Battle of Britain reenactment
For an example of how poor data analytics can greatly change an outcome, look no further than the Battle of Britain PHOTO: Steve Slater

We now have vast quantities of sales-related data at our disposal. If you use automated systems for compensation management, quoting and configuration, territory and quota, marketing and any other sales or marketing task, all of the related intelligence is at your fingertips. 

But having it is not enough. You need to analyze it — and you need to analyze it correctly. 

Manipulating Data to Tell the Story You Want

History bears out the importance of this. Before we had sales intelligence we had 5000 years of military intelligence, which increased in sophistication and in the amount of data factored into decision-making over time. 

Yet the amount of data isn't what leads to better decisions, as a history replete with awful military decisions indicates.

There’s no better example of this than the German intelligence failure in the Battle of Britain, the air campaign that many identify as a crucial turning point in World War II. The Germans entered the battle with momentum and a major advantage in aircraft (2,000 vs. 675), but their poor use of data caused them to make terrible decisions which helped tip the outcome in Britain’s favor. 

The reasons for this are all too understandable. The Germans entered the battle with a preconceived notion of their own strength, and the head of Luftwaffe intelligence, Joseph “Beppo” Schmid, had a tendency to shape the analysis of the data to fit the desired storyline of his boss, Hermann Goering. 

Sound familiar?

The Germans started by overestimating the number of British fighters lost during the summer of 1940. Their estimate was that 770 fighters had been lost from July 1 to Aug. 13, leaving the Royal Air Force with 200. In reality, the RAF lost 205, and had 608 fighters on strength when the battle started. 

German intelligence also failed to estimate the RAF’s ability to replace losses. The Germans thought the British could produce between 180 and 300 fighters a month. In July 1940, the British produced 498 fighters.

This failure of intelligence became worse as the battle continued. After a week of battle, the German estimate of British fighter strength bobbed up to 300 – half the real number. By Aug. 27, Schmid told superiors the RAF had fewer than 100 fighters left, but the truth was that the RAF was now up to 672 fighters. 

Beware Confirmation Bias

This is one of the hazards of data analysis: the interjection of your own storyline into the data. 

It’s easy to decide the data is telling you something you want to hear — “this new customer and these three new leads indicate that we should leap into this new vertical market!” — but take caution when the data seems to confirm your assumptions and, especially, your desires.

Furthermore, if you’re the person doing the analysis for a manager or an executive, don’t fall into the trap of shading the story the data is telling to preserve their good mood, or to make them feel better in the short term. The data is the data, it should be allowed to speak for itself. 

Misrepresenting it can result in disaster. If managers demand that you color your analysis, they’re more interested in their feelings than in sales success. Extricate yourself before you’re identified as the problem. 

Alternate Data Sources Can Reveal New Insights

Meanwhile, back in 1940, while the British were winning the numbers battle around aircraft, they were being slowly ground down by the pace of combat and by continuous daylight attacks on their bases. It takes much longer to train a pilot than to build an airplane, and by Sep. 15, the British estimated that reserves of both planes and pilots would be exhausted in three weeks or less. 

But the Germans never realized this. The same day the RAF made its dire prediction, the Germans switched tactics and instead of attacking fighter bases — the tactic that had strained the RAF so badly — they started bombing cities, giving the RAF breathing room to recover and eventually win the battle. 

The lesson here is to keep an eye open for alternate data sources that might put your primary data sources into context. 

Had the Germans been interested, they might have detected an abnormally high number of flights over Scotland and Northern Ireland, away from the combat front, indicating the frantic nature of the British training program. Instead, they fixated on the basic numbers of combat losses (both theirs and the RAF’s) in making decisions.

Sales number by themselves are one thing. Comparing them to general economic trends, or performance before and after a sales training initiative, or over the course of a special sales incentive program, might reveal nuances of that data that can help you persist in the programs that work and to quit the ones that don’t. 

Siloed Data Leads to Incomplete Pictures

Another key failure was the German inability to understand the RAF’s ingenious “Chain Home” radar system, which allowed them to put their fighters in the right spots to intercept German aircraft. The radar towers at the 21 stations were enormous and clearly visible, but the Germans never figured out that they were used as a single, combined system. There was plenty of signal intelligence to be intercepted, recorded and analyzed, but the Germans never connected the dots to understand how the British radar was used. 

Why? Ten separate agencies in Germany were involved in analyzing British radar capabilities, and none of them shared their information with the others.

This has a direct corollary in many businesses in which sales, marketing and even customer support are generating and analyzing data — but doing so only for the benefit of their own departments. 

Alignment of data increases in importance as the amount of data increases. Departmental uses are still valid, but so is the idea of pooling data and analysis to create a more sweeping view of the realities of the business. Is a failure in support causing prospects to shy away? Do customers who receive a certain type of message remain customers for a longer period than others? Are there customers whose deals were closed by certain salespeople who call on support less often? 

These kinds of nuggets of information are invisible unless you unify your data across departments. 

Fortunately for western civilization, the German Luftwaffe never mastered the art of data analysis during the Battle of Britain. 

Don’t make the same mistakes: using the data the right way will allow you to come out the winner in the battle for sales.