items per basket. Essentially, people bought more because relevant extras were presented to them. In general, around half of consumers say they have ended up purchasing an additional product they weren’t originally planning to buy because a recommendation suggested it. This is incremental revenue directly generated by the recommender.

From an ROI perspective, higher AOV means each converted customer is now yielding more revenue, which boosts the return without requiring additional traffic. If you can raise the average spend per customer through smart recommendations, the payoff can far outweigh the cost of running the recommendation tool. It’s like having a super-effective salesperson who gently upsells customers to add more to their cart in a helpful way – and that naturally improves the revenue outcome of each sale.

3. Improving Customer Retention and Lifetime Value

Not all benefits of recommendation engines are one-off or immediate. A major part of their ROI comes from enhancing customer loyalty and lifetime value (CLV) over the long term. When customers consistently receive relevant, personalised suggestions, it creates a better overall shopping experience that keeps them coming back. They feel that the brand understands their needs and tastes, which fosters loyalty.

Imagine a first-time visitor to your website who gets a great personalised recommendation and makes a purchase. If that experience delights them, they’re more likely to return later. In fact, studies have shown that shoppers who engage with recommendations on their first visit are nearly twice as likely to return to the site in the future compared to those who didn’t get relevant recommendations. Over time, those returning customers make repeat purchases, increasing their lifetime value to the business.

Additionally, personalised recommendations can reduce churn (for subscription services or apps) by keeping users more engaged. Netflix is a famous example here: its recommendation engine suggests content so well that users always find something interesting to watch, which keeps them subscribed. Netflix has stated that its recommendation and personalization efforts help avoid customer churn to such an extent that it saves an estimated $1 billion per year in retained revenue. While a retailer might not calculate “saved revenue” in the same way, the concept is similar – engaging customers with personalisation means they stick with your brand and continue shopping with you, rather than drifting to a competitor.

Higher customer retention and satisfaction also lead to positive word-of-mouth and brand preference, which indirectly bring in more business at lower marketing cost. When considering ROI, these loyalty effects mean that the returns from recommendation engines aren’t just one-time transaction boosts; they compound over the customer’s lifecycle. Loyal customers have higher CLV, and much of that value can be attributed to the personalised experience powered by recommendations.

4. Direct Revenue Contribution and Sales Attribution

Another way to look at ROI is to measure the share of revenue that comes from recommendation-fueled purchases. Many analytics tools can attribute sales to users clicking on recommended items. Retailers who track this often find that a surprisingly large portion of their online sales is driven by recommendations. Industry benchmarks indicate that a well-implemented recommendation engine can be responsible for anywhere from 10% to 30% of total e-commerce revenue. That means up to nearly a third of your online sales might be coming from customers who found products via the recommendation widgets or sections.

Even more striking, these sales can punch above their weight relative to site interactions. For example, one analysis (Salesforce Research) found that though recommendation sections accounted for only about 7% of clicks on an e-commerce site, those clicks were generating 26% of the revenue. This outsized impact shows that people who do engage with recommendations tend to buy, and buy more than the average visitor. So if over a quarter of your revenue is linked to the recommendation engine, it’s easy to justify the investment – losing that tool would likely mean losing that chunk of sales.

From an ROI standpoint, tracking “revenue attributed to recommendations” is a direct way to gauge the system’s value. If you see that, say, 15% of this month’s sales came from recommendation-influenced purchases, you can then compare that to how much you’re spending on the recommendation technology. In many cases, this comparison comes out heavily in favor of the recommendations. As long as you treat those attributed sales as incremental (i.e. sales that probably wouldn’t have happened otherwise), then that revenue is essentially the return on your investment in personalisation.

