Augmenting PreSales Expertise with AI and ML
I sat down (virtually) with Joe Miller, our Co-Founder and Chief Data Scientist to understand what he set out to accomplish at Vivun, and the company’s work on empowering PreSales teams with data science and artificial intelligence (AI).
As a result of our conversations, I published a whitepaper that goes into depth on some of the techniques we use in our AI-driven PreSales platform. This blog post is a synopsis of our overall approach to AI and machine learning (ML) at Vivun, and why we believe combining PreSales expertise with these methods can lead to transformational outcomes for every technology company.
The world of PreSales is filled with complexity
PreSales is hard. SEs working a deal must take into account their buyer’s unique needs, the competitive landscape, the intricacies of their product, how to best partner with their Sales counterparts, and so much more.
Furthermore, PreSales is a highly cross-functional role with increasingly broader responsibilities as a company grows. While the core responsibility of PreSales is in providing technical expertise to software buyers and guiding them through increasingly convoluted sales cycles, PreSales teams often take on additional roles in post-sales, security, product, partnerships as well.
Winning PreSales teams quickly identify and scale best practices
So the answer is more headcount, right? Unfortunately, PreSales professionals are notoriously difficult to hire, ramp and retain. Even great SEs, once you manage to hire them, need time to develop the mental models and context required to succeed at their new companies.
As team responsibilities and headcounts grow larger, they also become progressively more difficult to scale. Processes for onboarding new hires, supporting deals, and capturing product feedback that worked for a smaller, scrappier team of 10 or 20 won’t necessarily hold up in a group of 200.
To make success truly sustainable and scalable, PreSales leaders need ways to quickly identify what makes for great SEs within their organizations, and preserve that institutional knowledge through each successive phase of their companies’ growth.
What AI and ML can do for your organization
At first, AI appears as a buzzword-heavy industry, but AI and ML models are incredibly effective at pattern matching on large amounts of data, which make it especially useful for:
- Reducing the cost of making reasonable predictions
- Automating routine or lower-value tasks
- Quickly identifying interesting trends where humans would normally take longer
What many experts in the field will also tell you is that it’s “the human in the loop” and the quality of the data set that matter most when it comes to the application of AI and ML, not whatever system or models your software is using. AI-driven software platforms exist today to augment human reasoning, not supplant it entirely.
Unlocking PreSales superpowers
How then can we empower PreSales to operate at their fullest potential with AI? You might start with some of the fundamental questions that PreSales professionals ask themselves every day, and what can assist them in finding the answers:
“How likely is this deal to close? Are we working the right deals, the right way?”
SEs hold their Sales partners accountable in the forecast. Perhaps quantifying and structuring the technical conscience of every deal will give PreSales a stronger voice in those discussions, and prevent the forecast from being excessively colored by gut feeling. What if PreSales had a system that, based on the information they’ve logged on an opportunity, could make reasoned predictions on how likely that deal would be to close?
“Is the team spending time where it counts?”
PreSales often gets pulled into projects that don’t directly contribute to revenue, but making the case for more headcount, greater authority, or for other teams to take on more is difficult because tracking PreSales activity and deliverables involves a large amount of tedious administrative work. Streamlining the capture and analysis of those activities and deliverables could paint a clear picture for PreSales leaders of how the team spends its time, and where to make improvements.
“What product gaps are holding us back? How can we make our case to R&D?”
PreSales plays a crucial role in gathering product feedback from customers and prospects so that the product roadmap aligns with what buyers want, but even for Product organizations that value PreSales feedback, sifting through feature requests to find what’s important can be a daunting task. When the Product team is overwhelmed by a mountain of duplicate feature requests, the result is missed opportunities and a lack of alignment with PreSales on what’s truly important to buyers. Is there a way to automate the intake of feature request data from the field, then aggregate it into discrete Product Gaps that can be assessed and acted on by the Product team?
PreSales and AI explained
How do you build an AI platform for PreSales such that it’s grounded in the experience of actual PreSales professionals, and feels natural for them to use on a daily basis? Find out in our whitepaper, Unlocking PreSales Superpowers with AI, which details some of our core principles around AI, explains how we laid the foundation for our platform with a PreSales Ontology, and provides in-depth explanations on some of the key tools and techniques we use in Hero.
If you’re looking to learn more about the AI-powered PreSales revolution, you can also check out: