Sales Intelligence Blog by Lattice Engines

If you are a cycling enthusiast like I am, July is like the Super bowl, the Stanley Cup, the World Series, the Olympics.

Why? Because the Tour de France takes place in July.  For three weeks in July, some of the world’s best cyclists will race around France. Why aren’t all the best cyclists in the race? Obviously, things like injuries have an impact, but the main reason is that there is no such thing as the world’s best cyclist. While Lance Armstrong was winning those seven straight Tours, he never won the World Championships (although he did win it a few years earlier at a time when he was not considered to be a future Tour contender).  How could he be the best cyclist in the world if he was not a World Champion?  In fact, not a single World Champion of the last 18 years has won the Tour.

Tour de France 2011 | Christophe Ena / AP

The reality is that he was a great stage racer for three week tours, but for races like Paris-Roubaix in northern France, there were other riders on the team who were favorites. So, leadership roles actually depend on the course being raced. For any given race, there are only one or two leaders while the other racers in the 10-rider squad have their respective roles to play in ensuring success. Armstrong would not have won a single tour without teammates or the supporting crew. It may come as a surprise, but the winning prize money is equally split by all the team members.

How do you know who your sprinters, climbers, and all-rounders are? In cycling, every team does an in-depth analysis of their riders at various times of year, based on their results, and crucially, on lab tests with power-measuring devices. This way, they know the respective strengths and weaknesses, which can change as riders come into and out of form and train different aspects of their riding.

What about Le Tour de Finance?

In asset distribution, it is just as important to look at the ‘race results’ – the sales and redemption numbers – and analyze underlying dynamics like sales coverage patterns. Perhaps you have wholesalers that are killing it on gross sales but are using tactics that lead to redemptions in the long haul. Or, you may find wholesalers that are not necessarily successful overall, but who are very good at acquiring new advisors.  The key is to dig deeper and analyze what’s driving the results.

The challenge in asset management, of course, is that wholesalers are tied to a territory.  Each wholesaler will excel in certain areas. A wholesaler might be better at dealing with RIA’s than with wirehouses, or selling fixed-income as opposed to equity funds, or selling to existing advisors vs. on-boarding new ones. However, since wholesalers are assigned to specific territories, you can’t simply match wholesalers and advisors across the board based on advisor need and optimal wholesaler skill set.  The solution is to help wholesalers do better even when the terrain does not necessarily suit their abilities.

At the time that Lance Armstrong decided he wanted to win the Tour, bike racing was steeped in tradition but not necessarily science. Sure, there was tribal knowledge, but a truly scientific approach had not been tried. If he was going to make the transition from a good one-day racer to the most successful Tour de France rider in history, he was going to have to employ revolutionary methods.  He did this by putting a team together to uncover his weaknesses, and to work on specific training plans and tools to round out those weaknesses. Sure, he trained hard and was exceptionally gifted, but he used data extensively and was one of the pioneers in introducing a more systematic approach to bike racing. Fifteen years later, even masters racers (over 40) like me employ a much more scientific approach to training. The sport has changed. After one particularly hard race I asked a fellow rider who had dominated the category a few years earlier what he thought of the race and he said that the pace was way higher than it used to be. He knew from measurements that he hadn’t changed – the competition had become faster from better training methods.

In many ways asset distribution is similar to the way bike racing was in the late nineties. There are pockets of advanced thinking and methods but by no means is every firm employing data-driven approaches to asset distribution. The ones that do so successfully will be able to break away from the pack, and perhaps wear yellow!

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In “Scorecasting: The Hidden Influences Behind How Sports Are Played and Games Are Won”, Tobias Moskowitz (an economist) and L. Jon Wertheim (Sports Illustrated writer) take an analytical approach to assess and quantify sports phenomena like “home field advantage” and “over-paying first round draft picks.”