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Measuring the Impact and Calculating ROI

To rigorously determine the ROI of a recommendation engine, businesses should take a data-driven approach. Here are some methods and metrics commonly used to evaluate performance and financial return:

• Key Metrics to Monitor: We’ve already mentioned several – conversion rate, average order value, basket size, repeat purchase rate, and percentage of revenue from recommendations. You’ll want to monitor the difference in these metrics with vs. without recommendations. Many retailers create segments in their analytics: compare sessions or users who engaged with recommendations to those who did not. If you consistently see higher conversion or bigger orders among the former, that’s evidence of the recommender’s impact. Also track overall trends in site-wide metrics post-implementation.
• A/B Testing and Lift Analysis: The gold standard for measuring incremental lift is to run an A/B test or controlled experiment. For example, you could show personalised recommendations to a random 90% of your visitors, and hide the recommendation widgets for 10% (the control group). If you observe that the test group’s conversion rate is, say, 3% while the control group’s is 2%, and similarly the test group’s average order value is higher, you can attribute that difference to the recommendations. This allows you to calculate the extra revenue per visitor generated. Multiply by your traffic and you have an estimate of monthly revenue lift due to the engine, which feeds into ROI calculation. Some companies also do sequential testing (turn the engine off for a week, then on, etc.) to see the sales delta.
• Attribution Considerations: It’s important to attribute sales to recommendations thoughtfully. Not every click yields an immediate purchase; sometimes a shopper clicks a recommendation, browses, and buys the item a few days later. Try to capture those delayed conversions by looking at a window of time after recommendation engagement. Also, consider cross-channel effects – a customer might discover a product via an online recommendation but later buy it in the physical store, or vice versa. If you have an omnichannel setup (like linking online and offline profiles through loyalty accounts or a mobile app), include those cases in your analysis. Effective omnichannel retailers extend their recommendation engines into store experiences as well (for example, equipping sales associates or mobile POS systems with personalised suggestions based on the customer’s online browsing). This unified approach can further increase overall sales influenced by recommendations, though it makes attribution more complex. The more complete your attribution of recommendation-influenced sales, the more accurate your ROI figure will be.
• Calculating Net Profit: Remember that ROI should be based on net profit, not just revenue. So after determining the uplift in revenue from recommendations, subtract the cost of goods for those sales to get profit, then compare against the cost of the system. In many retail cases, however, gross margin on additional sales is fairly high (since you’re often selling more to existing traffic, not spending extra on ads to acquire a new customer), so the profit margin on recommendation-driven sales can be attractive.

By measuring all these factors, you can come up with a solid estimate of ROI. For instance, you might conclude: “Our recommendation engine costs $5,000 per month, and through A/B testing we’ve found it generates an extra $20,000 per month in gross profit from increased conversions and larger baskets. That’s a 4x return or 300% ROI monthly, which annualises to roughly $180,000 additional profit per year on a $60,000 yearly cost – a very positive return.” This kind of analysis gives executives confidence that the initiative is financially worthwhile.

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Real-World ROI Examples and Benchmarks

Looking at industry examples provides further evidence of the ROI potential of recommendation engines:

• Amazon: The e-commerce giant pioneered the use of recommendation algorithms (“Customers who bought this also bought…”) and has deeply integrated them across its site. While Amazon doesn’t publicly break out numbers, consulting firm McKinsey estimated that 35% of Amazon’s total sales are generated by its recommendation engine. That is enormous – more than a third of sales attributable to recommendations. Given Amazon’s revenue scale, this indicates tens of billions of dollars driven by recommender systems. It’s no wonder Amazon continually refines its algorithms; the ROI is clearly tremendous when a single system influences such a large share of revenue.
• Netflix: As mentioned earlier, Netflix’s content recommendations are core to its user experience. Around 75–80% of what people watch on Netflix comes from algorithmic recommendations (like “Because you watched X”). This personalised content discovery keeps users engaged and subscribing. Netflix has noted that its recommendations reduce churn significantly – they’ve cited a figure of about $1 billion in annual value gained from improved retention (fewer customers leaving) thanks to their recommendation and personalization efforts. That figure can be interpreted as ROI: Netflix’s investment in recommendation technology (which includes algorithms, data infrastructure, etc.) yields a return measured in hundreds of millions of dollars, easily dwarfing the costs. The success at Netflix exemplifies how powerful recommendations can be in driving user value and thus revenue for subscription models.
• Smaller Retailer Case – Natural Baby Shower: To show this isn’t only for tech giants, consider Natural Baby Shower, a boutique baby products e-commerce retailer. After implementing a personalised product recommendation engine on their website, they reportedly saw a 21% increase in average order value and 31% increase in basket size. This translated to a significant jump in monthly sales. For a company of that size, a double-digit percentage lift in AOV can mean a huge improvement in profitability. The ROI for them was very clear: the extra revenue from bigger baskets far outweighed the cost of the recommendation solution.
• Aggregate E-Commerce Stats: Across the board, retailers see a solid chunk of revenue coming from rec engines. We noted earlier that typically 10–30% of online revenue is tied to recommended product clicks. Also, personalised product recommendations account for about 24–26% of orders for many online stores even if those recommendation widgets only get a small fraction of pageviews. One particularly striking statistic: companies that fully embrace advanced personalisation (which includes using sophisticated recommendation engines) have reported getting $20 back for every $1 spent on these technologies. Not every company will hit that 20:1 ratio, but it demonstrates that triple-digit ROI percentages are achievable. Even a more modest result, say $5 back per $1 spent (a 500% ROI), would be considered a very successful investment in most contexts.

These examples underscore a pattern: when recommendation engines are deployed effectively, they can become major revenue drivers. The ROI ranges from very good to astounding, depending on the company and how optimised their system is. Of course, simply having a recommendation engine doesn’t guarantee such results – it has to be well-executed. That brings us to the final point: how to maximise the ROI of your recommendation engine initiative.

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Best Practices to Maximise Recommendation Engine ROI

To ensure you get the highest return on investment from a recommendation engine, keep the following best practices in mind. These strategies help align the technology with business goals and optimise its performance continuously:

1. Focus on Relevance: The effectiveness of recommendations boils down to relevance. Use as much data as possible about your customers (their browsing history, past purchases, demographic info, etc.) to inform the algorithms. The more accurately the engine can predict what a shopper actually wants, the more likely those suggestions will lead to a sale. In practice, that means leveraging AI or machine learning models that can crunch lots of data points to personalise results. Always ask, “Is this recommendation genuinely useful to the customer?” Highly relevant, personalised suggestions drive higher engagement and, in turn, higher sales.
2. Strategic Placement: Where and when you present recommendations matters a lot. Insert personalised suggestions at key touchpoints in the customer journey. For example, on product pages show “Customers also viewed” or complementary item suggestions (to encourage adding related items). On the cart or checkout page, suggest an accessory or an upgrade (“You might want to add...”). On the homepage or in marketing emails, highlight new arrivals or popular items tailored to the user. Also consider the visual layout – recommendations that appear prominently (e.g. near the top of a page, or in a noticeable sidebar) will get more attention. The goal is to integrate recommendations seamlessly into the shopping experience so that they actively assist decision-making without feeling intrusive.
3. Test and Learn: Optimising a recommendation engine is an ongoing process. Continuously experiment with different algorithms, recommendation strategies, or UI presentations to see what resonates best with customers. You might test variations like the number of recommendations shown at once, the wording of call-to-action headers (“Recommended for you” vs “You may also like”), or even different logic (such as collaborative filtering vs. trending items for first-time visitors). Use A/B testing to gauge which changes improve click-through and conversion. Over time, this iterative testing will refine your recommendation approach and improve its performance – which boosts ROI. The key is to not take a “set it and forget it” mindset; instead, actively manage and tune the system based on data and customer feedback.
4. Integrate Across Channels: For omnichannel retailers, extending personalisation beyond the website can multiply the benefits. Ensure your recommendation engine’s insights are utilised in all channels – this could include email campaigns (e.g. personalised product recommendation emails), mobile apps, SMS offers, and even in-store experiences. Consistency is important: if a customer gets great recommendations on the website, mirror those when they log into the app, and have store associates armed with that knowledge when the customer is in a physical shop. A unified approach means customers receive relevant suggestions at every touchpoint. This not only drives more sales (by capturing opportunities both online and offline) but also provides a smooth, personalised experience that strengthens loyalty.
5. Monitor and Refine: Keep a close eye on how customers interact with recommendations and adjust accordingly. Look at analytics for signs of what’s working or not. For instance, if certain recommended products have a high click rate but low conversion, perhaps the product is enticing but not convincing to buy – you might need to tweak pricing or the recommendation logic. If some recommendation widgets are being ignored (low engagement), try moving them or replacing their content. Also pay attention to qualitative feedback: if customers start complaining about seeing the same suggestions repeatedly, or irrelevant items, it can erode trust. You want shoppers to feel the recommendations are genuinely helpful. Maintaining that trust may involve filtering out items that customers have already bought or are unlikely to want (to avoid “bad” recommendations). By continually refining the recommendation rules and content based on performance data and feedback, you keep the system effective. This adaptive approach ensures that the uplift in conversion/AOV doesn’t stagnate or decline over time – preserving your ROI gains.

