In B2B, a buyer is nearly always a group of people working together rather than an individual acting alone. Forrester’s 2021 B2B Buying Study revealed that over 60 percent of purchases have more than four people involved–versus just 47 percent in 2017–and they can include different buyer roles and departments. These findings led to Forrester updating its B2B Revenue Waterfall, enabling companies to connect individuals to their buying groups, associate them to specific opportunities and then track the progression of opportunities through the revenue generation process. It shows a tighter alignment between marketing and sales.
However, there isn’t always a clear path to alignment. Lack of agreement on roles and responsibilities, lead qualification, and ways to measure success can impact an organization’s ability to increase the volume of opportunities and velocity they move through the Waterfall. To address these areas of underperformance, organizations can look to Conversational AI for a disciplined approach to close gaps in the sales process, enabling opportunities to move faster into pipelines.
Solving the Sales-Marketing Conflict
One of the greatest sources of disagreement between marketing and sales is defining an appropriate lead, whether it’s qualified, and when it can advance through the Waterfall. These disagreements often stem from an inability to qualify leads through personalized engagement. There’s no better way to determine if a lead is sales-ready than to directly ask them, “Are you ready to talk to sales?” However, effective, engaging outreach takes considerable time and effort. Imagine trying to manually personalize every email, online chat, or text message to multiple individuals within multiple buying groups and verticals, according to where each account is in the buyer journey–across hundreds or even thousands of accounts. It would be taxing for even the most well-staffed sales and marketing teams.
AI Assistants, powered by Conversational AI, are gaining momentum for their ability to touch every lead with personalized engagement at scale. When these leads are given a personal touch earlier in the funnel, they feel more connected and valued by businesses. More importantly, this AI-driven proactive outreach provides leads with the opportunity to raise their hand earlier in the customer journey–which means opportunities accelerate to the notice of the sales team. AI Assistants can also help sales and marketing mitigate challenges that arise from inconsistent and inaccurate data by evaluating the health of each lead through email verification and measuring information against criteria sets to guarantee emails sent have a high delivery rate. When it comes to helping marketing and sales be more effective, Conversational AI offers an ideal solution.
Defining Roles and Responsibilities Through the Waterfall
Misalignment on roles and responsibilities in the Waterfall can be another source of ongoing tension. Without agreed-upon goals, definitions, and defined roles for each team, miscommunications can often result in dropped leads. Conversational AI is helping to bridge the gap between marketing and sales teams by meeting both teams’ needs. AI Assistants can ensure marketing leads meet the agreed-upon criteria of being sales-ready through better traction in the pre-qualification stage.
AI Assistants can engage early-stage leads by generating and fostering initial interest through a lead’s actions. For instance, AI Assistants can determine if a lead would like more information by asking discovery questions upfront, saving time and energy for marketing and sales. Conversational AI can transform into another team member by adding continued touchpoints to maintain consistent communication throughout the buyer journey. Furthermore, there are Conversational AI use cases that fit retention and growth needs post-sales. For instance, if an AI Assistant identifies that a customer isn’t utilizing a product as much, it can proactively conduct outreach to check-in and share educational content.
Waterfall Measurement and Creating a Feedback Loop
All too often, marketing and sales teams question whether they’re doing a good job or if their AI Assistant is doing its job. Well, you have to inspect what you expect. To define what successful Conversational AI deployment looks like, organizations need to establish key metrics, such as average response time, waterfall velocity, the number of sales accepted leads, deal velocity, and pipeline value. Once the metrics are defined, a feedback loop should be introduced to collect data and glean insights. These insights can be used to communicate outcomes with marketing and sales to ensure both are joint stakeholders in the process. From there, it’s just about adjusting and reapplying.
For instance, if organizations apply Conversational Marketing at the top of the funnel, Conversational Sales in the middle and bottom, and Conversational Customer Success after a lead becomes a customer, there are various metrics to evaluate. Organizations can measure how engagement is being driven, the number of leads that have been contacted, the time for leads to get hot, and churn rates for existing customers.
There are different ways to consider Conversational AI applications across Forrester’s Waterfall, and leaders will have to assess where Conversational AI can deliver the most value and ROI. No matter the use case, Conversational AI has the power to provide leads, prospects, and customers with better experiences and empower marketing and sales teams to unlock their full revenue potential.