Know It All About Product Recommendation Engines: Are They Useful?

Thanks to e-commerce, customer experience personalization have become a de-facto norm. Already 89% of digital businesses invest in personalization, particularly in delivering personalized content. These companies include big names like Coca-Cola, Netflix, Sephora, etc. Even in the world of B2B sales and marketing, personalized customer experiences are increasingly becoming an expectation. As businesses continue to compete for market share and customer loyalty, it is essential to provide tailored recommendations that meet the specific needs of each customer. Based on a Salesforce survey, 52% of customers expect offers to always be personalized — up from 49% in 2019. In fact, 91% of consumers are more likely to shop with brands that recognize, remember, and provide relevant offers and recommendations. Their expectations are no different when acting for their company instead of for themselves.

This is where product recommendation engines come in. A recommendation engine is an algorithmic tool that uses data to suggest products or services to customers. But, how do they work? They use various types of data to suggest products to customers. This data can include purchase history, browsing behavior, customer demographics, and other relevant factors. The engine analyzes this data to identify patterns and make recommendations based on customer behavior. For example, if a customer has previously purchased a specific product, the engine may suggest complementary or related products that could be of interest to them, by identifying products that are often found in the same shopping baskets as that product.

There are several benefits to incorporating product recommendation engines into B2B sales and marketing strategies:

1. Increased revenue: By suggesting products that customers are more likely to buy, recommendation engines can help businesses increase their revenue. In a survey done by Invesp, 54% of retailers reported product recommendations as the key driver of the average order value in customer purchase. Furthermore, 44% of customers say they will likely become repeat buyers after a personalized shopping experience with a particular company… and repeat business means more revenue!
2. Improved customer experience: Personalized recommendations can help customers find the products they need more quickly and easily, leading to a better overall experience. Indeed, 49% of consumers said that they have purchased a product that they did not initially intend to buy after receiving a personalized recommendation.
3. Increased customer loyalty: When customers feel like a business understands their needs and preferences, they are more likely to become loyal customers. In a Salesforce survey, 84% of business buyers stated being more likely to buy from sales reps that understand their goals. Besides, 70% of consumers say that how well a company understands their individual needs impacts their loyalty. 
4. Reduced salespeople’s workload: Recommendation engines can automate the process of suggesting products, reducing the workload for sales and marketing teams - and getting rid of the guesswork altogether. According to McKinsey & Company, personalization increases marketing spending efficiency by as much as 30%. This can become a huge time-saver for businesses with large product portfolios.

In conclusion, product recommendation engines are a valuable tool for B2B sales and marketing teams looking to provide personalized customer experiences. By using data to suggest relevant products, businesses can increase sales from existing customers based on ordering patterns and preferences, and thus, boost revenue. They can also create customer satisfaction by understanding their needs, offering individualized suggestions and relevant content, and creating targeted marketing campaigns. 

However, according to ClickZ findings, one of the biggest challenges to personalization is “having adequate IT support for data extraction”. Here is where Fructifi steps in. Our software is able to extract data via a direct connection with your ERP or CRM, or by using a daily CSV exporter. No need to hire a costly data scientist, no need for time-consuming quarterly extractions - in a few clicks, your sales team will instantly know what product each customer should be offered, based on their purchasing behavior -  a game-changer for your B2B sales and marketing strategies.