Give Customers What They Want with Personalized Recommendations

But first things first: we should start by defining what customers really want.

Of course, we don’t want to just speculate what customers want based on educated guesses, and fortunately, we don’t need to do so. Machine learning systems run on data — and quite likely a wealth of it.

If you’re not doing it already, start by using a customer data platform and gather explicit data about your customers’ preferences. Make sure you are capturing customers’ interactions with your brand (what they searched, viewed, saved, carted, etc.) so that the system can determine customers’ implicit preferences.

Giving customers what they want is highly important, and a good start to that is having a strong data foundation and a deep understanding of your customers’ needs and preferences.

Customers’ expectations carry a lot of weight, and meeting those demands means showing that you know and understand your customers very well.

One of the best ways to give customers what they want is by curating the shopping experience to customers’ preferences, buying habits, and real-time behaviors. Also, using relevant and timely product recommendations makes it easier for them to find what they need.

Most consumers today have a short attention span, which means they will not waste time on content that is not useful to them. By using recommendations based on their preferences, you can build a more meaningful relationship with them and improve customer retention and brand affinity. That being said, product recommendations are an important part of any ecommerce retailer’s strategy.

You can deliver dynamically personalized product suggestions for each customer as long as you have a deep understanding of their lifecycle stage, as well as using an AI-powered system such as Blueshift’s Recommendation Studio.

AI systems are highly flexible, and you can count on them for mining and analyzing user- and product-level data to curate personalized recommendations for your customers. Still, as a marketer, you need to provide the right direction, which means choosing the best recommendation themes based on a deep understanding of the customer journey and where they are on their path to purchase (e.g. research mode, already decided, etc.).

Companies that tailor their strategy and use personalized recommendations are more likely to create long-lasting relationships with their customers.

Here are a few best-practice approaches to display customized recommendations at different points of the customer lifecycle:

Add Desirability and Credibility with Callouts

Include star ratings for social proof. Add badges that show items are “new,” “best sellers,” or “top picks.”

Trigger Messages at Key Points in the Purchase Cycle

Use dynamic recommendations in response to key customer actions or when product status changes.

Validate the Recommendation

Add validity to the recommendation by explaining why the customer received a recommendation.

Personalize Subject Lines

Use dynamic subject lines that pull in product names and brand names based on customer’s interests.

Personalized recommendations are the key to customer engagement and retention. By using a customer data platform that can unify and activate your data, marketers can create great shopping experiences.

With powerhouses like a customer engagement platform, it’s easier than ever for marketers to pull data from all their existing tools into one. Also, marketers can now access real-time data from their CRMs, websites, catalogs, and data lakes with a Single Customer View.

Keep in mind that if your goal is to create a consistent and personalized experience for your customers, then automation is critical, and the key is to find a technology partner that uses AI and machine learning. At Blueshift, we’re bringing the power of AI systems and data-driven marketing to you.

Ready to get started? Reach out to the Blueshift team to learn more about how we’ve helped other brands succeed and how we could help you up-level your marketing strategy.