How to craft the best omnichannel retail experiences for out of this world results
With 73% of shoppers researching and buying products across multiple channels, retailers must deliver omnichannel CX. Here's how to get it right.
There’s a paradigm shift in how customers shop online. Twenty years ago, consumers searched using keyword queries that resulted only in exactly matched words or phrases, including misspellings. Today, they can express intent or engage in any mode—text, image, QR codes, audio—and in any form, like long sentences in text or audio, blurry images without context, and typos.
With multimodal search, consumers can ask open-ended questions like “What do I need for a hiking trip?” and receive relevant responses.Even where consumers go to shop online has changed, with many beginning their searches on social media sites instead of e-commerce platforms. Modern customers expect premium services that emulate in-store experiences, and those expectations will continue to evolve.
The most pressing question is, what’s enabled search to become multimodal? There are two main reasons: the existence of documented use cases and technological advancements.
What is multimodal search?
Multimodal search is a sophisticated technology that applies artificial intelligence (AI) and machine learning (ML) to understand and interpret multiple input modes—such as voice, images, and standard text—to generate more accurate search results.
Users can take a picture of something and use that image to search for similar items or even speak in a different language and receive relevant results. The more users input queries, the more personalized responses can be.
For example, when searching for “milk” over time, AI will recognize that the user frequently adds the same type of milk to their cart and recommend those results first.
Voice and image input benefits all users, especially those with different preferences or accessibility challenges, providing accurate results and an overall better customer journey.
Earlier this year, Amazon announced the release of Rufus, a digital shopping assistant designed to complement the retailer’s multimodal capabilities.
Rufus seamlessly processes text and voice inputs in a conversational manner to navigate Amazon’s catalog and community. It provides tailored recommendations, answers questions, tracks orders, offers product comparisons, and delivers highly personalized solutions across various inquiry formats.
Multimodal benefits for e-commerce business
With multimodal search, organizations can expect increased sales, improved efficiency, and lower costs. Many of these services are automated, leading to better cost structures and operational efficiencies.
Implementing additional search types provides more data points, providing deeper insights into customer shopping patterns, personalization, and contextualization. AI enables employees to switch their focus from search data analysis to other tasks by automating repeatable tasks.
Voice and conversational search use has transformed customer care. Customer service associates can use the multimodal search features to quickly locate and accurately describe products to assist customers in person, on the phone, or online.
Multimodal search allows e-commerce companies to stay on top of the competition. Search startups are developing multimodal search options using large language models so consumers can shop in different modals (e.g., product images). One multimodal search provider saw positive results immediately, with a conversion rate five times higher than the standard e-commerce rate, demonstrating that return users activate even more searches than on their first visit.
How to avoid potential pitfalls
Missteps are common when integrating any new technology. Beware of looking only at generalized multimodal benefits without clarifying the problem. Don’t release a solution without first understanding the input types particular customers want or determining the return on investment.
Lack of diligence can also lead to legal or PR backlashes. Without clear measures and guidelines for employees using search tech, companies risk release of proprietary information. Adherence to data protection regulations like GDPR is essential.
To boost the chances of success in implementing multimodal search, e-commerce companies can follow these steps:
- Identify the problem to be solved, determine which modality offers the most benefit, and research solutions that fit business needs, considering bias mitigation and cost.
- Once the implementation process is established, provide employee training to ensure safe use.
- Craft disclaimers that notify users of AI generation and promote transparency, regular internal audits, and a human checks-and-balances system to test model output and catch issues early.
- Success metrics include assessing customer net promoter score (NPS), relevant evaluations to gauge model quality, and monitoring customer adoption of new modality changes.
The future of multimodal search
The transition from brick-and-mortar stores to online shopping accelerates yearly, and as the great multimodal race expands, consumer shopping methods will continue to evolve. Search methods will advance and become faster, more efficient, and ultra-personalized.
As researchers and developers create more efficient and secure multimodal systems by leveraging new and emerging technologies, the applications will continue to grow. Healthcare, supply chain optimization, and cybersecurity are potential use cases.
Customer expectations will increase with technological advancements, requiring organizations to stay knowledgeable and flexible regarding their customers’ wants and needs. Companies that don’t enable multimodal experiences to enhance human-tech interaction risk falling behind in the hypercompetitive retail e-commerce space.
