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How User-Generated Content Helps Improve Ecommerce Product Recommendations

Aleksandra Tadrzak

9 min read

May 14, 2024

When you run an ecommerce store, you quickly learn that some customers don’t know what they want to buy until you show it to them. Think of a confused shopper in a hardware store, unsure what type of drill they need to fix a broken cabinet at home. This customer will need expert assistance from the store clerk, someone who can listen to their problem and suggest products to solve it.

Online stores don’t have the luxury of having an in-person store clerk to assist their customers. What they do have, which works just as well, are ecommerce recommendation systems. Using customer behavior data, recommendation system algorithms can help online stores drive their sales by helping customers make a well-informed choice from their product inventory.

The power of recommendation systems

If you think that simply listing inventory on your ecommerce site is enough to drive sales, consider how 35% of all Amazon purchases are items shoppers did not look up but discovered through its robust recommendation engine.

The more historical data these recommendation engines have to use, the better the results. These recommendation algorithms are great at crunching data, performing data analysis, uncovering patterns, and predicting user preferences. Additionally, they employ various approaches, including collaborative filtering, content-based filtering, and hybrid models to generate personalized recommendations.

By analyzing user-generated content (UGC) alongside other data sources such as browsing history, consumer behavior, and purchase information, recommendation algorithms can fine-tune their suggestions to match each customer's unique profile.

In this article, we will examine the role of UGC and why implementing text-based recommendations in ecommerce is an essential step for a significant increase in sales, inspiring customer loyalty, and long-term success.

Why user-generated content is so important in ecommerce

Do you remember when your parents bought a new family car? They must have gone down to the dealership, taken a few options on test drives, and listened to the car salesperson’s spiel. But before deciding which model to buy, your parents certainly spent a few weeks discussing their options with friends and family. If you’re lucky, they even asked you!

When they finally made a purchase, they likely considered their friends’ opinions just as seriously as the salesperson’s. That’s the power of a strong recommendation, especially when it comes from a fellow consumer instead of a seller.

To put it simply, social proof will always be more convincing to customers than mere marketing. Before the internet, brands relied on positive word-of-mouth publicity to increase awareness and sales. In the internet era, user-generated content has come to fill that role. To consumers, what fellow buyers say about a brand is largely unbiased and authentic, affording UGC more credibility.

Ecommerce websites can also rely on UGC to add value to their own business. Monitoring and analyzing UGC allows brands to track customer behavior, build customer profiles, and create personalized experiences for each potential buyer, thereby improving customer retention. This is an effective strategy, as personalized ecommerce recommendations can lead to a 10% increase in average order value (AOV), according to a Salesforce market report.

Using UGC to train your ecommerce recommendation engine will refine its performance, leading to benefits for your store and your customers.

Where to find raw data in UGC

UGC contains a wealth of valuable data for ecommerce businesses if you know the right places to look. The internet is full of text information, and thousands more terabytes of it are created every minute. With artificial intelligence (AI) and a machine learning (ML) model, it’s now possible to sift through this vast ocean of ecommerce data without getting lost and return with actionable insights for your business.

If you want to mine through training data for useful information, you will need to look in the following places:

1. Text-based reviews

Buyers love to share their opinions on their purchases, and shoppers love to hear them. Amazon, one of the most successful players in the ecommerce industry, understands this well. The review section on their product information pages is composed entirely of UGC, making it easy for shoppers to make a decision.

All good ecommerce stores should allow their customers to leave reviews on the platform to gather more data on purchasing decisions and sales figures. This often influences the average order value as well.

2. Social media mentions

Social media platforms like Facebook, Instagram, and TikTok have become ecommerce platforms in the last few years. With many more customers shopping through social media apps, tracking brand and product mentions is more crucial than ever.

3. Forum discussions

Online community forums devoted to a single topic are a great place to find nice and novel text data for your recommendation engine. These digital spaces are filled with dedicated users of a product or service. They can contain unique, in-depth information you wouldn’t find in surface-level mentions on social media or short customer reviews.

4. Feedback forms

What happens when there’s not enough data available online for you to start training your recommender systems? You can go directly to the customers and ask them yourself!

Feedback forms are a great way of collecting data. They help you with customer retention by soliciting customer preferences and expectations when they visit your ecommerce store.

How text-based recommendation systems analyze customer behavior

There is a lot going on under the hood of text-based recommendation engines. Complex algorithms perform text mining to extract raw customer data that must be scrubbed and prepared for processing.

Cleaned text data can be represented in many different ways, and two of the most widely used methods are as follows:

Bag of words (BoW)

This method represents a given text as a collection of words, each assigned a value based on the frequency with which they are used in the document.

