The Skill of Extrapolation as a Consultant
One of the most vital skills a new consultant can have once they’re on the job is the skill of extrapolation.
This involves taking some data from a client and extrapolating the data to determine some kind of trend line.
In consulting, the data is rarely perfect and never complete. You are always missing something.
So you have to end up estimating, taking a sample, “backing into” the number you are looking for (by quantifying everything else except the number you are looking for), and many other ways of drawing reasonable conclusions from imperfect information.
Some new consultants who do well in case interviews actually have a very hard time with this on-the-job reality. This is particularly the case from those that come from science and math backgrounds — especially those with PhD backgrounds.
Often people from these backgrounds are looking for an exact, definitive answer. And the reality is in business, often there are multiple right answers. This is troublesome for some people.
What is also difficult for some is extrapolating a trend from a bunch of data points.
Here’s an overly simple example.
A few days ago, I received an email from someone who has a BCG interview coming up.
This person asked me what I thought BCG’s policy is for facial hair… in particular a man’s beard.
He astutely noticed that not a single male in the BCG recruiting website had any facial hair.
In my reply I said, “I am not aware of any facial hair policy at BCG or any other firm.
“But when I was at McKinsey, I do not recall meeting any consultants in North America, Europe or Asia that had facial hair.”
I also suggested the person draw his own conclusions from my statement.
Two days later, he cut off the beard he has had for several years.
That’s a pretty simple example of extrapolation.
There’s a joke within McKinsey that goes like this:
How many data points do you need to extrapolate a trend line?
Business Analyst / Associate: 150 data points
Engagement Manager: 25 data points
Partner: 4 data points
Director: 1 data point 🙂
This joke is an example of extrapolation accuracy vs. sample size.
There are other kinds of extrapolations too, including using data proxies.
If the data you really want does not exist, the idea is to use data that is correlated with the data you wish you had.
This is often the case in estimating market demand for a product that does not exist.
For example, when the Apple iPod/iTunes combination first came out, it would have been difficult to estimate demand for the iPod because consumers had never seen the product before.
This is where some creativity comes into play (yet another form of using extrapolation to find the answer you seek).
Suppose Apple was your client and they wanted to know what percentage of the mp3 player market you think the iPod could get… keeping in mind they asked you this question one year before the iPod came to market for the first time.
Well one way you could try to extrapolate an answer to that is to say, “What functional value does the iPod/iTunes combo provide? What is the next closest thing to this particular solution?”
The answer that comes to mind is an mp3 player, a car, and trip to the music store.
So you could conduct a market research project in the music industry to find out what percentage of the market hates driving to the music store and is willing to pay to avoid the trip.
Also since iTunes sells individual songs instead of full albums, you could also ask consumers, “What would you pay to avoid having to buy the entire album and just be able to buy the one song on the album you really want most?”
Keep in mind, all of these analyses are imperfect (welcome to consulting!).
You could then triangulate these different analyses to see if you could determine a likely range of potential market demand. This too is a form of extrapolating… a specific technique called “bounding the problem.”
This refers to estimating an upper boundary to the number you seek, and a lower boundary too.
I mention all of this because many of the skills needed to be successful in consulting are subtle.
They are the kinds of things you figure out in consulting… eventually.
The challenge is not whether or not you will figure it out. It’s whether you will do so before the managers and partners in your firm judge your performance.
If you have a summer associate or internship type position, you really only have a few weeks or months (depending on your role) to make the right impression to hopefully secure a full-time offer.
So the question becomes: Do you figure out these subtle aspects to doing well in consulting before or after the partners evaluate your performance?
As you can imagine, when it comes to performing well, solidifying your reputation in your office, or developing your personal “brand,” timing makes an enormous difference.
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