3 Tactics for Growing Revenue Using Data-Driven Search and Discovery
Amid economic turbulence, many retailers are revamping their marketing strategies to try and bring in more customers. However, increasing advertising spend is not the end-all-be-all solution to boosting their bottom lines.
Instead, they can increase their revenue by more effectively capitalizing on the customer interactions they already have on their e-commerce platforms and outperforming competitors. How? By prioritizing data-driven search and discovery technology that powers relevant recommendations and promotes fan-favorite items that are less likely to be returned.
1. Improve recommendations to increase cart order value.
According to a recent study, 56 percent of online shoppers are more likely to revisit a site with better recommendations. Another report shows that 74 percent of those polled believe that their loyalty to a brand is about feeling understood and valued, not discounts and loyalty perks, and 64 percent of respondents prefer to do business with a brand that knows them.
There are various ways to use data to improve recommendations, including different kinds of recommendations and unique ways to leverage data to drive these recommendations. Here are a few examples:
- Trending: Suggest other items that are trending in popularity and related to the searches your customer has performed.
- Ratings-based: People want to buy products with the best ratings.
- Personalized: Based on what you purchased last time, browsing history, location, or other factors, we recommend these other products. We’ll cover this more in the next section.
Using these data-driven methods, you can quickly enhance and improve results based on how customers interact with products so you’re more likely to recommend the products that actually convert the best.
2. Boost the biggest sellers to the top and promote items that are least likely to be returned.
To maximize the chances that every shopper completes a purchase with each website visit, you want to promote the best items in each category and every on-site search. An artificial intelligence-powered search engine empowered with dynamic re-ranking capabilities can keep track of this automatically — whether a visitor is using your site to shop or just browsing — and re-rank all items regularly using data from a set time period.
This approach adjusts results based on events like clicks, purchases, signups or other positive signals. Over time, it will automatically push the best items to the top, and you can set up your platform so that it either handles website updates on its own or else allows you to preview the updated results in the dashboard before publishing.
Alternatively, you also want to ensure that you’re minimizing the likelihood that someone will buy a product that's likely to be returned. Just as your search and discovery platform can use data to determine the most popular products, it can also connect with your PIM, inventory, shipping, returns management or other background systems to identify which products are most likely to be returned, and add that information as a custom ranking attribute.
This tactic achieves multiple goals at once: it improves customer satisfaction, thus promoting brand loyalty, and it also helps retailers deal with issues of inventory overstock by ensuring that they’re getting the right products in front of the right people while minimizing returns.
3. Personalize, personalize, personalize.
Companies using advanced personalization report a $20 return for every $1 invested, and consumers continue to stress that personalized experiences are important to them, too. Ninety-one percent of consumers say they're more likely to shop with brands that include relevant offers, information and recommendations.
Everything you need to provide personalized experiences for your consumers is in your customer data platforms, membership management or data warehouse. These will tell you everything you need to know about site visitors’ demographics, the ads and banners they click on, recently viewed or purchased products, and even their preferred colors and brands.
With a highly scalable, AI-powered search and discovery platform, you can turn that raw data into an improved browsing experience for everyone who visits your site, which will translate directly into business wins.
Piyush Patel is chief strategic business development officer at Algolia, a leader in globally scalable, secure, digital search and discovery experiences that are ultrafast and reliable.
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Piyush Patel has been building high-performance teams in the area of platforms focused on Digital Experience and has helped to shape and build teams at many well-recognized brands in the space holding positions in digital strategy, services delivery, channels/alliances and product strategy.