Using the Power of AI to Improve the Seller Experience, Simplify the Seller Workflow and Add More Value in Sales Calls

Data-driven selling has the potential to create enormous firm value by improving the effectiveness and value that revenue teams deliver. Particularly in complex, strategic, and highly considered purchases where consultative, creative and value-based selling is involved.

But business leaders will need to shift the focus of their investment in AI and advanced analytics if they are going to realize this full potential.  They need to make improving the selling experience, simplifying the seller workflow, and helping sellers add more value in calls a priority.

Why? “A primary reason most organizations fail to realize the immense promise of sales technology has been the lack of focus on the seller experience and the usability, utility, and adoption as a key strategic and operational goal of their selling technology deployments,” according to Greg Munster, Global Sales Operations Director at Canonical. “The root of the problem is that most selling tools are designed for sales managers not front line sellers. Organizations are deploying too many tools, with too much information, tailored to the needs of sales managers, operations professionals, developers and executive reporting. Fewer applications are focused on improving the selling experience, simplifying the seller workflow, or helping them add more value in calls with clients.”

“To date, most applications of AI in sales have been focused on “policing” activities – measuring seller activity, auditing sales opportunities, providing visibility into pipeline health, identifying skill gaps, and improving the reliability of sales forecasts,” says David Brunner, CEO and Founder of ModuleQ, a platform that has focusing AI on improving the seller experience and simplifying the seller workflow. “But they largely ignore the seller experience, utility and ease of use.”

Why does the seller experience matter?  It’s all about the economics of adoption and utilization, according to Jeff McKittrick, the AVP, Go-To-Market Systems and Strategy at Pure Storage. “Sales tools only create value and performance improvement if they are adopted by sellers,” says McKittrick.  Right now the levels of adoption of sales enablement, engagement, readiness and CRM tools – and the business rules and playbooks that come with them – is surprisingly low, according to McKittrick.  “The notion of universal adoption of sales tools is vastly overstated,” he continues. “in practice, the majority of sales reps don’t use the tools we give them on a regular basis. Low adoption means low returns on investment and inconsistent seller performance.”

 “The key to getting the most out of technology is to simplify the day-to-day selling workflow and ensure the tools you invest in get adopted consistently by all sellers,” says Lynne Doherty, the President of Field Operations at Sumo Logic. “We work hard to make sure the portfolio of tools we utilize help us to simplify selling by automating tasks, improving access to critical customer intelligence, and helping sellers predict which opportunities to emphasize and allocate effort to.”

“The solution to the problem is no secret,” says Greg Munster. “Make these tools simpler, easier to use, and more useful to the sales and service reps who are supposed to use them every day. Achieving an ‘Apple-like’ user experience, ‘Amazon-like’ ease of use, ‘Uber-like’ workflow, and ‘Facebook-like’ levels of adoption should be the goal for the selection, configuration, and deployment of selling tools. That will force solution providers to dramatically change their design criteria for the tools they arm their sales reps with.”

Me and my co-founder Anupriya Ankolekar have spent the better part of the last twenty years applying PhD level AI and systems design to find ways to add value to sellers and simplify their day-to-day workflow. This approach differs from most of the sales enablement, engagement, and readiness solutions in the market today. They focus their AI on learning about the priorities of salespeople, and then what information, insights and resources are most useful to them in achieving those priorities. We call this approach People-Facing AI.

Many sales enablement and engagement platforms have focused on automating low-value tasks and tactics – sending emails, or running sales cadences. Brunner chose to start at the other end of the spectrum by augmenting and enhancing the value of sales interactions. This is regarded as the biggest opportunity for technology to improve selling performance, by far, according to interviews with hundreds of growth leaders in the book Revenue Operations.

We started with consultative sellers – investment bankers, financial advisory professionals, consultants and lawyers. We focused our AI research on answering two important questions: How do you augment consultative sellers? And how do you help them achieve their top priorities?

For learning data, we focus on three powerful but almost universally available data sets. We use collaboration data from email, calendars and contacts. We draw account structures and opportunity data from CRM. And we augment that with news from a variety of robust third party sources, as a result of our partnership with the London Stock Exchange Group (LSEG) a diversified global financial markets infrastructure and data business.

These three foundational “data food groups” may not be sexy in a world where the average organization has a dozen or more sources of customer engagement, seller activity, product usage and financial transaction data from their first party systems, third party partners and Iot embedded in products. it’s important to understand there is a difference between big data and actionable insights. Sales leaders also need to be mindful that while all that information can better inform the sales process and improve the customer experience – it can lead to information overload.

The flow of collaboration data is full of rich signals and insights we can learn from. Each individually does some important things. Email implies engagement and importance. Calendar provides visibility into the future. And CRM provides the core account structure and process information to locate opportunities in the revenue cycle. Collectively analyzing this information helps us understand the sellers: when they work, where they apply effort, how they work, and what they value. Context and timing are especially important if you want to be relevant and useful.”