One of the more compelling chapters uses mathematics and game theory to explain why football teams (professional, college, prep) are generally better off foregoing a punt or field goal attempt and should “go for it” more often on fourth down.  The authors argue that football coaches consistently misunderstand the likely outcome achieved with letting the offense play fourth down, and opt for a conservative course that keeps scores low and punters employed.  They cite coaches like Bill Belichick and others in the college ranks that (statistically) opt to go for it on fourth down more often than colleagues do – and have achieve greater levels of success.  The book suggests we take a second look at our assumptions and expectations to make better decisions that will “put more points on the board.”

We have noticed sales leaders increasingly ask their organizations for more data about their market opportunity, their performance against that opportunity and the effectiveness of various sales motions.  Sometimes this can mean changing the central premise on which a function operates.  For example, sales leaders focused on driving higher new customer acquisition rates from “leads” have looked at new ways to enable reps to proactively contact a prospect – relying less on “hand raising” and more on data suggesting a prospect has a near-term business need. 

What are the elements of your sales process that need new thinking?  Do you punt or kick field goals when you should have a different set of players on the field?

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Sales people are the quintessential sports lovers, and it is a rare meeting that does not make generous use of metaphors and anecdotes borrowed from the game of Baseball. Baseball season is not in full swing yet, there is something really exciting going on across the world – the Cricket World Cup!

With over two Billion people in Australia, Bangladesh, Canada (yes Canada!), Netherlands, Ireland, England, Pakistan, Sri Lanka, West Indies, India, Zimbabwe, South Africa, New Zealand and Kenya paying attention to the event, you can’t go wrong learning a little bit about the sport and the parallels with Baseball.

Let’s start with the similarities. Both Cricket and Baseball are played between two teams taking turns with a “bat” and a “ball”. Throwing the ball is called “bowling” in cricket while the ball is “pitched” in Baseball. Both the games involve something being defended (the “Strike Zone” for baseball is equivalent to the “wicket” for cricket) against the ball. Once the ball is hit with a bat, the batters (batsmen) run from one base (wicket) to another. If the ball is caught before it hits the ground, the batter (batsman) is deemed out and someone else takes their place. The goal is to score as many runs as possible in each case, and the team scoring more runs wins. So far so good!

Now for the Differences:

• The way runs and outs are defined: In cricket the team clocks a run every-time the players run from one wicket (base) to another. Baseball runs only count when the player completes all the bases. Cricket outs come in many varieties (run out, caught, bowled, leg before wicket, stumped, and hit wicket). As you would expect from a game invented by landed English gentry, there are other obscure ways to get out like handling the ball, hitting the ball twice (go figure!), obstructing the field and timing out.
• The structure of the game: A cricket team has eleven players, and each player gets to bat until at least ten of them get out, or the number of pitches (balls) run out. The World cup format is called the one-day game, compared to more genteel five-day tests – originally funded by colonial economics. In this format, each side gets to play one inning each which is limited to a maximum of 300 balls (pitches) grouped together in 50 overs (an over has six balls).
• The way the ball gets to the batsman (batter): In cricket, you are allowed to bounce the ball off the ground before it gets to the bat. This means the condition of the field, the soil, the moisture level, the amount of grass on it, makes a huge difference in the speed and turn with which the ball arrives at the batsman. Since soil conditions are hard to quantify, this also makes the game somewhat more unpredictable and harder to model.

Lastly, Baseball has come to rely on predictive analytics to shape their teams and their game.  Ever since Michael Lewis wrote the excellent book Moneyball about the Oakland A’s and their manager Billy Beane’s predictive models of player performance, there has also been an increasing interest in the use of data to build world-class teams. There were many insights that emerged from the A’s analyses, but the one that really stuck were the importance of “on-base percentage” and “slugging percentage”, both of which were found to be better predictors of player performance than “runs batted in” or “batting average”.

Cricket is not so easy to predict.  Take for example what is probably the greatest upset ever in Cricket history – the Ireland-England game played on March 2nd, 2011.

With a long history and a solid line-up, England were 20/1 favorites going into the game. They had just drawn the game with one the perennial favorites – India, and were looking to wrap up the game with Ireland quickly.

