The rise of composable technologies, post-pandemic ecommerce growth, and an uncertain economic climate have significantly shifted digital commerce technology investments. Companies are turning away from long-term backend investments and toward frontend facing technologies that can prove faster time to value and measurable return on investment.
Ecommerce product search and discovery solutions do just that. Elevating both the shopping experience and bottom line metrics, it’s no wonder that they’re at the top of the list for savvy leaders in digital commerce.
From Ecommerce Product Search to Product Discovery
Offering solutions to improve the digital shopping experience, the vendor landscape has evolved rapidly during the hype of the pandemic years.
Digital commerce leaders often voice frustration about the confusion this causes in the evaluation process. It’s hard to keep up with the never-ending merry-go-round of mergers, acquisitions, and portfolio extensions for point solutions like ecommerce product search engines as the market coalesces around product discovery.
They’re the experts in digital commerce, after all, but the complexity of these turbulent times just doesn’t leave time to keep track of vendor technology market movements. And very understandably so.
In conversations, I always recommend turning to the industry analyst firms for advice because they focus their time and expertise on identifying the best fit for a given need and use case. As one such industry firm, last year Gartner called out the shift of commerce search into a wider shopper journey scope: “The focus of digital commerce search has shifted toward the wider needs of product discovery.” (1)
- Gartner Market Guide for Digital Commerce Search, 10 August 2021
How Do I Choose the Right Ecommerce Product Search Solution?
Choosing the right product discovery solution for your team depends on your unique business needs, timelines, technology principles, and (of course) budget. Here are some critical considerations driven from the learnings of selecting modern, composable solutions.
1. Invest in future-fit technology.
Ecommerce product search is not a new discipline, and many offerings are decades old. That means they may not be built on modern, cloud-native technology principles. What may appear like ‘stability’ in the marketing brochure quickly turns into an anchor dragging down your customer experience, agility, performance, and availability.
Look to vendors who offer a fresh technology foundation, using Microservices, API-First, Cloud-Native SaaS, and Headless principles. That ensures agility, availability, scale and the ability to leverage omnichannel search experiences. MACH-certified vendors who are members of the MACH Alliance are a great place to start.
2. Mind the core
For AI that powers a product discovery portfolio, it is critical to pay attention to what drives the underlying learnings. This is where vendor offerings differ dramatically. Some use outdated search cores like SOLR, where others are built on Elasticsearch cores.
In both cases, the roots are in full text search functions and focus on keywords, meaning that search results are not optimized for ecommerce use cases.
More modern, commerce-centric cores employ advanced machine learning models that focus on customer behavior and clickstream-first, vector-based algorithms. This allows them to optimize for a range of ecommerce-specific metrics, rather than content conversion only.
Ask vendors about the foundation of the search and discovery core and invest in technologies that are purpose-built for commerce use cases. Ensure the technology is proven to work for your future initiatives, like voice search or guided selling. Make time to consult with industry analysts for advice over getting a degree in data science.
3. Look for homogenous offerings
The success of the product discovery category has led to more M&As in the past years, in which large legacy vendors supplement their offerings with small niche solutions. Often, these technologies are not yet fully integrated, meaning that they introduce silos, breaking points, and missed opportunities for learnings across different moments in the product discovery journey.
Before you know it, you play software vendor by trying to plug those holes with your own development team.
Carefully investigate if your ecommerce product search and discovery vendor has integrated algorithms and products beyond the brochure. Run a proof of concept to validate cross-portfolio learnings leveraged in a common core to avoid the API camouflage trap of underlying silos.
4. Verify that customer success excellence drives value
Search algorithms, semantic models, NLP, and AI/ML are complex fields of data science. Many vendors will throw hyped terms at you and expect you to trust that their offering will work for your needs. Flamboyant case studies with impressive stats follow. Your carefully crafted RFP checklist quickly becomes a full bull5|-|1t bingo sheet.
Ask how a dedicated Customer Success team will support tuning the core AI/ML engine on your site or app and verify your live clickstream during onboarding. Every product catalog is different, as is every use case and every traffic pattern. Getting the most out of your product discovery partner means tuning the underlying core in your ecosystem and against your primary goals. If a vendor tries to convince you otherwise, treat it as a big red flag.
Prove These Critical Considerations with a Live Value Assessment
The most important aspect of making sure that your next ecommerce product search and discovery solution fits your needs is to run a live assessment as part of the evaluation process.
Over the last decade, search technology has changed fundamentally. AI/ML-powered cores have gained mainstream adoption and cloud-native and API-first principles are becoming the de facto standard for scalability and data connectivity. Tools that were great 10 years ago may just not be up to standard anymore.
On one hand, marketing materials tout all the hyped buzzwords—but data science has evolved so far that evaluating the core search engines meaningfully requires a post-grad degree in Artificial Intelligence.
Understandably, technology buyers have time for neither.
Factors that might come into play are the volume and nature of your traffic, the differences across regions and channels, the nature of your product catalog (including the quality of attributes and metadata), and the breadth and depth of data integrations with adjacent customer intelligence.
And importantly, what does success look like for your ecommerce goals? Beyond conversion rates, do you care about revenue per visitor (RPV), average order value (AOV), margins, or maybe cost reduction in delivery or inventory velocity? Different tools optimize for different metrics and the best tools are flexible to meet multiple goals and retrain to changing ones. But to know if they’re the best, you have to test drive them in your conditions.
And your commerce search and product discovery partner should be able to offer that opportunity.
Schedule your Constructor Proof Schedule now with your customers and your data, and find out why our customers consistently see +3X ROI. You shouldn’t have to settle for less.