Customer needs are often much less complex than they appear. At the end of the day, customers want a seamless, personalized experience. And it is up to the brand to provide them with that.

Consumers today want to shop without having to spend too much time tracking down what they need, while marketers need a way to expose customers to their diverse product catalog without derailing the path to purchase. Conveniently, product recommendations can solve for both needs. Let’s delve into how the right recommendations can lead you to cracking the code on customer experience and boost your business metrics along the way.

What Your Customers Are Looking For

These days, good customer experience is almost synonymous with personalization. Customers expect to be treated as an individual, and they have little patience for brands who do not cater to this expectations: a staggering 71 percent of consumers report experiencing frustration when their shopping experience is impersonal. A personalized experience shows the consumer that the brand is thinking ahead of them, not the other way around. When it appears that there wasn’t much thought put into the experience, consumers become annoyed and often take their business elsewhere.

To meet your customers’ needs and edge out your competition, you must figure out how to effectively serve them at an individual level, and build their sense that you care about facilitating a smooth journey for them every time.

Use Data to Put Customers in Context

In their State of the Connected Consumer report, Salesforce found that 58 percent of Millennials—a key demographic—are willing to exchange their personal data for product recommendations that are more personally relevant. Additionally, 52 percent of Gen Xers and 42 percent of Baby Boomers said they would do the same.

Case in point: Monetate’s Back-to-School Shopping Study found that the more granular a brand is able to get with personalization, the better. In fact, 77 percent of respondents said they were more likely to shop at a retailer that marketed products based on their child’s age, gender, or grade level. Whereas consumers used to keep information about themselves guarded, personalization methods such as product recommendations have driven them to be more open to sharing their data for collection and use.

So, what does this new trend of data-openness signal? It means that consumers are willing to give you personal information, but only on the condition that they receive a more valuable interaction in return. Brands must use this data wisely in order to deliver on that expectation. Gone are the days of “wisdom of the crowd” recommendations that only offer suggestions for “purchased-also-purchased” and best seller items. Having more audience data and permission to use it gives brands a huge opportunity—but how best to go about putting the theory into practice?

In order to provide relevant product recommendations, brands are deploying algorithms that leverage multiple data sets (including historical data, third-party insights, real-time visitor behavior, and inventory information) to form a more complete picture of each individual in the moment. This framework is then used to deliver relevant recommendations in real time that speak to a customer’s identity and where they are in their journey to purchase—effectively balancing customer experience with your merchandising goals, for a win-win.

Recommendations Provide Value for Customers—and Marketers

Product recommendations have become a must-have for every brand because they are an excellent way to increase average order value, drive higher profits, and secure customer loyalty. Ideally, product recommendations can serve both consumers looking for an optimal customer experience, and marketing teams with inventory requirements to meet. While many may view these goals as separate, and sometimes even competing, a good recommendations solution should be able to advance both.

For example, by making sure that the products shown are in stock and in the customer’s size, brands can ensure a smoother interaction that is less likely to end in discouragement After all, falling in love with a product is no fun if the product isn’t actually available for purchase. It also doesn’t result in a sale.

A good product recommendations engine will let you use algorithms to display top settling items, new products, recently viewed products, products purchased by others, and more. But it’s important that these algorithms also be infused with customer context so that they are relevant to the individual. If a business is looking to highlight a new product line, they can strategically display those products in their recommendations in order to increase product visibility and drive purchases, while taking into account the user’s in-the-moment context to make sure that they are displayed at the stage of the customer journey most likely to spark appeal.

Make sure you have a product recommendations engine that will allow you to provide a personalized customer experience that also aligns with your business goals, so you can drive conversions while boosting customer loyalty.

Testing and Optimization for Improved Customer Experience

Testing and optimization is a powerful combination that can be used to improve the customer experience. Let’s take a look at a few features of product recommendations and how they enable retailers to know their customers better.

Pulling Relevant Information

In order to deliver relevant recommendations to customers, you will need to consider using a product recommendations engine with machine learning capabilities. With machine learning, brands can grab millions of data points from all channels in real time to determine the best experience for each customer. This information can then be used to inform product recommendations in the moment, which will present customers with the most timely and relevant items possible.

A machine learning-powered solution collects data for individualization over time, improving its understanding of relevancy as it gathers additional datapoints to test its hypotheses. For returning customers, a machine learning personalization engine will use a combination of historical customer data (from favorite product category to clothing size), demographic and location-based data, and real-time browsing data to make a decision about what products to display. Gradually, as the engine gets to know that customer better and better, the decisions it serves will be more on point. For new and unrecognized customers the engine will use what data it can harness (IP data like location and weather, for example, and all current browsing data as it unfolds in the moment) and will serve content based on what similar customers have responded well to in the past. As that customer moves through the site, the engine develops a better and better understanding of how to serve them—and customers similar to them—in the future.

Optimizing the Experience

Testing and segmentation will allow you to learn more about what your customers want and how you can tailor the overall experience for them. A test and segment solution can help you think out of the box on how to deliver a personalized recommendation. Find the segment that reveals your customer’s most urgent needs, whether that means targeting based on local weather or purchase frequency.

Testing could also reveal areas of improvement in recommendations content, placement, or display that you may have yet to explore. For example, men’s big and tall apparel retailer Destination XL made an interesting discovery when testing how visitors preferred to see products displayed on category pages. Through A/B testing, they found that customers preferred to view product recommendations displayed three across (which differed from their hypothesis). As a result of this small tweak, Destination XL saw an increase in conversion and add-to-cart rate for visitors.

Monitor the Results and Optimize

As you continue to test and optimize, listen to what the results are telling you and make adjustments as needed accordingly to better serve your customers.

Your brand’s needs are just as important as the customer’s, however. Make sure that the changes you are making to the experience not only benefit the customer, but align with the metrics you care most about. That way, you will end up with successful product recommendations, happy customers, and completed goals.

The Bottom Line

Customer needs are ever-changing, and it is up to you to shift along with them. While it may seem like a complex task at times, the true key to cracking the code on product recommendations is to listen to your consumer. Leverage data from across all channels so that you can be responsive to your customers and pinpoint what they want out of their interaction with your brand. Once you find a way to present your customers with the right products at the right time, you will be well on your way to nailing the perfect customer experience.

For more information on how to get started with product recommendations, head over to our Monetate Intelligent Recommendations page. Or, if you’d like to speak with an expert, contact us today.