The English team batted first (remember there is only one inning), and scored 328 runs. Eight of their players got out before they ran out of their 300 balls (50 overs). The top scorer was Jonathan Trott, a 29-year old, with 92 runs made off 92 balls. This means he ran between the two wickets 92 times – this by the way is why the Cricket players are in better shape than their Baseball counterparts. His strike-rate (SR) was 100%, as in he scored a run for every ball he faced. Not bad!!

328 runs is actually a lot of runs, even for Cricket. The usual heuristic is that if you bat first and score less than 200 (4 runs per over) you are in trouble, while a 300+ score (>6 runs per over) puts you in the safe zone. Anything in between leaves the match open. So England going in with 328 must have felt pretty secure about the outcome.

Ireland started poorly, losing their first 5 wickets (getting 5 outs) for 111 runs, with only 154 balls remaining. Since they had to score 329-111 = 218 more runs to win, they now needed to score at a strike-rate of 141%, which is an impossible target for almost any team. Well not for Kevin O’ Brien from Dublin, Ireland. He scored 113 runs off 63 balls at a strike-rate of 179%, that’s right – he scored almost 2 runs for every ball he faced. The English team didn’t know what hit them, as the Irish wrapped up the game with a score of 329, having lost only 7 wickets (3 outs still to go) and 5 balls remaining.

I have often heard the refrain from my friends that they don’t like soccer because it is too low-scoring. Well if you want a really high-scoring sport, Cricket could be the answer.

So what does all this have to do with B2B Sales Intelligence? Not much, except that the definitive “Moneyball” for Cricket or for Sales Teams is yet to be written. Our software helps the Sales side, but Cricket deserves its Billy Beane too. I hope someone will explain how to spot the next Sachin Tendulkar, Wasim Akram or Don Bradman. That will make the game a lot more interesting, maybe even to Baseball lovers.

In a future version I plan on writing the complimentary “Baseball for Cricket lovers” but we will have to wait till the World Series for that.

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For those interested in more details, please see the detailed Wikipedia article on the subject. I have borrowed some of the images and content from this source.

Special thanks to Geoff Zassenhaus, a crazy Red-Sox fanatic (is there any other type?), who has been patiently explaining Baseball to me for over fifteen years. In memory of the late Partha Niyogi, my brother-in-law and friend, who could have written the “Moneyball” for Cricket. His love of the math(s) of Cricket was infectious; I miss our late-night conversations about how to use statistics and machine-learning to find outliers in cricket and elsewhere.

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At the beginning of the NFL season, a questionnaire was sent to NFL coaches and other top brass.  It asked what they felt was the most important statistic as it related to a team making the post-season.  Conventional wisdom has been “defense wins championships” — so the majority said “defensive scoring (low points per game)” or “yards allowed” or other defensive categories.  Some even said offensive “points per game” or “rushing yards per game.”

As it turns out, over the last several years the statistic that has proven to be the most predictive (as it relates to teams making the post-season) is “offensive 3rd down conversion %.”  Not one coach or general manager guessed this.

2010 Offensive 3rd Down Conversion Ranking:

Top 5:

• New Orleans (2nd place; near-certain to win a playoff berth)
• Atlanta (1st place; already earned playoff berth)
• New England (1st place; already earned playoff berth)
• San Diego (2nd place; likely playoff team)
• Indianapolis (1st place; likely playoff team)

Bottom 5:

• San Francisco (last place)
• Denver (last place)
• Carolina (last place)
• Washington (3rd place)
• Arizona (last place)

For teams that consistently convert third down plays, it generally implies you have a good quarterback, good coaching/preparation and players that simply come through in the clutch.

The implication for sales & marketing leaders is this – creating truly effective sales campaigns requires an understanding of historical behavioral and purchase patterns:

1. Understand the attributes of your top customers;
2. Understand the attributes that make your sales teams successful;
3. The most obvious metrics (“revenue growth” or “attainment”) are often the result of more fundamental metrics.

Integrating these into your sales intelligence efforts will help align new campaigns to what works and imprve your chances of the win.

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