How can you provide effective product recommendations for customers?

One way to boost quality product recommendations is to add a gamification element to it and tie it to loyalty. Earn points towards your next purchase by providing a review.

They are six components of an excellent product recommendation engine 1 - Shop quiz 2 - Well defined navigation 3 - Collection page filters 4 - Product page bundles 5 - Shopping cart upsells 6 - Post-purchase cross-sells

Think of product recommendations as your personal shopping assistant. They help customers find gems that they might not have discovered on their own. It's like having a friend who knows your taste and points you to the perfect items. Effective recommendations can make shopping easier, more enjoyable, and truly personalized. This not only boosts sales but also builds loyalty by showing customers that you understand and value their preferences.

As a seasoned ecommerce leader, I've found that providing effective product recommendations is all about leveraging advanced technology and understanding customer behavior. At Ginkgo Retail, we use robust algorithms and machine learning to analyze purchase history, browsing patterns, and demographic data to tailor recommendations to each customer's preferences. By personalizing the shopping experience and offering relevant suggestions, we enhance customer satisfaction and drive sales.

Effective product recommendations extend beyond the confines of a single interaction or platform. They weave through the customer’s entire journey, from email marketing campaigns to social media ads and the online shopping interface itself. This seamless integration ensures that the personalized shopping experience is consistent and omnipresent, gently guiding the customer towards items that capture their interest and meet their needs, regardless of the touchpoint.

First, you can track browsing history to understand what products a customer is interested in. Second, purchase history can provide insights into a customer’s preferences and buying habits. Third, you can use data from customer reviews and ratings to understand what products are popular and well-received. Fourth, tracking items that customers add to their wish list or shopping cart can also provide valuable data. Additionally, demographic information such as age, location, and gender can be used to tailor recommendations. It’s important to ensure that all data collection is done ethically and in compliance with privacy laws and regulations. Always inform customers about the data you collect and how it’s used to improve shopping experience.

There are generally two types of datapoints you can collect to power personalized recommendations. Zero-party data is data the customer is giving away voluntarily. As an example, if you're a fashion brand and you ask a customer for their gender or preferred styles through a popup, this would be considered zero-party data. First-party data is data that you collect without the customer giving it away voluntarily, and you then use it to infer certain traits about the customer. For instance, if a customer has been browsing through summer dresses, then you might infer that they're interested in that product category. Ideally, you need both zero-party and first-party data for efficient recommendations.

To elevate product recommendations: . Privacy First: Treat customer data with the 'Spider-Man Principle'—great power, great responsibility. Seek the 'Why': Dive deeper than purchase history; understand motives. Feedback Fuels Fine-Tuning: Use real-time reactions to refine suggestions. Tell a Product's Tale: Data should narrate a story, making each recommendation a journey. Spot Unique Patterns: Utilize your data's unique insights for tailored recommendations. Privacy as a Promise: Make data security a highlight, building trust and loyalty. Remember, with the power of data comes the responsibility to respect privacy and comply with data protection laws—think of it as the "Spider-Man Principle" of e-commerce.

Gathering data for product recommendations is like being a detective. You're looking for clues about what your customers love. Start by tracking their browsing history, purchase patterns, and product reviews. Surveys and feedback forms can also offer insights straight from the horse’s mouth. This data helps paint a picture of customer preferences, enabling you to tailor recommendations that hit the mark every time. It's all about understanding their needs to serve them better.

I guess surveys don't turn out to be as fruitful as they sound. Most of the times, D2C brands doesn't make decisions based on surveys. They know their consumer pains and desires. And they try to intrigue them. You should simply stay within your relevant sub-niche of your hero product, and try to make your new offering meet the level of quality your hero product has.

You can segment customers based on demographic information such as age, gender, and location, which can be useful for recommending products that are popular within specific demographic groups. Behavioural data, such as browsing history, purchase history, and product ratings, can also be used to segment customers. For example, you can group customers who frequently purchase certain types of products and recommend similar items. Additionally, psychographic segmentation, which includes factors like interests, attitudes, and values, can be used to recommend products that align with a customer’s lifestyle or preferences. The goal of segmentation is to understand your customers better and provide them with more personalised shopping experiences.

