Changing the PreSales Game with Data Science
In 2002, the cash-strapped Oakland A’s were forced to rethink conventional baseball wisdom to scout quality players and work within the confines of their small-market budget.
They had just lost their three greatest players to big-money teams like the Yankees and Red Sox, when the Oakland A’s General Manager Billy Beane realized he must take a different approach to win based on hard data, much to the disdain of the baseball-lifer scouts in the clubhouse.
Previous to Beane’s data-driven strategy, scouts had determined player viability based on “talk” such as the beauty of a swing, the looks of the player, and if he still lived in his Mother’s basement.
Essentially, scouts had used their eyes to recruit, which, as you can imagine, didn’t exactly translate into winning games. Yet, this was how rosters had been created in baseball for decades.
Beane knew he must approach the 2002 season far differently than he, or anyone, ever did if he wanted his ball team to have a chance at even having a winning record. For that reason, Beane recruited a Harvard economics major fresh out of school who applied Machine Learning (ML) techniques to algorithmically determine a player’s value, and ultimately, they changed the game forever.
So what can Billy Beane’s 2002 Oakland A’s historic season teach us about PreSales and Data Science?
Gut feelings are never enough
Similar to Beane’s frustration with the Oakland A’s, companies often only listen to the loudest salesman or voice in the boardroom. Like basing scouting decisions off looks, even the most powerful software companies fall victim to gut feelings to guide sales forecasting, deal inspections, and even product decisions.
It’s like trying to hit a baseball in the dark.
If companies want to win, they must think differently, employing data science and AI/ML tools to help PreSales professionals leverage their collective knowledge and make data-driven decisions that ultimately have a positive impact on revenue.
Beane decided to move away from using traditional baseball stats such as the number of home runs or steals or RBIs, and dealt with flack from every scout in his clubhouse. Instead, he used—what his newly-minted Harvard grad Assistant General Manager identifies as the metric that most closely converted to winning games—on-base percentage. The idea was that on-base percentage (data) was an undervalued asset and sluggers (the loudest voices in the room) were overvalued, and if you could get on base, you could score runs, and then win games.
Who then are the best players to get your software on base with buyers and drive home deals? PreSales.
They are the most equipped players in the dugout as they are a perfect brew of technical experts and sales-humans. When outfitted with the right tools, the same tools Beane’s A’s used—ML and data science—PreSales can transform their organizations into winning teams.
With ML solutions like product gap clustering and technical forecasting, driving forces of Hero™ by Vivun (Hero), PreSales can swiftly recognize opportunity gaps and close them to increase the chances of closing deals.
With Hero, built upon our Bayesian network (which specifies how one should update one’s beliefs upon observing data), PreSales teams can metamorphose their organization’s tactics from using gut feelings to hard data that has been aggregated and synthesized by Hero’s ML functions. This produces data-driven recommendations such as deals to focus on and how to win them.
Hero tells you how to win the game
Most simply put, Hero uses ML and data science to produce converting deals. It’s easy to understand how Hero’s AI works via the lens of baseball. If we start by looking at how Beane transformed his clubhouse and his players’ decision-making and baseball entirely using ML algorithms, we can see how PreSales data can become a PreSales team’s most innovative and dynamic team member. At Vivun, the end goal is always to distill raw data into actionable recommendations that allow PreSales team members to have confidence in their trajectories and conclusions.
Using the graphic above as an analogy, I will walk you through how Hero uses—what Beane’s A’s back in the day exposed the tip of the iceberg of— data analytics, ML, and visualizations to make sense of information. Think of the process of the baseball example we give as a baseball manager adjusting their lineup for an important game. Hero does this for PreSales managers, just with no baseballs, and the process is based on the same idea—maximizing as many winning runs, or deals.
1. Raw data
Raw data is like all the data points being created on a baseball field. They haven’t been structured into comprehensible stats. At this stage, the same is true for Hero, which typically has a vast sea of data on deals and opportunities that don’t make any sense together.
So then the next question is how does Hero inject understanding into them?
2. Structured data
Vivun models the data to represent PreSales intelligence, and Hero’s unique knowledge representation is the foundation of this.
Any PreSales professional can acknowledge that most deals look and operate differently. At Vivun, we like to think of this as mental models you have about what deals are like. If you want to get a computer to understand these types of models, you must structure the data in a way that fits that worldview. That’s our approach to data analytics.
Similar to the structured data (Home Runs, Stolen Bases, etc.) in the baseball example, PreSales’s structured data becomes understandable and is reflected in numbers like attach rate, support ratio, opportunity time, opportunity outcome, deal efficiency, direct reports, open volume, and much more.
Now, Hero can start applying logic and inference to the worldview and use ML to identify trends that emerge about opportunity gaps, product feedback, and the technical forecast.
This will answer questions like: What are the functions that describe how deals are dynamic and move and change in the world?
And this is the very same way the baseball manager considers Shotenu Ohtani as the starting pitcher. He takes all the knowledge available to him via ML and recognizes that Ohtani won against this competitor last year and is leading the league in wins for replacements.
Hero recasts a pure data science task into a practical recommendation based on Vivun’s understanding of how deals work and the goals businesses optimize (profit, efficiency, energy expenditure, etc.)
Using ML, the outputs of Hero—Product Gap Clustering and Hero Score— communicate recommendations and tells you how and why it arrived there. Hero’s AI is explainable—it’s not a black box. The transparency allows users to feel confident in their decision-making.
Hero’s purpose is not to automate the PreSales team but to automate the data and give each team member solid, understandable recommendations from which they can ultimately make winning and data-driven decisions.
It’s with the same wisdom and intelligence via ML that Hero’s platform is built on that also lets the baseball manager know if he wants to win tonight, he should start Shotenu Ohtani at the pitcher’s mound.
It’s in the science (and Wheaties)
Like Beane’s A’s, PreSales teams will be transfigured by actionable data that enables them to become revenue-generating sluggers. Primed by AI/ML and built using sound data science principles, Hero will help PreSales teams globally change the game from within their organizations.
Sign up for a Hero Demo and we’ll show you more!