AI in a Post-COVID World: Insights from Pam Didner

AI in a Post-COVID World  – Insights from Aaron Bean and Pam Didner

If you were to ask 100 marketing and sales leaders about artificial intelligence (AI) and its role in revenue enablement today, you’d probably get 100 different answers.  

As we move forward with hybrid working models, seeking to maximize the intelligence and buyer engagement potential of digital interactions, automation that complements and increases the value of our marketing and sales motions is a ripe opportunity for revenue teams to act on. 

I sat down with Pam Didner, B2B marketing and sales enablement thought leader, speaker, and author of ‘The Modern AI Marketer’, to discuss the current state of AI, where it’s at now, and what the future holds. 

AI’s core role in revenue enablement today 

AI is intelligence demonstrated by machines. But how do machines demonstrate intelligence? Put simply, we train the machines to learn—known as machine learning (ML). The same way that humans become competent at a task over time, AI teaches machines to improve efficiency, maximise outcomes, or aid decision making. 

There’s no robot or virtual assistant that’s about to take our jobs—at least, not for many years. Instead, using AI is about removing error-prone manual work, expanding our ability to evaluate and decide on next best actions, augmenting and improving our human actions.   

More specifically in the context of marketing and sales enablement, AI has three core capabilities: 

1. Eliminating repetitive, manual tasks 
   
2. Data-driven personalisation and segmentation 
   
3. Predictive analytics—to parse buyer intent signal and guide buyer enablement actions  

Practical AI use cases in marketing and sales over the next 12 months  

The pandemic has accelerated buyer expectations and correspondingly, how revenue organizations must act. The future of buyer and customer engagement is now predominantly digital, so how can you apply AI to marketing and sales programs today and over the next 12 months? 

Start by leveraging AI and ML capabilities within the current tools and platforms you already own. For example:  

• In your CRM. Salesforce ‘Einstein’: AI capabilities built into the platform can help you better understand your buyers, such as where they’re at in the buying journey.  

• In your ABM platform. DemandBase: purpose-built to support account-based GTM strategies with AI that help you better understand engagement at the account and buying group level. 
• In your virtual agent platform. Drift: conversational AI chatbots can deliver an on-demand guided experience, automate initial engagement and direct individuals to contextually relevant information, content and offers. 

Once you’ve picked-off the low-hanging fruit, look to go big. For example, build a customer engagement model with your internal data and analytics team. Run regression analysis to understand the impact of marketing/sales/customer success motions, buyer engagement and business outcomes, then improve your marketing and sales plays based on predicted outcomes—measure, rinse and repeat.  

AI is only as good as the data behind your model 

It’s easy to get carried away with the seemingly near-limitless possibilities of AI. But there are common pitfalls that are often overlooked.  

Most important is the quality of data used to train your AI model. This can tremendously impact the outcome of an AI initiative. Data not only must be clean but also needs to be properly organized and tagged—because structure is what ultimately enables machines to learn and draw inferences from the relationships in your data. 

Understanding the relationship of your source data with the effectiveness of the model you’re building is critical. And whereas getting clean data can take time, unless it’s done properly, you’ll likely not realize the true benefits of AI—garbage in still creates garbage out. 

Practical actions to rediscover your customer  

The pandemic has transformed how we work, how teams interact, even who sits on the buying committee. At the same time, marketing teams have gotten closer to their sales teams, with organisational structures reshaping to facilitate tighter collaboration.  

Typically, buyer personas and buying committee dynamics don’t evolve quickly; but the pandemic has proven a catalyst for change. Now more than ever it’s imperative to deeply understand your ‘characters’ mindset, going beyond the persona to more closely relate to buyers and customers through highly relevant messaging, content, and high-value engagement offers.  

As marketers, we must adopt a ‘detective’ mindset. This way, we can better interpret the customer behaviours that we now have improved visibility to. For instance, if someone comes to your website, what are they trying to discover and what next steps should your systems and revenue organization anticipate?  

Despite AI’s advancement, machines are still just machines. They’re not so developed that human intelligence, judgement, and interpretation aren’t required. We’re still needed to interpret the data, apply common sense, and make a judgement call. 

Learning about your customers should never stop. To enable buyers and drive revenue, now is a great time to revisit your beliefs, challenge your assumptions, and rediscover your customer anew.  

Align sales and marketing from the top down 

Closer alignment is still widely regarded as the holy grail across the revenue organization. AI can support your transformation as you move forward.  

Process and systems integration—and visibility of metrics—are critical to building sustainable sales and marketing alignment. Take advantage of AI to transform your customer engagement and empower revenue teams to be more efficient and relevant in how they engage accounts and buying groups.  

In the Spotlight with: Aaron Bean and Pam Didner

Check out the full interview as I interview Pam Didner about AI and it’s role in B2B marketing today.