Retail Strategy

7 Ways to Use Enhanced Product Attribution Data Across Your Ecommerce Business

Retail Strategy

7 Ways to Use Enhanced Product Attribution Data Across Your Ecommerce Business

“If you build it, they will come” goes the maxim, but given that only 10 percent of ecommerce businesses survive their first 120 days, perhaps the secret to success is more complex. There’s no doubt that the conventional approach, which attracts high volumes of low quality traffic, falls short. What can transform the fortunes of retailers, however, is closer alignment of product attributes and customer behavior data to maximize retention and order value. That requires granular product attribution data. Here are 7 ways to use that enhanced data to grow your ecommerce business.

1. Site Search

The humble search bar is the starting point for 43 percent of online shoppers, yet 42 percent of ecommerce stores manage to overlook the importance of optimizing their search feature. It’s no longer acceptable to return 10-plus pages of results that drop off dramatically in relevance after the first onward click. Customers are looking for focused, relevant suggestions that evolve their previous search history and resonate with their current context. By using granular product data and enabling long-tail semantic searches and predictive autocomplete, retailers can guide users directly to the items they really want. 

And it’s not just about finding the product. Sites that support semantic searches (which requires enhanced product data) can see cart abandonment rates drop down to as low as 2 percent.

2. Personalized Marketing

At this stage of the digital age, marketing should be personalized by default. Few things rankle more with today’s millennial or Gen Z shopper than finding themselves shoehorned into generic segments, such as age, location and gender, to match them with a pre-configured persona. Yet the practice is still common, and it explains why only 22 percent of online shoppers are currently satisfied with the level of personalization they receive. 

E-commerce fashion retailers do not need to be afraid of collecting customer data to build a richer psychographic profile. According to Salesforce, 57 percent of shoppers will willingly share their personal data in return for a better, more personalized customer experience. If it gets them better discounts, resolution of issues, stock alerts and product recommendations, they will embrace the exchange. For retailers, 88 percent of online businesses see a lift in sales when they offer personalized results and services.

3. SEM and SEO

No matter what is displayed on the virtual clothes rack, the quality of the traffic you attract to your e-commerce store is a defining feature of how many visitors become customers. Search Engine Optimization (SEO) and Search Engine Marketing (SEM) are themselves the engines of that acquisition strategy, and with enhanced product data they are better equipped to attract engaged customers who are likely to convert. 

Gone are the days when a site could stuff product descriptions and meta data with the most popular keywords and hurdle to the top of the SERP. Today’s search engine, particularly Google with its Enhanced E-commerce feature, rewards relevance and customer experience. The goal should be to sow longer tail keywords to attract higher intent customers. The benchmark for CTR in e-commerce through paid search is below 3 percent. With enhanced product data from the outset, the chances of conversion are far more favorable, allowing your advertising budget to go further.

4. Better Filters and Facets

The goal for any fashion retailer is to help shoppers quickly find products they already know about and discover products they didn’t. Many e-commerce stores are still struggling to outperform the brick-and-mortar store sales assistant in this respect. More granular filters and facets can help, as well as a user-friendly interface that allows customers to add or remove fields easily, even at checkout. Rather than showing visitors a static selection of filters, enhanced product data can help you customize options according to previous interactions or current context (e.g., “On Sale!” “Just in!”). Likewise, more targeted and granular facets build a fuller picture of customer preferences, leading to better product recommendations and suggestions in the future.

5. Product Descriptions

It’s easy to treat product descriptions as static features, and in the worst cases e-commerce retailers simply stick with the manufacturer’s product description. By combining human, relevant product descriptions that focus on benefits and pain points rather than specs, supported by intelligent product tagging, retailers can build stronger connections with consumers in the digital setting. That level of detail can evolve through the customer funnel. As buyer awareness increases, retailers can trim down descriptions and focus on more compelling triggers such as scarcity or urgency.

6. Trend Forecasting

The elements might be as simple as color, pattern and design, but the permutations in retailing are limitless. Enhanced product data allows retailers to analyze real-time and historical customer behavior for clues about the next styles and trends that are likely to be in demand. Retailers are better equipped to respond at speed too, breaking free from the conventional fashion or apparel cycles and seasons, and reacting to spikes in demand with fast, agile product runs in the appropriate quantity. 

Trends do not emerge out of the void. Customers leave hints with every interaction about what is likely to be popular next. With the ability to analyze interactions with granular data, retailers can extract that insight in advance.

7. Inventory Optimization

Stock piled high in the warehouse or heavily discounted clearance items both impact the bottom line. Each is the result of insufficient insight into customer demand. The more granular a store can go with its data, the easier it is to synchronize supply and demand, opening up more attractive options than discounting. Likewise, more sophisticated forecast modelling can reduce the occurrence of “out of stock” warnings. Studies show that 31 percent of customers will switch to a competitor if their product is unavailable on their preferred site. And that’s just the first time. 

We’ve covered just 7 ways to improve your ecommerce business through enhanced product attribution data. Now discover how Lily AI uses more than 15,000 product attributes and the largest proprietary training data set in the world to make your retail business boom.


Neil Patel - 5 Ways to Make Your E-Commerce Website Search Feature Convert

Smart Insights - Personalized marketing: Brands and e-commerce retailers

Yoast - Product page SEO: 5 things to improve 

Shopify - SEO for Product Descriptions: 5 Steps with Case Studies & Examples

Forbes - Three AI And Tech Trends That Will Transform The Fashion Industry

BigCommerce - How Big Data Can Improve Inventory Management