Commerce Driving Chat Bots to Say It Better

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

Commerce Driving Chat Bots to Say It Better

Bots are where the web was in 1994. The arena is still wide open, and we don’t know what’s going to work and what’s not, or areas where the overhype is most prevalent. The rise of the chat bots domain is still filled with unknowns, but there’s a tremendous amount of money to be invested and made in this industry, along with big wins and big losses, especially during this training-wheels period.

The above were thoughts given by Robert Stephens, co-founder of Assi.st and creator of GeekSquad, during VentureBeat’s MobileBeat2016 conference on July 13. In a conversation titled “Achieving the “Commerce” in Conversational Commerce”, facilitated by Editor-in-Chief Blaise Zerega, Robert and a cohort of other leaders in their fields chatted about how commerce will likely help move the era of the chat bots forward into a profitable and chat-worthy future.

Uber’s Chris Messina is credited with coining the term “conversational commerce”, an apt way to describe the increasing interactions between consumers with business-driven chatbots or chat apps. In April of this year, Facebook unleashed support for messenger chat bots, which as of June 30 numbered 11,000 (with 23,000 other user accounts created for the Bot Engine tool, we can only imagine how many more bots are on their way).

Just to name a few in the commerce space – CNN, 1-800-Flowers, Bank of America, Expedia – and the list of retail- and service-inspired chat bots continues. According to Business Insider Research, users for messaging apps have surpassed social networks, and the trend doesn’t look to be slowing anytime in the near future. 

(Note: Readers with a strong interest in conversational interfaces for business should see our full, in-depth article titled: “7 Examples of Chatbot Use-Cases That Actually Work“)

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At present, says Stephens, bots can be easily started with structured messages for specific and honed use cases, particularly in customer service, where similar situations have been well-documented, the common problems/solutions and questions/answers are known, and the appropriate choice of language (still robotic sounding) is easy enough to implement. These customer service efforts will continued to be scaled, said Robert, and the redundant issues that crop up will need to be handled before commerce can really start to revolutionize the customer experience and glean maximum value.

Starbucks is just one example of a company using a mobile app and simple chat bot that allows you to place and pay ahead of time for your Starbucks order, which (is supposed) to be ready and waiting by the time you arrive. They still seem to be ironing out the kinks in the delivery model (mobile orders are supposed to get priority, but in-person interactions with other customers can cause delays), but the idea is meant to make more efficient use of the consumer’s time and bring in more purchases.

But what about bots that need to be a little more ‘chatty’, that you need to strike up a conversation with in order to accomplish a goal with more moving variables? This might be anything, from planning a trip to receiving treatment suggestions for an ailment. Herein lies the sweet spot for companies looking to get into the mix early; future advancements are wide open, which is why it’s such an exciting place to be, said Facebook’s Product Manager Seth Rosenberg.

Taking Chat Bots to the Next Level

The ultimate goal of enhanced customer service involves the bot having access to data in your account and fulfilling your intent quickly, based on your previous experiences, a contextual ‘understanding’ of your current message and intent, and with the ability to handle and quickly adjust according to your needs and desires. The idea almost sounds too good to be true, but this is exactly the direction in which forward-thinking businesses and software developers are trying to move.

We’re still in the training phase, and much of that involves humans (by example) training the ‘bots’ i.e. providing information to the algorithms behind them. Rosenberg notes that in order for the chat bot trend to really take hold, consumers need to see glimpses into a “delightful experience”, even if a human is behind the chat initially. This is exactly the strategy that companies such as Facebook and x.ai are using i.e. incorporating virtual human assistants behind their messengers, getting people used to talking with bots and also training the bots at the same time.

If chat bots are going to become status quo, then information on what works and what doesn’t will also need to be shared and collaborated on between companies. Fortunately, companies like Google are helping software developers increase the “IQ” of bots by releasing some of their algorithms used to create bots with a better grasp of syntax and grammar.

What really peaks Robert’s and Seth’s interests are the number of sales made through chat and the number of chats where a sale was lost. Successful business strategies need to be measured, and one way of assessing the results is by measuring the chat bot experience itself.

Robert states that finding ways to measure friction (he defines friction as effort i.e. the number of steps needed + time + cost and price) is step one in the journey to make chat bots a smoothly integrated part of business.

“Desingineer” Kevin Scott writes a great article on Medium about applying Jakob Nielsen’s 10 usability heuristics (for evaluating user interfaces) to chat bots, and seeing how a few popular choices hold up to the still-used standards. Evolving existing standards for more antiquated interfaces into the chat bot domain will likely help lead to the creation of new best practices and measurements, and companies are already trying to get an early start in jumping on this bandwagon.   

Kochava call itself the “first company to support measurement for chatbots at scale”, already supporting “hundreds” of brands (Microsoft, Yahoo, and Pepsi are just a few of the logos highlighted on their homepage). “As we explore how bots can interact with consumers, and brands begin to create chat-optimized ad campaigns at scale, Kochava is ready with a universal platform to track and measure interaction across multiple channels and devices, giving brands a truly comprehensive view of their customer’s engagement,” said Charles Manning, Kochava CEO, in an April press release.

They sell it well too, asking businesses if they’re ready to take the “1% challenge” (save 1% on your ad budget is the goal) and illustrating an example breakdown of measurements, like number of click based installs and cost per install (CPI), and a resulting ROI of $28,014.

Msg.ai is a company selling “artificial intelligence for conversational commerce” i.e. accessible and customizable chatbots, along with a platform for monitoring and encouraging engagement with chatbots. Businesses can implement A/B (multivariate) to test tone and engagement with rich media (videos, coupons, GIFs, etc.), another feature that is and will likely be heavily integrated with successful chatbots in particular industries. On the flipside, there are also companies already offering a mobile customer engagement platform but are looking to introduce chatbots into their services, such as Mobify.

So while it’s a little early to release autonomous chat bots to consumers, the fanned flame of the chat bot looks to be catching fire with smart businesses, who recognize the changing behavior of consumers and see the opportunity to reduce marketing and customer service costs and increase ROI. In the meantime, these motivations will continue to drive innovation in conversational commerce (of which chat bots are a major part), opening gateways for conversation between consumers and brands/companies, where previously none existed before.

 

Image credit: Engadget

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