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Headlines tell us the recession is lingering, but that doesn’t mean your retail business has to be left to chance. There are innovative ways to squeeze more from every customer interaction. This is where AI and a data-driven approach can help. In this article, I’ll share 8 examples of using a data-driven site search and discovery to improve the bottom line.
You want to maximize the chances that someone buys something with each visit. One way to do that is to promote the best items in each category and every on-site search.
A search engine like Algolia does this automatically for both on-site search and your on-site collections. Whether a visitor is using your site search or just browsing, Algolia can dynamically re-rank items every 24-hours using data over a sliding 30-day period.
Dynamic Re-Ranking 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. You can set up dynamic re-ranking and forget it — it’ll do its thing — or you can preview the results in the dashboard to know exactly how product results are affected.
During a recession, every penny counts. Dynamic re-ranking gives you a good chance at increasing sales and revenue. You can take it one step further by boosting high-margin items.
By connecting your backend data and metadata such as your wholesale price vs the retail price, you can “tell” Algolia which items have the highest margins, then promote those items.
This is accomplished with Custom Ranking. It gives you control to put even more weight on the actions and attributes that are most important to your business such as inventory, margin, returns, and anything else you’re indexing.
Let’s say that you have two items in the same category that perform very well and have nearly the same conversion rate. However, one item has better margins than the other. With customizable ranking, you can give it a bump.
These kinds of rules will cut across your entire catalog so every big margin, popular product is pushed up in ranking. You’ve not only given yourself a better chance at conversion, but improved your margins!
We just spoke about focusing on the top sellers and biggest margin items, but that doesn’t mean the rest of your catalog is dead weight. While the Pareto principle says that most of an online seller’s profits come from just 20% of their products, the other 80% still has value.
Typically, long-tail keywords and phrases are very specific and can be longer than “head” terms (the top 20% of your catalog). While your team may not have time to optimize for these keywords, the Algolia engine can. Our upcoming hybrid search engine uses data to understand concepts, correct for common misspellings, and know what products in your catalog are most relevant.
In the screenshot example above, someone typed in “vertical vaccum”. They meant “upright vacuum.” Despite the wrong terminology and a misspelling, the search engine returned the correct products. Add a concept like “best” to the query and Algolia would return top sellers or high rated vacuums.
You can do this with traditional keyword search engines, but it requires writing a lot of rules and synonyms, and creating other “hacks” like keyword stuffing, to make it work. You might have time to do this for the top 20% of your catalog. AI can do it for 100% of your catalog automatically so you can maximize every opportunity.
One study showed that product recommendations account for up to 35% of Amazon’s total revenue. Businesses that personalize their sites can boost sales. It’s not just something that retailers want; 56% of online shoppers are more likely to revisit a site with better recommendations.
There are different kinds of recommendations and different ways to use data for recommendations. Some include:
While there are other recommendations types (e.g., staff selected products), by using data you can quickly enhance and improve results so you’re more likely to recommend the products that actually convert the best.
The most successful e-commerce companies in the world understand the power of personalization for improving customer experience and driving on-site conversions.
Companies using advanced personalization report a $20 return for every $1 spent. Consumers want personalized experiences, too: 91% of consumers say they are more likely to shop with brands that include relevant offers, information, and recommendations.
Whether you’re just selling on your site or have adopted an omnichannel selling approach, the data you need for personalization is there. It lives in your customer data platforms (CDP), membership management, or data warehouse, and includes information such as:
All of this information can be used to personalize search and browsing results for each user. In Algolia, it’s a matter of identifying the attributes you want to use for personalization, classifying and weighting the behavior (view, clicks, conversions, etc.), and enabling personalization tokens. You can even A/B-test personalization to determine which configuration works best. A real-time preview shows how results for any query might change. With personalization data, you can quickly improve results to win business.
It’s likely that the most popular products are the least likely to be returned. But just in case, to minimize the likelihood that someone will buy a product they’re likely to return, you can add a rule.
If you’ve connected Algolia with your PIM, inventory, shipping, returns management, or similar backend systems, you can determine which products are most likely to be returned and add that information as a custom ranking attribute. In this case, you’ll want to reduce the chances that one of these items are displayed in results.
When it comes to merchandising, you’re often promoting specific products for a given time range. For example, your end-of-year sale may run from October to December. With data-driven merchandising, you can create a rule to automatically promote all products that are on-sale or with higher-inventory.
And, the dynamic rankings, recommendation, and personalization features still apply. You may only be promoting a subset of your catalog like on-sale items, but within that subset you can still push the same data-driven rules.
So, which of the seven ideas should you start with? Merchandising? Personalization? High margin boosting or better recommendations?
Actually, It might be that some combination of these changes improves results even more than any single change. That’s where A/B split testing can help. You might think that A/B testing is for picking the best color for your CTA buttons. In fact, it can also be used to test your on-site search algorithm to determine which one drives more sales.
There are hundreds of permutations on search results that could lead to different outcomes as measured by clicks and conversions. By tweaking how results are displayed or what order results appear, you can impact search effectiveness, visitor satisfaction, and even website conversions.
Let the data lead you to the right choice.
Hard times may be ahead, but that doesn’t mean you need to give up hope. A data-driven approach can help unlock revenue in every visit.
Does it mean you need to hire more people to help? Absolutely not. But, you do need the right tools. We designed Algolia so anyone can adjust results of their on-site search and browse algorithm. No additional headcount required!
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