Customers Don't Know What They Want Until You Show Them

Online shopping made speed and convenience the most important aspects of the customer experience. Now that fast delivery and unlimited variety are the norm, customers are overwhelmed with options and drawn to businesses that offer personalized recommendations for easy buying decisions. Data-driven recommendation engines can help businesses shorten the sales funnel and reach niche markets. Instead of waiting for buyers to make a decision, smart businesses show customers which products they want next.

Recommendation Engines are Reshaping the Customer Experience

Businesses are rapidly adopting data analytics to inform back-end decisions, but these same tools can improve front-end customer service. Customers want the best of both worlds: the flexibility of online shopping and the personal experience of learning about products and getting tailored suggestions from a knowledgeable salesperson. Predictive marketing is the key to bridging this gap in expectations and driving more sales. Supplied with detailed customer data, an AI-powered recommendation engine can predict the right product solution for customers and companion items they may want in the future.

Predictive marketing combines data mining and behavior mapping to fulfill a need before customers identify one. The two major data models include content and collaborative filtering. In content filtering, the database algorithm comes up with recommendations based on user-supplied information, such as keywords or price ranges. In collaborative filtering, the algorithm looks at buying behavior over time to spot meaningful trends between customers with similar habits.

Think about the predictive tactics Amazon uses to influence purchases. When a customer views a product, the site instantly creates a bundle recommendation based on recent search behavior and products others shoppers bought together in the past. If the customer adds the product to a cart, Amazon tries to cross-sell other products the buyer is likely to enjoy.

Using Data to Boost Customer Loyalty and Sales

Recommendation engines give businesses of all sizes the means to sway customers at more touchpoints without scaling up their sales teams. When faced with too many options, the majority of customers either put off a decision or go with the most popular product in their price range. In the latter case, customers often settle for a mainstream product when a niche product could better serve their needs. Consider these key benefits of implementing a recommendation engine.

1)Social Proof: Customers are more likely to buy products that are recommended by people they know or customers who share their interests.

2) Personalization: Consumers like to feel valued, and they respond positively to customer-centric business models that anticipate their needs.

3) Customer Satisfaction: Accuracy improves as businesses collect more customer data. Customers are more likely to be happy with product choices and trust the recommendations in the future.

4) Sales Volume: Recommendation engines introduce customers to niche products that normally sell at a much slower pace. This change in sales volume offers more consistent ROI and inventory control for businesses, reducing dependence on top-selling items.

Recommendation engines solve a common problem for businesses — how to get customers to make a decision now, instead of leaving the site. At the same time, recommendation engines depend on high volumes of data to analyze complex buying behavior. Moving forward, businesses that want to stay competitive have to find non-invasive ways to collect data and deliver effortless customer experiences that answer the question "what's next?"

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