How to Measure ROI of Product Recommendation on Your Shopify Store

Product recommendations are essential for customers to discover new products. They can increase sales and build strong customer relationships by showing personalized service. Modern product recommendations use machine learning and AI-powered technologies to stay competitive.

This shift to AI systems is becoming more and more accessible to businesses thanks to advancements in algorithms and infrastructure systems. Retailers don't need expensive processors or machine learning experts anymore. They can get intelligent recommendations from third-party providers at a low cost.

Still, merchants should be aware of the performance of the recommender system through ROI so they can have a clearer picture of what they are doing and how they can optimize the product recommendation for more sales.

What Is ROI?

Return on Investment (ROI) is a financial metric used to evaluate the profitability of an investment. It measures the return or profit generated from an investment relative to its cost. ROI is typically expressed as a percentage and is calculated by dividing the net profit of the investment by the initial cost of the investment and then multiplying the result by 100.

The formula for calculating ROI is:

ROI = (Net Profit / Cost of Investment) * 100

A higher ROI indicates a more profitable investment, while a lower ROI suggests a less profitable one. ROI is commonly used by businesses and investors to assess the performance and potential profitability of various investments, such as stocks, real estate, marketing campaigns, or business projects. It helps in comparing different investment opportunities and making informed decisions about resource allocation.

In the case of a product recommendation tool, you can use the below formula to calculate ROI:

ROI of Product Recommendations =

(Revenue from Product Recommendations / Price of the Tool) * 100

Calculate ROI of Product Recommendation Using Boost

Boost AI Search & Discovery's product recommendation analytics offer valuable insights into the performance of recommendation widgets. By monitoring visitor response and tracking resulting sales, you can optimize recommendation models and improve their placement in the buyer's online shopping journey.

The Recommendation analytics dashboard consists of four main sections:

• Overview: This section provides general yet insightful metrics and reports, including the Revenue generated from Boost's product recommendations. This metric represents the combined value of all orders associated with Boost app's Recommendation events. It gives you a high-level understanding of how your recommendation strategy contributes to your overall revenue.
• Page performance: In this section, you can analyze how different pages on your store are performing in terms of recommendation engagement and sales impact. By examining metrics such as click-through rates and conversion rates for each page, you can identify which pages are effectively driving conversions through recommendations and which ones may need improvement.
• Widget performance: This section focuses specifically on the performance of individual recommendation widgets. You can assess their effectiveness in driving conversions and make data-driven decisions to enhance performance. By analyzing metrics like click-through rates, add-to-cart rates, and purchase rates for each widget, you can identify which ones are performing well and which ones may require adjustments or replacements.
• Product performance: Lastly, this section allows you to evaluate the performance of recommended products. By analyzing metrics such as click-through rates and conversion rates, you can identify top-performing products and optimize your recommendations accordingly. This information helps you understand which products are resonating with your customers and which ones may need to be replaced or promoted more prominently.

To calculate the ROI of Product Recommendations, you just need to focus on the Overview dashboard and note down the Revenue number within a certain period of time. We suggest a 30-day period for a more insightful calculation. Our pricing is quoted monthly, so it's also easier for merchants to measure the monthly ROI of the Recommender system.

After collecting these two numbers, you can use this formula:

ROI of Product Recommendations =

(Revenue from Product Recommendations / Price of the Tool) * 100

Boost AI Search & Discovery offers multiple product recommendation types at the starting price of $19, very affordable for all businesses. Besides, we also offer an advanced add-on for merchants to unlock AI-powered recommendations.

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Why You Should Track ROI from Product Recommendations

In today's retail landscape, retailers are investing a significant amount of resources into product recommendations. However, it is equally essential for them to measure the return on this investment. To effectively track the success of their online stores, several eCommerce metrics need to be monitored, especially when it comes to measuring conversions.

While technical professionals often rely on metrics like recall, accuracy, and precision to evaluate the performance of machine learning systems, it is crucial to understand how these technical metrics translate into tangible business value. By analyzing ROI-related metrics, retailers can gain valuable insights into the effectiveness of their recommendation systems and make data-driven decisions to optimize their strategies.

ROI of Some Popular Product Recommendations in eCommerce

Recommender systems are essential for companies to boost their profits by reducing customer churn and increasing sales. Let's examine some compelling statistics that prove the effectiveness of these systems.

Netflix, for instance, has made significant investments in enhancing its recommendation engine, and it has paid off tremendously. Recommendations influence a staggering 80% of the content watched on Netflix.

By leveraging these algorithms, Netflix not only reduces customer churn but also saves a remarkable $1 billion annually through improved customer retention. Moreover, Netflix excels at providing recommendations promptly, as research shows that most members lose interest after browsing just 10 to 20 titles for 60 to 90 seconds.

Despite this limited window, Netflix successfully retains existing customers and attracts new ones. In the second quarter of 2017 alone, Netflix experienced an impressive year-over-year growth rate of 32.3%, adding 5.2 million subscribers to its already substantial base of 99 million members.

Now, let's shift our focus to Amazon, the undisputed king of online retail. Amazon introduced item-to-item collaborative filtering back in 1998 and has continuously improved its recommendation system ever since.

Unlike many other services that only incorporate recommendations at one stage of the customer journey, Amazon integrates recommendations at every step to maximize order value. Although Amazon hasn't disclosed the exact revenue generated from recommendations, industry experts estimate that a significant 35% of consumer purchases on Amazon can be attributed to product recommendations, according to McKinsey.

These examples clearly demonstrate the immense impact and financial benefits that recommender systems bring to companies like Netflix and Amazon.

Conclusion

Measuring the ROI of product recommendations on your Shopify store is crucial for optimizing your marketing efforts and driving business growth. By setting clear goals, tracking conversions and revenue, analyzing customer engagement, assessing customer lifetime value, and calculating ROI, you can gain valuable insights into the effectiveness of your recommendations. Remember, data-driven decision-making leads to increased profitability and ensures you're providing an exceptional shopping experience for your customers.