The key for sales leaders is to understand what AI is good at vs. what humans are good at. We seek to find the perfect middle where these two forces can combine to create value. AI is good at casting a wide net, processing huge volumes of information, and uncovering ‘known unknowns’ and ‘unknown/unknowns’. People, on the other hand, are particularly good at creative problem solving, strategic thought, relationship building, and ‘sensemaking’. We seek to find the “perfect middle” where one plus one is greater than three and AI can add value, simplify the selling process, and solve problems for sellers that they care about.

Jill Billhorn, the SVP of Corporate Sales at CDW believes this notion of People-Facing AI is an important key helping sellers to keep up with the velocity of selling and the digital customer. “The more sophisticated our selling model becomes, the more resources our sellers need to find or pull to prepare for or follow up on sales calls,” says Jill Billhorn. “You’ve got to gather many things – plays, references, case studies, industry expertise, customer intelligence, facts – and it can be exhausting. We spend too much time not fully engaging the customer or adding value.”

To solve the problem, Billhorn wants to fundamentally change the way sellers operate by intelligently pushing relevant and useful information, insights and tools to them. “We are moving from an environment where account managers must “pull” resources, intelligence, information, and content to prepare for and execute calls, to one where those things are “pushed to them,” she continues. “We know everything about our customers and the solutions we are recommending. It’s a matter of using that informational intelligence to ship to a seller so they can let them do what they do. So we spend a lot of time thinking about this as push instead of a pull. It would be a game changer.”

Brunner and his team have made Munster’s holy grail of an ‘Apple-like’ user experience, ‘Amazon-like’ ease of use, and ‘Facebook-like’ levels of adoption their north star. “You need to earn adoption by adding value,” says Brunner. “We are achieving high levels of adoption and seller relevance by using our AI to develop extremely high context and utility.”

Our team developed the ModuleQ platform to be embedded into the collaboration platforms sellers use on a daily basis to become part of their natural flow of work. This allows the AI to get smarter and intelligently “push” recommendations to sellers with what he calls gentle nudges. Within sixty seconds of plugging into the platform, the AI in ModuleQ can identify and recommend content that is useful with  60-70% accuracy based on the business priorities of the seller. And it only gets smarter from there. Within a few weeks, as the AI learns more the recommendations are 80% useful by sellers.

“The common denominator among the small handful of companies that are using enabling technology investments to outperform their peers is that a primary objective of their implementations is to create value for salespeople,” says Bob Kelly, CEO and Founder of the Sales Management Association. Creating value for sellers is a primary design point for ModuleQ. We focus on two primary metrics – utility and relevance. Our seller-facing AI is constantly asking revenue teams two fundamental questions: Is it useful? and is this a priority? We keep asking those questions over and over until we get it right.

As we get smarter, our AI is continuously scouring the digital information universe – on the order of 50,000 business news articles every day as well as internal corporate content feeds – to separate the signal from the noise and find the things that matter to sellers and are relevant to their customers that potentially could inform their approach in a powerful way. We deliver these insights in the flow of their workday in the form of morning updates in the start of the day, pre-meeting briefings before major client meetings, and real time alerts when critical information or opportunities emerge.

Another important way to drive adoption is by working in the platforms sellers like to use instead of forcing them to go into a different platform or CRM to get the information they need. The best pane of glass is the one your salespeople are using. And with the rise of hybrid work, for many organizations, the most popular and frequently-used tools are now online collaboration platforms like Microsoft Teams or Slack. Particularly with consultative sellers and relationship leads in business services, technical solutions, and investment banking organizations. Over a billion people use Microsoft Office and 270 million use Teams. So ModuleQ embeds its AI into Microsoft Teams and Slack rather than introducing another app for sellers to use or providing access through the company CRM. “We found that it’s so much more effective when you meet users where they are rather than making them go to another pane of glass to do their job. So rather than altering your team’s workflow we are enhancing it with intelligently pushed content and insights.


Greg Munster echoes this sentiment. ”In my experience, the tools that get adopted are the ones that front line sellers find useful and easy to use,” says Munster. “And collaboration platforms like Teams, Slack, Zoom, and in our case Mattermost are among the most popular, useful, and highly adopted applications with salespeople.

“ModuleQ is great because it’s not yet another app that management is prodding you to use,” said Adeel Hasham, a veteran enterprise sales executive. “It’s just a completely effortless feed of relevant customer intelligence, showing up right where you work in Microsoft Teams, and automatically personalized to your book of business. The AI helps you have more timely conversations with your prospects and giving you confidence going into meetings that you’re not going to get blindsided.”

ModuleQ’ s focus on using AI to improve the seller experience and enhance the day-to-day seller workflow is paying off. The London Stock Exchange Group has deployed People-Facing AI to over 1,000 sales and account management users globally. They are seeing Facebook-like levels of engagement, with over 60% of front-line sellers actively using the tool every day. Across multiple enterprise sales deployments, ModuleQ has measured a strong relationship between seller productivity and usage of the AI. More frequent use of the AI was associated with 32% more customer interactions and 70% more sales opportunities.

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