Term frequency-inverse document frequency (TF-IDF)

In this method, each word is assigned a level of importance according to how frequently it is repeated throughout the text. This is ideal for highlighting uncommon but important keywords and looking beyond commonly occurring search queries.

After processing and feature extraction, text data must be modeled in a way that allows the algorithm to make accurate predictions and personalized recommendations.

Let’s look at the technical details that make text-based recommendation engines so effective at dealing with UGC data.

Natural language processing: the key to analyzing UGC

Natural language processing (NLP) is a form of ML that allows AI programs to interpret text or speech with human-like levels of comprehension. In ecommerce, NLP allows a recommendation engine to fully understand user queries to deliver relevant recommendations.

NLP programs extract meaning from data types, primarily through sentiment analysis. Also known as opinion mining, this allows NLP programs to categorize text documents according to the emotions they contain. If you want to monitor customer sentiment for your ecommerce site, sentiment analysis is a valuable tool.

Other ways NLP can extract data from text is through topic modeling, which helps detect overarching themes in a collection of documents, or named entity recognition (NER), which automatically detects and classifies information like names, places, and other named entities within a text. With these and other tools, NLP enables text-based recommendation engines to conduct a clear and insightful analysis of UGC.

Strategies to implement UGC for ecommerce recommendations

UGC is a valuable asset for ecommerce recommendation engines, both as input data and for discovery. If you are looking to implement UGC in your ecommerce recommendation system, consider the following practices:

Integration

Look for avenues to integrate UGC into your store. Amazon is a perfect example, as its customer reviews add significant value to the site for both buyers and sellers. Find ways to encourage users to contribute their own UGC as they shop.

Personalization

UGC is invaluable for training recommendation algorithms to make better predictions. It allows the program to build a detailed customer profile outlining individual preferences and dislikes and serves as a record of previous consumer behavior and purchase history.

Real-time adaptation

Just like in-store clerks are able to adapt to the requests of shoppers, the best ecommerce recommendation engines have real-time adaptability. They instantly react to a customer’s actions and make recommendations based on their user behavior, like suggesting athletic socks to someone who is browsing for running shoes.

Challenges to using UGC for ecommerce recommendations

Despite the utility of UGC in refining the performance of an ecommerce recommendation process, it still has some critical limitations. Several issues emerge when using third-party data like UGC, which could affect the quality of data analysis.

Let’s look at the most pressing challenges to the text-based analysis of UGC in ecommerce websites:

1. Noise and bias

When considering what UGC you want to use for your recommendation engine, it’s important to be selective. Because this type of content is collected chiefly as unstructured data, it is inherently full of noise and unimportant details.

Also, certain users may be biased against a certain product category, which can affect the tone of their content. Looking past the noise and overcoming bias is a key requirement for text-based recommendation methods.

2. Scalability

Matching the recommendation engine to the scale of ecommerce operations is vital. Recommendation systems with less computing power are not built to handle UGC from datasets of tens of thousands of customers.

3. Ethical considerations

Before using UGC to train recommendation engines, obtaining consent from the original authors and the customers is important. Ecommerce companies must be explicit when using customer data and give users who don’t wish to participate a means of opting out.

Benefits of adding UGC to text-based recommendation engines

If you want your ecommerce website to serve customers better while increasing sales, it’s beyond time you started using UGC in your text-based recommendation engine.

There are multiple benefits to utilizing UGC for product features and recommendations:

Increased user engagement and brand awareness.
Higher visitor conversion rates.
Improved customer experience while shopping.
Expanding the training dataset for AI programs.
Promoting customer loyalty.

New directions for an ecommerce recommendation system

The ecommerce industry continues to grow as more consumers embrace the convenience of shopping online, and the technology behind these recommendation engines is on course to grow alongside it.

In the future, there are some exciting developments awaiting text-based recommendation systems. Let’s look at some of the most promising ones.

Multimedia content integrations

Currently, these systems focus on text-based UGC more than any other. However, there is much more content out there that contains just as valuable information, most of it in a picture or video format. However, as recommendation engines start to become more feature-rich, they can easily work with other data formats.

Advanced AI techniques

AI and ML algorithms are expected to grow exponentially in the next few years. Developments like advanced neural networks have already led to drastic improvements in recommendation engine performance, which will compound further as the technology improves.

Implementation of text-based recommendations in ecommerce

UGC plays a major role in influencing customer decisions, so you can’t afford to ignore it. A text-based recommendation system is a must-have for an ecommerce website. It will significantly improve your ability to extract valuable information from every customer conversation.

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