Let’s Chat About Conversation Automation Technology In B2B Marketing
Today’s B2B buyers and customers expect information gathering and interactions with a brand to happen on their terms, to be relevant to their needs, and to be responsive to their situation. The modern B2B buying journey and customer experience is more self-guided and digital than ever before.
Forrester’s 2021 B2B Buying Study found that the top three self-guided interactions that B2B buyers feel are meaningful or impactful are: 1) exploring a provider website (37%), 2) searching the internet (33%), 3) and exploring industry or business association websites (31%). Twenty-two percent of B2B buyers also find an online chat feature on provider websites to be meaningful or impactful.
In response to the consumer-like — or simply human — behaviors of their audiences, B2B marketers are integrating more digital conversations into the tactic mix to meet buyers and customers where they are, better understand interaction context and intent, and enable those audiences in the moment while informing the next tactic. Forrester’s 2021 State Of Demand And ABM Tactic Survey found that 58% of demand and account-based marketers are leveraging conversation automation technologies, and 43% plan to increase or significantly increase budget for online chat as a conversational delivery mechanism.
Here are several key learnings on this topic from our recently published report, New Tech: Conversation Automation Technology For B2B Marketing And Sales, Q2 2022.
Complex Conversations Require Comprehensive Tech Capabilities
The context of conversations in B2B is fundamentally different and more complex than consumer-purchase-oriented conversations. More people are involved in the decision-making process, and purchasing journeys and implementation time frames are longer. This results in a higher volume of interactions through digital and non-digital channels and, now, a mix of human and nonhuman agents.
Conversation automation technologies help B2B marketing and sales leaders holistically approach the design, deployment, and optimization of conversational interactions to deliver relevance and value in the moment while informing the timing and treatment of the next action.
These technologies provide chatbots and virtual assistants (VAs) to automate the exchange of dialogues in natural language and integrate those conversations into and across delivery channels like the website, mobile apps, social media, third-party messenger apps, email, voice skills, and augmented experiences.
Beyond automating simple rules-based dialogue, most offerings provide purpose-built conversational AI that continuously learns about audience context and business objectives to handle more complex conversations. Unlike many popular B2C use cases where the objective is to replace or prevent human conversations, a hybrid approach to conversational agents is required in B2B, where handoffs occur from nonhuman to human agents when predefined conditions are met (for example, reaching a scoring threshold, handling complex solution questions, resolving errors in dialogue, and intent matching).
B2B conversation automation technologies are packaged as standalone platforms or are embedded inside existing marketing, sales, or content engagement platforms. Common use cases in B2B include marketing for the buyer’s journey and customer lifecycle, web conversion optimization, account-based engagement, multichannel nurture integration, event support, and sales outreach and process automation.
New Expectations For Buying And Selling Drive The Evolution Of B2B Conversation Automation Technologies
Trends shaping adoption of conversation automation technologies by marketers and their target audiences alike include the consumer orientation of B2B buying behaviors and interaction preferences, increased expectations for the customer experience, workforce mobility, digital acceleration, and the proliferation of AI and automation in the tech stack. This increasing utilization of conversational tactics in B2B and the willingness of both internal and external audiences to converse with chatbots and VAs to access information and complete tasks reflect the new normal of consumer expectation, customer obsession, and workforce augmentation.
The B2B conversation automation technology market is uniquely positioned to enable both buyer and seller, resulting from the value provided in three key areas:
- Customer-obsessed conversations. Conversational interfaces inside traditional and emerging digital channels provide buyers and customers with new ways to access information using tactics through which they’re already engaged (e.g., visiting websites, accessing content on a mobile app, attending a virtual event, etc.). These conversations must be designed to sense and respond to audience needs, spoken and unspoken, across complex and connected buyer and customer journeys. Integrations among core marketing and sales platforms, content repositories, and data sources facilitate the flow of insights and decisions fueling these conversations and their outcomes.
- B2B revenue engine connection. Conversation automation technologies support B2B marketing, sales, digital experience, and internal use cases aligned to the revenue engine to meet the changing information needs of audiences throughout the customer experience. New buyer and customer signals captured in conversation and the insights they contain must connect to nurture plans, tactics, and sales outreach activities to deliver a consistent and customer-obsessed experience at each stage of the lifecycle, from discovery and evaluation to actualization and advocacy.
- Audience insights feedback loop. B2B marketing and sales teams must be able to understand and predict audience information needs, preferences, and interaction goals and use those insights to orchestrate the next tactic, inform personalization strategies, prioritize sales outreach, and continuously learn from the outcomes of those interactions. Conversational AI capabilities help B2B organizations better understand and enable their audiences in real time, developing a feedback loop of insights through machine learning that can be integrated into strategic planning and made actionable in program design and execution.