Conversational Commerce: Why You Should Consider a Native eCommerce Chatbot
Despite the buzz around AI’s potential to make ecommerce as personal and natural as an in-store visit, fully-baked intelligent shopping assistance remains several years away.
In the interim, online retailers are embracing chatbots as a first toe-dip into conversational commerce using them to expedite customer service and provide novel ways to discover products.
According to LivePerson’s analysis of over 20 years of live chat logs, 70% of ecommerce chat inquiries can easily be handled by automation. While typical live chat is offline after business hours, chatbots are available 24/7 and reply instantly, unlike human agents who may throttle several chat threads at a time.
Today, with many call centers closed or running minimal hours due to the coronavirus crisis, live help chatbots are even more valuable to ensure customers are cared for as quickly as possible.
Online shoppers value speed-to-resolution
Fifty-six percent of online shoppers say they prefer to resolve issues through messaging apps than to email or call customer service, and 40% say they don’t care whether a chatbot or a person answers their customer service questions. Fifty-six percent of online shoppers say they prefer to resolve issues through messaging apps than call customer service.
Chatbots don’t have to have all the answers. Pre-built dialogs handle routine inquiries, with the ability to hand off to a live person or help desk when a bot is stumped. Chatbots can also collect names, email, order numbers, account preferences and marketing opt-ins and pass this data back to different databases and tools.
How online retailers are leveraging chatbots
Beyond customer service, messenger apps like Facebook and KiK are used by a number of retailers including Lego, Sephora, ASOS, Marks and Spencer and H&M. With 67% of consumers open to shopping through a chatbot, these interactive shopping tools provide a novel way to explore online catalogs, engage with content and get personalized recommendations in a conversational context.
Messenger chatbots offer an alternative to the traditional “search-and-browse” ecommerce experience. Most support voice (through speech-to-text) and image recognition via a mobile device’s camera, such as ASOS’ Enki assistant (below). Advanced messenger chatbots support natural language processing (NLP) to infer intent and make intelligent recommendations.
Such chatbots are typically deployed with “builder” platforms like Chatfuel or ManyChat, with varying features and capabilities. Some chatbot builders support in-chat payments through Stripe integration. Others integrate with popular email and review platforms, store locators, CRM and help desks through tools like Zapier. (Check out our review of 14 chatbot solutions for ecommerce).
For remarketing, Facebook Messenger bots support automated sequencing campaigns to send promotional offers, personalized alerts and cart recovery messages through Facebook Ads. User “tagging” can be used for analytics, segmentation and remarketing strategies.
Messenger chatbots versus native assistants
American Eagle Outfitters is another retailer embracing messenger shop-bots. Through AEO’s Gift Bot, KiK and Facebook Messenger users can take a short quiz to discover gift suggestions served by the bot. Shoppers can also upload photos of clothing and shoes that fit their style and explore visually similar items.
While both American Eagle Outfitters and its sister brand Aerie are accessible through one website and a unified cart, AEO chose to create different bots for each brand, individually deployed to both KiK and Facebook Messenger platforms.
“(We) wanted to have separate chatbot experiences for each brand,” says AEO’s Chief Technology Officer, Colin Bodell, “because not every customer shops both brands or would find information from both retailers relevant.”
While this strategy keeps bots tightly focused, it also creates siloed experiences within each brand and platform. In addition, the shopping experience is restricted to what’s possible within a chat dialog. Category browse, site search, product content, reviews and shopping carts are inaccessible until a user clicks out of the messenger app and into the e-store (where all chat context is lost).
Why build a native chatbot?
While messenger chatbots are popular and integrate with platforms your customers are already using, you’re limited to the features and functionality provided by the messenger platform (which are all equally available to your competitors). Your chatbot strategy may call for support for key use cases, such as:
Keeping conversational commerce in context
Integrated with and accessible from your online store, native chatbots preserve context while keeping your website’s rich features at shoppers’ fingertips. For American Eagle Outfitters, a native bot would allow shoppers to explore both brands as seamlessly as they can on the website, or scope to their preferred brand or department. All it takes is asking bot users their preferences early in the dialog.
Unlike platform-specific bots, site-native chatbots are accessible to all site visitors and are more easily discovered through the website than chatbot directories within each messenger app.
Offering enriched capabilities and personalization
Native chatbots also enable you to integrate commerce features beyond what messenger bots support. For example, APIs can pull data from personalization and promotions engines, customer accounts, loyalty programs and order management. You can also build chat experiences that support bundles and carts, connect with a wishlist or support mobile wallets and one-touch payments.
Predictive analytics can be used to trigger proactive chat based on browsing behaviors such as slow scrolling or pauses on a product list page, excessive “pogo-sticking” navigation between pages or frequent repeat visits without a conversion. Proactive chat may also trigger from certain cart conditions like “over X items in cart,” or “over $X in cart.”
Supporting omnichannel and endless-aisle
APIs can sync chatbots with microservices such as inventory management for real-time endless-aisle. Instead of struggling through a mobile website’s search and category menus, an in-store shopper could scan a barcode or upload a picture of any product to locate additional sizes, colorways or quantities through chat. Out-of-store shoppers can use the same feature to reserve-in-store. GPS and maps can “send location” and provide turn-by-turn directions to local stores.
Protecting sensitive information and supporting secure in-chat transactions
To facilitate transactions and handle customer support inquiries with sensitive data such as passwords, chatbots require encryption and other privacy controls to comply with PCI, GDPR and PIPEDA.
While some third-party chatbot builder platforms such as Ada meet PCI DSS standards, you’re limited to the payment processor(s) supported by the platform (namely Stripe). API-driven, headless commerce and microservices give you the flexibility to natively support in-chat payments that work seamlessly with your existing digital platform and processors.
Taking conversational commerce beyond the bot
APIs and microservices extend conversational commerce beyond chatbots to the Internet of Things, in-store digital installations, smart speakers, wearable devices and more. These new “heads” can access any commerce service required, with business logic tuned to what makes most sense for every device -- even those which are fully voice-enabled and lack a screen or graphic user interface.
How headless commerce supports native chatbots
Consider your chatbot as a new touchpoint with its own business logic and interface. You want to pull data from certain commerce services (such as catalog, personalization engines, search, accounts, site content like FAQs and Shipping policies, order management, pricing, promotions or even cart and checkout) -- but use this data in a way that suits the conversational format.
If your ecommerce platform is headless, your chatbot can connect to any or all of these services through a robust API, with business logic configured and tailored to the chatbot within the API layer without changing code in the commerce platform itself. This ensures that adding a chatbot won’t interfere with your online store experience and won’t require complicated development.
If you’re using headless commerce and microservices architecture, the services within your ecommerce platform are independent of each other. This adds an additional benefit, as you can compose a chatbot experience even quicker, connecting to only the services you want (versus the entire back end if you’re using a monolithic headless platform).
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