By following these practices, companies often create a virtuous cycle: better recommendations lead to more engagement and sales, which then justifies further investment in personalisation and data, which then enables even more refined and effective recommendations. Over time, this compounds the ROI of your recommendation engine initiative. It also raises the bar for competitors – retailers who excel at personalisation gain a long-term competitive advantage in customer experience that can be hard to match.

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Conclusion

So, what’s the ROI like for recommendation engines? In summary, it’s typically very high. Recommendation engines have proven to drive significant improvements in crucial metrics like conversion rate, average order value, and customer retention, all of which translate into increased revenue. When you quantify the impact, it’s common to find that the additional profit generated by a recommendation engine far exceeds its cost – often yielding ROI in the hundreds of percent, if not more. Businesses from e-commerce giants to niche retailers have reported that personalised recommendations boost sales and profits substantially, making the investment well worthwhile.

However, maximising ROI isn’t automatic; it requires thoughtful implementation and ongoing optimisation. Companies that see the best returns treat their recommendation engine as a strategic, dynamic part of the customer experience – constantly feeding it rich data, testing and tuning the outputs, and expanding it across channels. In doing so, they ensure that their recommendations remain relevant and effective, continuing to drive incremental sales.

In the fast-evolving retail tech landscape, delivering personalised experiences is no longer just a nice-to-have – it’s increasingly a baseline expectation from customers. Those who leverage recommendation engines effectively not only enjoy immediate sales lifts, but also build deeper customer relationships that pay dividends over the long term (through loyalty and lifetime value). All of these factors contribute to ROI, both short-term and long-term.

In essence, a good recommendation engine can be seen as an investment that yields ongoing returns: it’s like hiring a super-intelligent sales assistant who works 24/7, gets smarter with time, and consistently boosts your revenue. And as the examples and data show, the return on this investment can be game-changing for many businesses.

In conclusion, the ROI for recommendation engines tends to be strongly positive – often dramatically so – provided that the system is implemented well and aligned with a customer-centric strategy. For retailers and brands looking to increase sales and stay competitive, investing in personalised recommendation technology is very often a wise decision that pays back its cost many times over.

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Key Stats and Takeaways

• 10–30% of revenue: Retailers commonly report that between 10% and 30% of their e-commerce revenue is directly attributable to purchases driven by product recommendations. This indicates a large chunk of sales comes from the recommendation engine’s influence.
• 7% vs 26%: In one analysis, product recommendations accounted for only ~7% of clicks on a retail site but drove 26% of the site’s revenue – demonstrating the outsized impact of those recommendation interactions on sales.
• 4.5× higher conversion: Shoppers who click on a recommended product are much more likely to buy. For example, one study found they were 4.5 times more likely to make a purchase during that visit than those who didn’t engage with any recommendations.
• +21% AOV, +31% items: One retailer saw a 21% increase in average order value and a 31% increase in number of items per order after introducing personalised recommendations, as customers added more to their carts due to the suggested items.
• 2000% ROI (20:1 return): Companies using advanced personalisation (including recommendation engines) have reported extremely high returns – up to $20 in revenue for every $1 spent. This 2000% ROI exemplifies the potential payoff of effective recommendation strategies.
• 49% of consumers: Nearly half of consumers say they have purchased a product they initially did not intend to buy because a recommendation suggested it. This highlights how recommendations generate incremental sales that otherwise wouldn’t occur.
• 35% Amazon Sales: Approximately 35% of Amazon’s e-commerce sales are estimated to come from its recommendation engine. This underscores how pivotal recommendations are to even the world’s largest retailer’s revenue.
• $1B Saved at Netflix: Netflix’s recommendation algorithms drive ~80% of content watched and help save an estimated $1 billion per year by reducing customer churn, illustrating the huge financial impact of a well-tuned recommender system on customer retention.

These statistics collectively illustrate the strong ROI that recommendation engines can deliver – boosting conversion rates, increasing order sizes, and contributing a significant share of revenue, all of which lead to returns that far outweigh the costs. For businesses evaluating the investment, the data makes a compelling case that a smart recommendation engine, paired with good execution, is a revenue-generating asset with a high return on investment.

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