Traditional, tree-based chatbots were too simple to replicate a human-like conversation between shopper and store assistant. With conversational AI, we finally have that capability.
When given the option, most of us prefer to talk to an assistant instead of manually searching for things. Every day, half of the US population uses voice search to find what they need without typing.
For those that don’t mind typing but enjoy convenience, conversational chatbots serve a similar purpose – no wonder that global retail spend on chatbots is estimated to grow from $12 billion in 2023 to $72 billion by 2028.
Not only is conversational shopping convenient, but it also represents the pinnacle of personalization, which consumers increasingly crave. 47% of Gen Z and 46% of Millennials want to receive personalized product recommendations, and 40% of both groups want personalized service in e-commerce.
What exactly is conversational shopping?
In the early days of ecommerce, all consumers could do was manually search for products with specific keywords or click through catalogs. A far cry from the real world shopping experience in which, when in doubt, you could always ask a store assistant for help.
Over time, online commerce evolved, producing more convenient customer experiences. From websites with catalogs and search engines, we moved to mobile apps, voice assistants, shopping through social media platforms, and now personalized, contextual conversations between the store and the shopper.
Conversational shopping is by far the most similar to real world shopping among different online experiences:
- It relies on conversational agents that interact with customers to provide personalized recommendations and answer questions.
- It transforms the customer experience and encourages customers to engage in online commerce more often.
- It creates a humanized ecosystem where customers can chat and transact, contributing to business profitability and improving customer satisfaction.
Online retailers can finally provide a virtual shopping assistant that has a personal touch, understands the customers’ needs, simplifies and enhances the shopping experience, and provides support before, during, and post-purchase.
Different flavors of conversational experiences in commerce include:
- Assisted shopping, helping either consumers or business clients find what they need, make shopping decisions, and find support within a store.
- Personal assistants, in the form of bots that find information, provide guidance and advice, and direct consumers or business clients towards stores if necessary.
- Internal assistants, meaning bots on the side of the seller, which streamline internal processes and help human workers achieve more and cover a larger group of customers in need of support.
And people clearly want to use these experiences. According to one study conducted in South Korea, the 5th largest ecommerce market, conversational tech usage is driven by:
- Usefulness, ease of use, trendiness, and informativeness when it comes to text-based chatbots.
- Convenience, interactivity, and ubiquity when it comes to voice-based chatbots.
The same study provides clues as to why some people might not be willing to engage with conversational bots:
- They’re not proficient enough in navigating digital experiences.
- They’re afraid of functional risk, i.e. the product not working as promised.
- It seems too intrusive in terms of data privacy and security.
It would seem that, in order for customers to adopt conversational experiences, they need to be advanced under the hood but very easy to use and trustworthy from the customers’ perspective. This combination is necessary to both enhance customer experience and improve business outcomes.
How is conversational shopping changing commerce?
As adoption of conversational shopping spreads, among retailers as well as search engines, brands that want to stay top of mind with customers will need to adapt:
- Rich data is a necessity, bare-bones descriptions without much media won’t do. Images, animations, videos need to come into play, accompanied by detailed information that will help conversational bots choose your offer among competitors, and create adequate personalized offers within your store.
- A unified customer experience is necessary, especially for the younger generations, who want to feel cared for across every customer touchpoint.
- Customer engagement and retention strategies become more important than ever.
- Brands need new ways to differentiate themselves from competitors across different channels where their customers can be found.
Benefits of conversational shopping
For managers that are thinking about integrating conversational capabilities into their platform, there’s a lot to be gained (though not without challenges, which we’ll cover next):
- Improved customer experience – thanks to AI learning from data and predicting customer needs, the shopping experience is more personalized and contextualized.
- Higher customer retention – customers are more likely to return to a store with a conversational experience that helps them make better buying decisions.
- Higher conversion rates – by engaging with customers at the right touchpoints, conversational bots can help them finalize their purchases.
- Better customer engagement – conversational experiences keep customers engaged, as opposed to more static experiences like traditional search.
- Greater efficiency – AI doesn’t get tired and is available 24/7, helping human workers handle more customer inquiries and reducing wait times in customer service.
- Improved data analysis – data from customer interactions with conversational AI can be analyzed to extract valuable insights.
Challenges of conversational shopping
Conversational shopping isn’t a plug & play kind of solution, and there are some key considerations to take into account prior to implementation:
- Data privacy concerns – in a time when many people are worried about the privacy of their data online, it’s important to ensure that they can trust the AI if they’re going to use it.
- High implementation costs – implementing a conversational experience comes with a high upfront cost, so it’s important to get a good estimate of how much ROI it could produce to decide if it’s worth it.
- Technical issues – the technology stack and process for building AI products is quite complex, it’s easy to overshoot by investing in something that’s too complex for the use case and could cause technical issues down the line.
How to maximize ROI from conversational shopping implementation
Taking into account the challenges that need to be conquered, managers need a strategy to ensure a swift and profitable implementation (or, in some cases, decide not to implement):
- Analyze whether a conversational experience would fit your scale and your customers’ demands, and determine whether it would work with your broader growth strategy.
- Prepare a detailed analysis of the customer journey and user experience on your platform to see where a conversational bot could have the most impact.
- Explore the details of how AI works and what are the necessary ingredients to make it a beneficial addition to your business.
- Take a good look at your data strategy, i.e. how you gather, store, and analyze data about customer behavior, your operations, inventory and so on.
- Don’t go all out, try a pilot program first and make sure you leverage a methodology like product design sprints to get the most out of this process.
- Expand your SEO strategy to include optimization for voice search and AI search.
Conversational Shopping Examples
Voice assistants like Alexa and Siri
These don’t need an introduction, they’ve been around for a while. But with the proliferation of generative AI, they’re poised to get an upgrade that will make them much better at helping you.
Mercari’s Merchat AI shopping assistant
The virtual thrift shop Mercari released a ChatGPT-powered bot that helps customers quickly find what they need. If not sure, it responds with clarifying questions first, and then finds the most accurate recommendations based on what you requested.
Walmart’s Text To Shop
Text To Shop leverages text messages as a quick and easy channel for customers to find what they need and order it, or re-order things that they buy often, schedule delivery or pickup, and do the checkout process.
Zalando’s fashion assistant
Zalando experimented with a ChatGPT-powered fashion assistant to help customers find adequate additions to their wardrobe without having to spend hours browsing their vast catalog
SimplyCodes GPT assistant
SimplyCodes enriched their discount and promo-code finding application with a GPT-powered assistant that helps customers find the best offers from over 300,000 US retailers.
Is Conversational Shopping Worth It?
As always, it depends. For a niche retailer with a well-informed base of customers who don’t need anybody to help them shop – not really. But a large store where customers often abandon carts and churn because they can’t decide what to buy? That’s a perfect fit for conversational AI.
If you’re not sure if this solution is right for your business, reach out to our retail department for a consultation.