From their interactions as consumers, B2B buyers and customers are already accustomed to receiving relevant information curated by algorithms and delivered at the right time in dynamic and digestible formats. Across channels, devices, and interfaces, these audiences ask for what they want. They’re also aware of how their behavior drives the algorithmic co-creation of interest-based content experiences that are forever #ForYou.

Expectations for immediacy and value will continue to be amplified with demographic and technographic shifts in the workforce. These trends in buying behavior are forcing the next evolution of strategies, tactics, and technologies designed to influence, guide, and enable these empowered B2B audiences. As a result, businesses and their customers alike are leveraging chatbots and virtual assistants (VAs) to facilitate dialogue, access information, and complete tasks.

The increasing adoption of conversation automation technologies for B2B marketing and sales use cases is evident in the presence of seemingly ubiquitous chatbots and, for marketers, in the volume of conversations executed. Conversation automation is a natural evolution of the logic-based, dynamically personalized experiences and interaction sequences first made possible through the introduction of marketing automation platforms with their modular content repositories and rules-based assembly. But as we learned back then, more is not always better.

Three B2B Conversation Design Flaws Deliver Dystopian Dialogue At Scale

It’s a brave new world for marketers, where machines can decide when and how to engage our audiences, presumably on our behalf. The speed with which we can now initiate and facilitate dialogue combined with the ability to augment the workforce in relentless pursuit of a single objective is a recipe for dystopia, if we’re not careful. Conversations overly focused on internal objectives (more tactic conversions, more “leads” passed to sales) instead of audience objectives are doomed from the start. Three motifs of dystopian dialogue in B2B are:

  • Poor design and user experience amplified by automation. Common conversational user experience pitfalls include dead-end dialogue with chatbots, overpromising in words and underdelivering in action, forms disguised as conversations, and obnoxious outreach cadences from virtual assistants overtrained on appointment setting.
  • Conversational tactics and triggers not tuned to buyer and customer signals. Neither the user’s intent statement nor the seller’s intention for a tactic are the same as purchase intent. Intent confusion inside a conversation is often a result of not listening for, not accurately interpreting, or not responding meaningfully to the signals indicating how and when to engage.
  • Conversations without context. B2B conversations are often treated transactionally, but they’re much more complex than that, with more people involved in the decision process, longer purchase time frames, and more total interactions in the journey. Conversations siloed from interaction histories, planned and in-flight tactics, and longer-term nurture efforts create a disconnect that propagates through the tactic mix. The “next best” will always be a fuzzy match at most.

A Pragmatic Approach To Better Automated Conversations With B2B Audiences

Marketers must think holistically about the contextual design, deployment, and fine-tuning of automated conversations in an audience-centric and customer-obsessed way. Impactful conversation design requires a mindset shift from the dominant focus of helping sellers sell to enabling buyers and customers to get the information they need to progress their journey from any stage. B2B organizations should plan for three types of conversations designed to: 1) enable and influence; 2) transact and sell; and 3) support and serve.

The ability to deliver immediate value through dialogue starts with a solid foundation in how conversations are conducted, then a pragmatic approach focused on how context affects meaning and is then applied to the B2B customer lifecycle. This highlights three governing principles for B2B conversation design: cooperation, inclusion, and social context.

  • Use the cooperative principle in B2B conversation design. Introduced by philosopher of language Paul Grice, the cooperative principle of effective conversational communication assumes that participants are truthful, informative, relevant, and clear in pursuit of a mutual goal for conversation. These four maxims should be leveraged to guide the design and automation of conversational interactions.Cooperative Principle in B2B Conversation Design
  • Recognize language and its delivery as a social construct. Language is an expression of identity for all dialogue participants, governed by the conventions and norms that drive connection and meaning in conversational settings. Embrace and extend diversity and inclusion commitments by creating nonhuman agents that are culturally aware and on brand. Dialogue system utterances, personas of chatbots and VAs, and intent phrases used to train conversational AI should be inclusive and free of bias.
  • The buying group provides social context for B2B conversations. Industry, job role, and buying group composition all contribute to the specialized language that B2B audiences use to evaluate, purchase, implement, and utilize solutions to their business needs. Contextual conversation design interprets and speaks the language of the group with its collection of personas and roles working together to access, share, and synthesize information throughout the decision-making process.

Join us at this year’s B2B Summit North America to learn more about the current state of conversation automation in B2B, the role of AI, and best practices for the design and orchestration of conversational interactions that engage and enable B2B audiences. Be sure to add the session, Brave New World: From Dystopia To Delight In Automated Conversations, to your agenda. See you in Austin!