Retailers understandably focus on enhancing their on-site search capabilities, but that doesn’t always extend to their Search Engine Optimization (SEO) and Search Engine Marketing (SEM). Why do you think that is, and what effect is that having on their top-line revenues?
I think the two problems are different. Within a retailer, there’s generally a marketing function that is looking at the top of the funnel to drive relevant traffic to their site. SEO and SEM are tools those marketing teams use to drive that top of the funnel. On-site search is usually managed by digital product teams that are concerned with product discoverability and optimizing the experience of that inbound traffic, on through to purchase.
For on-site search, there’s a lot of capability for the retailer to tune their search algorithms to make sure that when a customer starts typing to discover products on their site – that they surface what the customer is actually looking for. When you start to move that problem further up the funnel, you start to run into the problem of search engines like Google or Bing – you can’t really tune what results come back; all you can do is try and provide more relevant data to those search engines to help them surface up the right information to your customer who may be searching on those sites.
So one of the challenges is that product data that is often lacking in on-site search also lacks when you actually push that product data out to a search engine provider, like Google or Bing. Again, from the top of the funnel all the way into your site, if you’re working with less-than-stellar product data, the consumer has to work harder to find what they’re looking for, and they’re going to be coming to your site, potentially, from a link or a search result that’s not quite as relevant to them. They’re really just trying to get to a starting point on your website.
How does having enhanced product data ensure retailers are better equipped to attract engaged customers who are likely to convert?
It kind of goes back to the whole mantra of “garbage in, garbage out”. The better and more relevant data that you can provide about a “fitted black cocktail dress” or let’s say a “floral print day party dress” to your on-site search algorithms, or to the big search providers like Google and Bing, the more relevant your results should be. The more consistent and rich product data that you can provide in your Google or Bing product feeds for instance, the better of a chance there is for those search providers to recognize this particular product when someone types in day dress, or floral print dress.
What does this product data do for a retailer’s page quality – and why does that matter?
There are a couple of areas where page quality really matters. Very generally, what search engines and product listing ads are looking for on your site is content that is static, and content that is relevant. Hard links to different pages on your site; relevant product information – that sort of thing. So it’s twofold: the better data you can pump in and proactively give to a search engine for doing marketing and paid listings, the better off you’re going to be.
Conversely, when these search engines are crawling your website, if you’re able to build a page with more relevant product attributes, either in copy or underneath the underlying code, or more relevant information that these sites can crawl, the organic search results that they’re going to surface up are going to be that much more relevant to a customer signaling their intent with their search phrase.
What sort of retailer behavior does Google’s enhanced ecommerce analytics reward these days?
One area we’ve heard about when talking to folks who specialize in SEO is ensuring that you have things like static content and links on your site that are able to be indexed by search engines. On a website, when you have dynamic facets and filters, as products go in and out of stock, they’re showing up, but then they’re also dropping off the website. It’s very difficult for a search engine to actually index those pages and provide relevant content, because what they indexed yesterday might be gone tomorrow.
One of the big things that we see is retailers creating static links to pages that are “curated”, if you will. They are specifically designed to drive traffic for a particular product. Some of the behaviors that we see are, if you have a particular product category, or a particular type of product that you’re looking to drive SEO relevance up in, you can create a landing page that has that product or has several products that are relatively static – which search engines can then crawl, index, and drive up. One of the ways that you can enhance that with product data is providing things such as more relevant descriptions, or more information about a product.
Once you’ve created this static page, you can write better copy or better product descriptions with better product data. That’s just one of the tactics that is pretty commonplace that a lot of our customers and retailers use to actually drive up SEO for certain products that they care about.
What are some of the key aspects of the Lily AI customer intent platform that make it so valuable for retailers looking to convert via SEO and SEM?
The first step is really better product data. One of the biggest challenges we see for on-site search is just that lack of product data: the lack of descriptors of attributes about the product, and the inability to be able to drive dynamic or interesting copy based on product attribution. The Lily AI customer intent platform will take all of our customers’ data in and run it through our machine learning and artificial intelligence pipelines, and spit out super rich, super deep and super consistent attribution about that product.
What we’ve seen for on-site search is some dramatic increases in relevance. One example of this is, again,“day party dress.” If you have a customer that’s looking for a “floral print day party dress,” for instance, and they type that into your search bar, maybe the only attributes you have for your entire batch of products, or your entire catalog of dresses is just that it’s floral print, or maybe that it’s just a dress. So what you’re doing is surfacing up a lot of results that your customer will have to sift through in order. If you have product attribution that says, we have 55 day party dresses that are floral print, and we surface those up at the top of our results, it may be a long tail search, but you’re putting the exact product that the customer is looking for right in front of them instantaneously.
The conversion rates happen to be much higher for these more relevant on site results. So you can imagine moving this same mechanism up the funnel. With our customer intent platform, you provide that data farther up the funnel to search providers like Google, and you get customers landing on your site from more relevant organic and paid search results with higher purchase intent for the product or search result that they clicked on.
Anything else you’d like to share?
One of the things that the Lily AI customer intent platform provides is our insights and products that are geared toward consumer intelligence. We have a whole suite of tools that will build out customer affinity profiles and provide recommendations and personalization out of the box, but when you pair it with the rich product data our product intelligence tools provide, it’s really a compelling offering.
Think about something like a cold start problem – a customer coming into your site as a guest, and you have no context or no profile on who they are, what they’re doing – for recommendations and personalization tools. If they’re coming in with that really rich product data from an organic search result click, Lily’s customer intent platform can start working with that data from the first page load and the first click on your site to start creating a truly dynamic profile.