In today’s fast-paced, data-driven world, consumers have embraced real-time, personalized recommendations delivered to their preferred device in a way that is unobtrusive and engaging.
However, very few e-commerce companies are currently delivering personalized search recommendations. The strategies and technologies are all available and proven today, enabling those who rise to the challenge to reap rewards like customer loyalty, brand elevation and incremental revenue.
Give your site the search personalization advantage
According to Survata, 49% of consumers’ first product searches begin at Amazon. You may be surprised that Amazon doesn’t offer personalized suggestions via search. No matter what kinds of products you’re selling, e-commerce search personalization gives you the perfect opportunity to create a customer experience advantage against Amazon and others right away.
What’s more, implementing personalized search results can divulge numerous insights that you may not even have considered. Real-world query data from Constructor reveals powerful insights about what individuals buy primarily, and then secondarily based on their searches.
For example, for a grocery retailer, Constructor’s machine learning algorithms were able to deduce that people who purchased flour were also likely to buy measuring cups. Not a far stretch, right? But we also found that people who purchased pre-packaged cheese slices bought child-sized water bottles, giving us more insight on the purchases of parents. Even more unusually, we discovered that people who bought Dr. Pepper also bought Gas-X. The jury is still out on whether correlation implies causation on that one.
While it’s fun to think about specific product recommendation correlations, Constructor’s algorithms are doing very sophisticated learning from many shoppers, searches and purchases. Personalization comes from finding behavior matches between our shopper and others with similar behavior patterns. And the actual search result rankings will differ from shopper to shopper.
Now that your interest in personalized search has likely been piqued, let’s take a closer look at precisely what it is, as well as what it isn’t. This will give you the foundational understanding needed to see for yourself why implementing personalized search results is worth it when it comes to maximizing revenue and creating an engaging experience for visitors and customers alike.
Here’s what e-commerce search personalization IS
Search personalization is an innovative new approach that leverages artificial intelligence (AI), automation and optimization rules to create recommendations that are both unique and individualized.
You may be wondering how these personalized recommendations are so accurate. This happens through search inputs that span:
- Historical data on individual customer preferences
- Data from other users, including their purchase history, pages viewed and their search queries. This produces an abundance of data which can then be run through a personalization algorithm to create relevant, attractive and personalized recommendations.
- A powerful machine-learning algorithm that learns the best products to return based on the individual’s preferences, purchase intent and their current search query.
Successful personalization today requires AI technology and large data sets to drive superior outcomes. While many digital experience vendors make claims about “personalization” and “artificial intelligence,” very few actually deliver.
Here’s what search personalization IS NOT:
With any hot technology like AI, vendors are quick to latch on to the new terminology. “The AI label is still being stuck on absolutely anything,” states Ben Hanson of WhichPLM. This means that buyers must ask hard questions about how technologies are implemented and evaluate potential outcomes from specific use cases. Here’s a sample of what might be incorrectly presented as “personalization:”
- Segmentation that covers a few clumsy demographic data points like gender or age.
- Retargeting approaches that show consumers products they’ve looked at but haven’t bought.
- Manual curation of buyer profiles or your product catalog. Instead, you’re leveraging computing power and intelligent algorithms to help accentuate and enhance your staff’s efforts.
- So-called “personalization” approximations that leverage neither predictive algorithms nor purchase intent data to create their individualized recommendations.
What happens with other companies’ attempts at personalization is that you end up with segmentation, not “personalization.” Segmentation may provide a level of refinement over run-of-the-mill query keyword relevance, but because it doesn’t use predictive algorithms, it will alway be less precise than AI-driven personalization. What’s more, segmentation-based approaches are not able to learn more about preferences over time.
Here’s an example: a shopper from Boston who prefers dressing in California-casual clothing. When using a poorly personalized search engine to look for a new outfit, she’ll need to page through a lot of off-target search results before she finds the items she prefers. That’s because geographical segmentation de-prioritized the breezy outfits she prefers. In other words, legacy search engines treat her like other Bostonians, rather than like the individual that she is.
Achieve success with personalized search results
So now that you have a better understanding of what personalized search results are and encompass (and what they are not), what specific criteria should you look for when choosing a personalized search solution?
First, you’ll want to look for a solution that is rooted in data and continues to learn and improve its recommendation algorithms. Secondly, when your search results are as unique as the individual, you are realizing the potential of artificial intelligence, improving the shopping experience for every individual and treating all customers as a “segment of one”.
The recommendations should be accurate at the moment the buyer is seeking them. There can be no delays. These recommendations can be made after a search, or once a product is added to the cart, but whenever and wherever they appear, it must be in real-time to have the biggest impact on both the customer experience and your bottom line.
Constructor leverages many different layers of machine learning in order to bring your users truly personalized results. From co-purchase behavior to user preferences from similar purchases, the data that is created can be analyzed and filtered in a meaningful way to create the kind of experience that will make customers feel that there is the true spirit of AI behind their search.
Finally, personalized search results have to deliver a measurable improvement in business outcomes. You need to see marked growth in revenue, profitability and all of the metrics that bolster conversions.
Want to learn more about optimizing e-commerce on-site search?
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