Exploring the World of Ecommerce, A/B Testing and Personalisation - Insights from an Experienced Leader

Category: Product Recommendations

The Dos and Don’ts of Product Recommendations: Best Practices for E-Commerce Websites

Welcome to the world of e-commerce, where the power of product recommendations can make or break your online business. In this blog post, we’ll be exploring the Dos and Don’ts of product recommendations, so you can keep your customers engaged, coming back for more and drive customer lifetime value.

Do: Personalise Your Product Recommendations
It’s no secret that customers love to feel special, and personalising your product recommendations is a great way to do just that. By using data like browsing history and past purchases, you can suggest products that are tailored to each individual customer’s needs and preferences. Who knows, you might even recommend something they didn’t know they needed!

Don’t: Spam Your Customers
We’ve all been there. You’re browsing a website, and suddenly, you’re bombarded with endless product recommendations. Not only is this annoying, but it can also be overwhelming for your customers. So, make sure to limit your recommendations to a reasonable amount, and avoid displaying the same products over and over again.

Do: Keep It Relevant
When recommending products, make sure they’re relevant to what your customer is currently or have browsing or purchasing. If they’re looking for a new phone case, recommending a set of kitchen knives is not only irrelevant, but it could also come across as pushy and annoying.

Don’t: Be Too Predictable
While it’s important to keep your recommendations relevant, don’t be afraid to mix things up a bit. Recommending items that are slightly unexpected can surprise and delight your customers, and keep them coming back for more. For example, if a customer is buying a new winter coat, you could recommend a matching scarf or gloves. It’s the little touches that can make all the difference.

Do: A/B Test and Experiment
Like anything in e-commerce, product recommendations require constant testing and experimentation. Don’t be afraid to try new things, like recommending products based on social media activity or location. And always track your results, so you can adjust your recommendations accordingly.

Don’t: Forget About the Customer Experience
At the end of the day, the customer experience should always be top of mind. Make sure your product recommendations are displayed in a way that’s easy to navigate and understand, and avoid any pop-ups or interruptions that could disrupt and distract from the shopping experience.

So, there you have it – the Dos and Don’ts of product recommendations. By following these best practices, you can keep your customers engaged and coming back for more. And who knows, you might just make a few sales along the way!

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Creating Personalised Product Recommendations: Going Beyond “Customers Who Bought This Also Bought”

As an ecommerce professional, you understand the value of giving your clients a personalised experience because you are a leader in e-commerce. Personalisation can increase sales, foster customer loyalty, and enhance the entire purchasing experience. However, are you fully utilising personalised product recommendations?

Sure, we’ve all seen the “Customers who bought this also bought” section on product pages. It’s a classic tactic that has been used for years to encourage additional purchases. But if you want to take your personalisation game to the next level, it’s time to go beyond the basics.

So how do you formulate personalised product suggestions that your customers will actually find appealing? Here are some pointers to get you going:

Use AI to its full potential.

Artificial intelligence (AI) has revolutionised the world of ecommerce personalisation. By using machine learning algorithms, AI can analyse customer behaviour and preferences to deliver truly personalised recommendations. Consider investing in an AI-powered recommendation engine to take your personalisation efforts to the next level.

Get creative with your recommendation placements.

Sure, the “Customers who bought this also bought” section is a tried and true method, but don’t be afraid to get creative with your recommendation placements. Consider adding personalised recommendations to your homepage, category pages, and even your checkout page. The more opportunities you give your customers to discover new products they’ll love, the better.

Consider customer context.

Personalisation is more than just making product recommendations based on prior behaviour. It’s also about taking into account the customer’s current context. A good example is, if a visitor is browsing your site from a mobile device, you might want to recommend products that are more likely to be purchased on-the-go as impulse buys.

Use social proof

Social proof is a powerful behavioural-science motivator when it comes to purchasing decisions. Consider adding social proof elements to your personalised recommendations, such as reviews, ratings, or even user-generated content. Seeing that other customers have purchased and enjoyed a recommended product can be a powerful incentive to make a purchase.

Don’t forget the human touch

Finally, don’t forget that personalisation isn’t just about algorithms and data. Adding a human touch to your personalised product recommendations can go a long way in building a connection with your customers and drive value for your website. Consider adding a personalised note or recommendation from an member of your team to show your visitors that you truly care about their experience.

In conclusion, creating personalised product recommendations is a powerful way to boost sales and improve the overall shopping experience for your customers. By leveraging the power of AI, getting creative with your placements, considering customer context, using social proof, and adding a human touch, you can take your personalisation efforts to the next level. So, what are you waiting for? Start creating personalised recommendations today and watch your sales (and average order values) grow.

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