AI and machine learning tools are increasingly essential parts of the ecommerce tech stack. But many online brands and retailers may not feel equipped to effectively mobilize AI tools to meet demand and stand out in a cluttered marketplace.
Common misconceptions about intelligent technology can prevent ecommerce teams from successfully integrating AI tools into their operations. Ecommerce companies often hesitate to use AI tools because they are concerned that AI will require them to give up control, cost too much time and money to implement, and offer unclear ROI.
Let’s unpack these misunderstandings about ecommerce AI and clarify the benefits it offers brands and retailers in the digital marketplace. Here’s the truth instead.
Myth #1: AI tools require retailers to give up control
When many people think about AI, the first thing they think of is automation — human operators sitting back while a machine takes control. If that’s the image you have of AI, then any hesitation at all to hand a robot the keys makes complete sense.
Intelligent systems featuring machine learning and natural language processing (NLP) are designed to supplement and streamline human-led decision making. Data-powered tools that prioritize transparency ensure that leaders and teams can still have control and visibility into what the AI is doing across a range of ecommerce use cases.
For example, an AI product discovery tool can analyze individual and collective query data to show merchandisers where and how they can most effectively take action. Teams can use these data-driven insights to set new merchandising rules, like boosting high-inventory products in search rankings or identifying trending products to highlight in custom collections pages.
Or think of the work required to manually redirect common synonyms or variations of the search query “galoshes” to the correct results page. Handling menial and repetitive work like correcting typos, identifying synonyms, and creating redirecting rules, the AI can still return the right products, thus freeing up time for merchandising teams to contribute more strategically to business outcomes.
AI’s deep learning capabilities also help sellers dial in on the personalized product recommendations and interactions that stand out to online shoppers. AI-driven product discovery can customize individualized search results in real time. Using customer clickstream data from a shopper’s search and browsing activity, AI re-ranks results to prioritize products best aligned with their specific needs, interests, and brand preferences. This creates a better user experience, and that better experience is critical to driving top-line revenue.
For example, a customer who loves Oatly’s dairy-free chocolate ice cream might see Oatly brand products at the top of their search for non-dairy milk. Achieving the one-to-one personalization that today’s consumers expect — and that is most likely to get them to convert — is only possible with the data processing power of AI.
Myth #2: Integrating a new AI tool is hard and expensive
In the ecommerce industry, the stakes of adopting a new tool are high. Merchandising teams are already pushed to their limits keeping up with customer and organizational demands. For organizations facing tight margins, monolithic “one-size-fits-all” tech solutions that are difficult to integrate and maintain are a liability, not an asset.
Leaders are right to be wary of solutions that front-load with promises but are hands off when it comes to getting you results. A secret about the best of today’s AI-driven tech? Success isn’t determined by the software alone.
The best AI solutions will also include the hands-on customer support your enterprise needs to implement the tech effectively. This includes implementation, communicating time to be up and running, when you should start to see results, and where to go if challenges arise. It also means ensuring that your team knows how to use the platform in their workflow: navigating the dashboard, using analytics to find and track merchandising data, and automating tasks previously done manually.
Surveys show that ecommerce leaders largely underestimate the workload and pressures merchandising teams are facing. Merchandisers themselves, though, are enthusiastic about the possibility of using new AI tools to manage their growing list of cross-functional tasks.
Lifestyle brand Life is Good saved its merchandising team hours of weekly manual work by integrating Cognitive Embeddings Search into their product discovery work. Thanks to machine learning, the team was able to offload the bulk of the manual tasks involved in maintaining their product discovery experience.
With Constructor’s support, the team successfully integrated the new platform into their workflow without sacrificing time or investments in other operations. When partners are transparent about the implementation timeline and process, ecommerce teams are better prepared to use intelligent tools to actually make their jobs — and lives — easier.
Myth #3: There are no clear ways to measure the impact of AI on KPIs
AI platforms are becoming an increasingly common component of business tech stacks. In fact, the number of companies using them has doubled in the past five years, not just in merchandising but also in other areas like customer service and inventory management.
But organizations consistently struggle to quantify the concrete impact that these tools have on their business goals. Less than half of companies who’d integrated a basic level of AI-driven technology had confidence in their ability to assess its ROI, reported a 2022 PwC survey.
It makes sense that companies don’t feel confident measuring the impact of AI. Tools that aren’t designed with transparency in mind operate as black boxes. Companies are expected to blindly trust that the algorithm understands their business’ individual needs and goals. In reality, many generic “search” products aren’t actually built for ecommerce at all, let alone to optimize for ecommerce metrics.
That’s why it’s important for AI to enable companies to see how it supports their specific KPIs. At Constructor, we do this in several ways:
- Before taking on any new software, sellers have the chance to see the potential ROI quantified in a pre-purchase value assessment. Constructor analyzes the current state of a company’s site, using data from the product catalog to predict the impact that implementing AI would have on critical metrics like average order value and revenue per visitor.
- After Constructor is integrated, we A/B test against the legacy solution to prove that we achieve the predicted lift to the KPIs that matter.
- We allow companies to tell us what KPIs matter most to them, and then we work with them to optimize for those metrics.
- Constructor’s deep commerce core is continually evaluating how items are slotted, boosted, and buried. If a manual boost is inadvertently hurting your metrics, the system can alert your team so that they can make a change.
Constructor’s solutions are designed to empower brands and retailers to optimize for the KPIs most critical to their ecommerce business. Sellers can easily track a variety of metrics and adjust their priorities based on how their goals change at a given time. For example, at the end of a season, an apparel brand might optimize for sell-through rate to balance inventory, while a grocer may instead focus on boosting RPV.
The truth? Ecommerce AI is the future
For many years, the conversation around the potential of AI has seemed like science fiction — dominated by hypotheticals, idealism, and some suspicion. Today, AI’s value to ecommerce businesses is no fantasy. Companies can now achieve their business goals, maximize their operational efficiency, and provide exceptional customer experiences. And that’s exactly what it will take to stay competitive in the online shopping landscape.
The future of ecommerce AI is here… but don’t take our word for it. Make us quantify the results you can expect to see from Constructor product discovery with the proof schedule, a live value assessment on your website using your data.