Around the 1950s, psychologists William Edmund Hick and Ray Hyman propounded the Hick’s Law, which states that greater the choices the longer a person takes to reach a decision.1 More than half a century later, as customers get overwhelmed by too many choices, this could well be the guiding principle for businesses implementing conversational commerce.

Conversational commerce, a term2 coined recently, denotes the convergence of messaging apps and shopping. However, approaching conversational commerce merely as an integration of chatbots into sales process streams will lead to businesses leaving value on the table. This is why business leaders must define clearly what business problems conversational commerce could solve, looking beyond the obvious cost-saving benefits.

The ‘Why’ of Conversational Commerce

Studies3 show that new products have been failing at the stunning rate of 40 to 90 percent. In the U.S. Consumer Packaged Goods (CPG) industry, 70 to 90 percent of the products do not stay on the shelf for more than 12 months. Studies of online shopping behaviors4 show that the average cart abandonment rate is almost 70 percent. The product failure rates, in fact, have been consistent over decades. Online cart abandonment is also likely to persist as e-commerce throws endless choices at customers.

Businesses could seek to leverage conversational commerce to solve this fundamental problem, i.e., how do we contextualize, personalize and thereby simplify buying for customers amid the myriad choices available. In other words, implementing conversational commerce will make products appear as solutions for customers’ problems. A conversation will enable the following to happen:

  • Seller gets cues from the customer on the current mood, intent and spending ability

  • Customer understands features and benefits step-wise and is not bombarded with information

  • Seller understands the weightage of customers’ decision criteria and takes action accordingly

AI Is the Enabler, Albeit Work in Progress

That technology will need to power conversational commerce is stating the obvious. Analyzing a multitude of variables in the customer’s present and historical data, and matching them with products require computational abilities, which only machines can provide. Natural Language Understanding (NLU) is an integral part of conversational Artificial Intelligence (AI), which allows recognition of intent in customers’ speech or text. Intent is simply what the customer is trying to do or accomplish. Natural Language Processing (NLP) and Natural Language Generation (NLG) further help in responding to the intent in the same form of speech or text.

Machine Learning (ML) enables the system to keep updating its understanding of human behavior, language and connotation. These technologies are powering intelligent chat platforms as well as voice-enabled systems such as Alexa and Siri.

However, NLP and related technologies are still work in progress. A few years ago, Microsoft launched Tay, a teenage AI chatbot, to interact with millennials.5 However, as termed by the media,6 the chatbot’s language turned racist, genocidal and misogynistic. It’s imperative for businesses to understand that implementation of conversational AI must be deliberate and well-calibrated.

Making It Work

To be able to leverage conversational AI to improve sales performance, it is necessary to build a digital sales ecosystem. In such an ecosystem, AI not only drives conversations on its own, but enables sales specialists to focus on more value-added work. A conversational AI solution should leverage all the levers below to significantly improve both performance and customer experience during sales:

  1. Intent

    Does your AI solution…

    • Model customer behavior to discover intent?

    • Take customer intent into account while interacting?

    • Employ funnel analytics to understand customer intent and segment accordingly?

    • Leverage predictive analytics to recommend products and services to your customers?

    • Use ethnography and human-centered research methodologies to understand customer needs?


  2. Market

    Does your AI solution…

    • Allow variations in different markets based on market research and analytical models to get a view of the addressable market?

    • Segment your target market?

    • Have a process to identify new markets and long-tail niches?

    • Create buyer personas to rally the organization towards understanding their needs?

    • Have a direction on how to deal with different segments of the market?


  3. Presence

    Does your AI solution…

    • Engage customers on mobile or web?

    • Use different channels to proactively promote your brand, products and services?

    • Have an active play on social media?

    • Enable you to have an active and evolving Search Engine Optimization (SEO) strategy?


  4. Advantage

    Does your AI solution…

    • Have a clearly designed way of articulating value proposition for your products and services?

    • Differentiate your products and services from the competition?

    • Leverage differentiation and value proposition communicated clearly in the company messaging?

    • Map the customer needs and problems with the value proposition of products?

    • Use customers’ language to clearly articulate the benefits of your products and services?


  5. Customer Experience

    Does your AI solution…

    • Proactively extend assistance to the customers at all stages of the buying cycle?

    • Allow you to capture the ‘Voice of Customer,’ to drive improvement opportunities in customer experience?

    • Provide the right options for payment using historical data of each customer?


  6. Trust

    Does your AI solution…

    • Leverage and cultivate customer reviews around your products and brand to build trust?

    • Help the customer as a trusted advisor to solve their problems instead of being a salesperson?

    • Bring human support from your organization for critical discussions such as offering free returns, exchanges, money-back guarantees to dissatisfied customers?


Barry Schwartz, author of The Paradox of Choice: Why More Is Less, said, “Learning to choose well in a world of unlimited possibilities is harder still, perhaps too hard.”7 In the context of business, conversational AI can enable sales by making this choice, meaningful and simpler for buyers. This cannot be achieved with a simplistic investment in messaging platforms. To make conversational AI work, businesses will need to define the strategic objectives and carefully consider the critical levers.

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