Bridging the gap between what sales teams ‘think’ and what your CRM system ‘knows.’
AI and Machine Learning has been touted as solving many of our B2B sales challenges. You merely apply an AI tool, and it predicts which opportunities will close. It applies advanced algorithms to deliver advice and next best actions for each opportunity in your pipeline. You can then predict and sustain revenue. Just glance at the tout sheets from the multitude of AI platform vendor’s offering sales analytics and you will see they claim to do everything but talk to the prospect.
Artificial intelligence and machine learning have made great advances in the understanding of customer actions in retail markets, consumer products and services, and online transactions. When sales volumes are huge and there is a structured buying process, predicting group buying behaviors can be quite precise in guiding marketing and sales campaigns. Think selling jeans or consumer goods.
Unfortunately, extrapolating these same techniques to the world of low-volume, high-value, complex enterprise SaaS sales is fraught with challenges. The buying process is complicated, highly variable, and lacks structure which results in a series of dynamic human interactions that create an infinite number of possible sales cycles.
So, what gets in the way of applying AI to your B2B sales process? Well, to net it out, humans. Salespeople and prospects are unpredictable.
The selling strategy, experience, and knowledge associated with aligning the buyer journey to the seller actions often only exists in the mind of your sellers. The information associated with compelling events, the solution value, and process for purchasing are not easily mapped to the structure and format of your CRM system. Applying predictive analytics and advance AI algorithms to flawed or missing data is truly the definition of insanity.
What to do? To bridge the gap between human intelligence and machine learning follow the blueprint applied in industries such as healthcare, robotics, and aerospace.
Known as ‘human-in-the-loop’ (HITL) it is a structured approach to survey executives, managers, and sales teams on those strategies and actions most impactful to sales success. These findings bridge the gap between what your sales teams ‘think’ and what the CRM platform ‘knows’ – and can then be used to train, test, and fine tune your defined AI algorithms.
Step 1: Leverage sales analytics and AI to create a first pass of where you win.
Based upon your historical opportunity win rates, establish a list of those key attributes and metrics that have the highest impact on sales success. Compare your results to those found in top performing companies to identify those data elements and sales process flows that will have the highest positive impact on your sales performance. Run these optimized algorithms against your current pipeline to identify those opportunities that have the highest probability for success or failure.
Step 2: Challenge key stakeholder to define ‘why you win’.
Create a survey to capture what sales, sales management, marketing, and executive management identify as those key activities that define sales success. Provide an objective structure that includes those key market segments, solution attributes, performance metrics, and behavior metrics that your team ‘knows’ have the highest impact on sales performance and predictability. The key is to determine where across the ‘lead to opportunity closed/won’ lifecycle each one of these identified data elements could and should be captured.
Step 3: Align algorithms to encompass both what you ‘think’ with what you ‘know’.
Document and communicate those attributes and metrics that account for your unique AI algorithms to all stakeholders across the company. Explain why capturing these data elements in an accurate and timely manner delivers sales success. Create dashboards for users to self-monitor compliance across each stage of the sales process. Every 4-6 months, recalibrate your algorithms to include HITL adjustments aligned to evolving corporate strategies and market dynamics.
Step 4: Continually coach to success.
Once you have established those attributes and metrics aligned to sales success – leverage these insights to coach underperformers. Leveraging reports, dashboards and views, managers can quickly review an opportunity to prepare for pipeline review meetings. Don’t fall into the trap of relying on your AI tool to automate recommendations and point to data inaccuracies. Leverage these algorithms to create real-time reports by sales stage to identify in real-time those opportunities that are aligned, and misaligned. Establish your CRM platform as the ‘only source of truth’ for tracking, tracing, and coaching corrective actions for all misaligned opportunities across your sales funnel.
Conclusion Make sure you apply HITL insights to your AI analytics. AI applied to complex, B2B enterprise sales has been way over hyped. Sales teams completely discount the value of AI applied to CRM systems since they know it does not account for the fundamental opportunity knowledge they have between their ears. The solution is to make sure sales teams, managers, and executives are empowered to optimize the AI algorithms to include their collective IQ. Combining AI and HITL ensures sales algorithms will be optimized to deliver opportunity insights, sales team performance, and longer-term revenue predictability.