If you're only starting out with recommendations, a good first step is to lean into contextual recommendations: e.g., if a customer is viewing a specific product, recommend complementary/alternative products. If you want to take it up a notch, you can identify a series of customer personas, i.e., consumer archetypes that typically shop with your brand (e.g., "The Bride," "The Adventurer," "The Everyman"). Your products/collections should be tagged with the archetype(s) they are most relevant to. You would then use the zero-party and first-party data you have collected to associate one or more of these archetypes with each customer and recommend the corresponding products.

Segmenting your customers is like organizing a party. You want to create groups based on interests, so everyone has a good time. Look at their shopping habits, preferences, and past purchases to group them. This way, you can recommend products that truly resonate with each group. Whether they're bargain hunters or luxury lovers, personalizing recommendations makes customers feel understood and appreciated, leading to better experiences and loyalty.

Utilize predictive analytics to forecast future purchasing behaviors based on past interactions, social media activity, and broader market trends. This forward-looking approach allows you to anticipate needs and interests before the customer explicitly expresses them, positioning your brand as insightful and proactive. For example, predicting a customer's interest in upgrading a product based on their usage patterns and offering timely recommendations can enhance satisfaction and loyalty.

Analyse your customer data to identify patterns and preferences. For example, if customers often buy certain products together, you can use “bundled” or “frequently bought together” recommendations. If you notice that customers tend to buy similar items, “similar product” recommendations would be effective. For new customers, consider “best-seller” or “trending” recommendations, as these are generally well-received products. Personalized recommendations based on browsing or purchase history can be effective for returning customers. Also, consider the context. For instance, during checkout, recommending add-ons or accessories related to the product in the cart can lead to increased sales.

Every touchpoint calls for a different type of recommendation. In your emails, you will want to optimize your recommendations based on behavioral/demographic information for every customer. At checkout, you can offer more direct cross-selling recommendations based on what the customer has in their cart. Analyze your customer journey and ask yourself, what kind of recommendation would be most helpful for the customer at this stage of the funnel?

Choosing the right type of product recommendations is like picking the perfect gift. You need to know the person well. Consider the customer's journey on your site. New visitors might like best-sellers, while returning customers appreciate personalized picks based on their browsing history. Seasonal recommendations can spark interest during holidays. Matching the recommendation type to the customer's stage and interests ensures your suggestions feel thoughtful and relevant, just like a well-chosen present.

At my e-commerce agency I have great experience incorporating a "Shop the Look" section on product pages. It's an innovative strategy to inspire customers and increase the Average Order Value (AOV). Users are inspired by outfits and not single items. I think this approach enhances the shopping experience and is a great way to drive extra sales. If you are using Shopify, there a multiple ways to set it up: Look for an app, setup a collection or ask your agency to develop it for you.

First of all, you should measure incremental revenue generated by product recommendations. A/B testing different recommendation techniques/customer archetypes will then allow you to optimize your recommendation engine.

Testing your product recommendations is like trying out a recipe before serving it at a dinner party. Use A/B testing to see what works best—compare different types of recommendations, like personalized versus best-sellers, to see which ones your customers prefer. Keep an eye on metrics like click-through rates and conversion rates to understand their impact. Regularly tweaking and refining based on feedback and data ensures your recommendations always hit the spot, just like a well-cooked meal.

We conduct split tests as follows: Batch 1: General audience A/B test Batch 2: Personalized audience A/B test A combination of the 2 will create different segments that can be compared at the end of a period of 4-6 weeks, and then decisions can be made for the next test. With each cohort, new product data can be inputed.

Beyond traditional A/B testing, consider holistic testing strategies that account for the entire customer journey rather than isolated interactions. This means testing different recommendation models at various points in the customer journey to understand how they influence not just immediate clicks or purchases but also long-term engagement and loyalty. For instance, comparing the impact of personalized vs. bestseller recommendations on not just conversion rates but also on repeat purchase behavior can offer deeper insights into their effectiveness.

Consider setting up recommendations manually to ensure logical links between products that follow a specific product journey. Analyze historical data and apply judgement to improve the algorithm of recommendations

Don’t overlook the power of social proof in your product recommendations. Incorporate customer reviews, ratings, and user-generated content to lend credibility. It’s like getting a recommendation from a friend; people trust it more. Also, consider leveraging machine learning algorithms to refine suggestions over time, ensuring they become more accurate and personalized. This combination of social proof and technology can significantly enhance the effectiveness of your recommendations.

Offer recommendations in your post-purchase emails. That way, you can expose customers to other products right from the communications they are most likely to read (for example, their order delivery update emails).