September 20, 2011


If you were at the conference last week, you heard a keynote presentation from Bill Bass, e-commerce guru at Charming Shoppes.

One of the terms he used in his presentation was "HiPPO", or "Highest Paid Person's Opinion".  This is a term that generates a lot of oxygen out on Twitter.  Data-driven individuals love to point out the folly of the "C-Suite", where decisions seem to not be made based on facts, but rather, on the gut instinct of the person with the biggest salary.

Every one of us have experienced this issue. We get especially frustrated when the opinion is different from our opinion, we seldom point the issue out when the opinion is in agreement with our point of view.

As an analyst/manager, from 1988 - 1997, I railed against the concept of "The HiPPO".  I couldn't believe how decisions were actually made.  I couldn't believe how the departments that I worked in were underfunded while VP positions were being added in large quantities.  I felt frustrated that my data-driven ideas, all seemingly iron-clad and backed up with thousands of pages of tables and charts representing actual customer behavior, were not adopted.

As a Vice President, from 2001 - 2007, I became "The HiPPO".  I hosted hundreds/thousands of meetings, meetings where data was presented, data that I either didn't believe was accurate, or had a philosophical difference of opinion with the presenter of information, leading me to decide something contrary to what the data suggested.

You have to be a VP/SVP/EVP/President/CEO to understand why data-driven arguments are ignored.

And you have to be an Analyst/Manager to understand how frustrating it is to not be listened to.

So let's try to bridge the gap.

There are many reasons why seemingly good ideas are rejected in favor of the opinion of a high-paid Executive.
  1. Incorrect Data:  I cannot tell you how many times I sat in meetings, and quickly noticed errors in the data being presented by Analysts/Managers.  The Analyst/Manager believed the data was accurate, but twenty years of experience told me different.  This isn't talked about enough ... audit your results, folks!!  If an Executive doesn't accept your data-driven argument, it is entirely possible that the Executive doesn't believe your data is accurate, and doesn't want to publicly humiliate you.  I've probably done this a thousand times ... after a meeting, I'd speak to the individual in private about the error instead of humiliating the individual in front of his peers.  To the folks in the meeting, I was just a dumb HiPPO.  Please, I'm begging you ... audit your results!
  2. Incongruent Strategy:  You're a VP, hosting a meeting, and your Analysts/Managers are presenting something that is 100% opposite of the direction your CEO wants to take the company.  Now, you're in a dilly of a pickle here.  Do you tell your team what your CEO is thinking?  Do you criticize your CEO?  Do you protect your CEO?  Well, it depends, doesn't it?!  So often, you're forced to make a quick decision in a meeting, and that decision results in the rejection of a data-driven argument from an Analyst/Manager.  How you communicate your thoughts to your team might make you look bitter, might make you look like a gossiper, might make you look authentic, or might make you look like a HiPPO.
  3. External Data:  This one comes up a lot.  I was recently in a meeting where an Analyst/Manager used Forrester Research data that predicted a mobile/social dominated marketing landscape ... and used this as an argument for making significant changes to today's marketing strategy.  The Executive mentioned after the meeting, to me, that she was looking for the Analyst to find ways to drive sales increases this Fall, not in 2016.  The Executive did not shame the Analyst in the meeting.  Analysts/Managers fall into this trap, it's a confidence trap ... we use external data to try to communicate that we aren't the only ones who are thinking a certain way.  Where possible, Executives seem to value internal customer behavior over external customer research that may not be applicable to the company.
  4. P&L Accountability:  This one is hard for the Analyst/Manager to understand.  When an Executive promises to deliver $50,000,000 net sales and $5,000,000 earnings before taxes, the Executive isn't going to want to risk not delivering on this promise.  When the Analyst/Manager presents a data-driven idea that, in the eyes of the Executive, has a 50% chance of success, well, you've got problems.  What if the idea results in a 5% reduction in net sales and a 20% reduction in profit?  Yes, the Executive gets fired!  The Analyst/Manager, however, does not get fired.  Given the risk/reward ratio, the Executive is unlikely to side with the data-driven argument.  By the way, Executives operate under the fear of being fired all of the time ... they're not going to tell the Analyst/Manager that one more mistake results in being canned, they're simply going to reject a solid data-driven argument and not communicate why.  And Executives with job security are not necessarily more likely to accept data-driven arguments, either.  The risk/reward relationship in each company is different.  The Analyst/Manager simply needs to understand that the risk/reward relationship is much riskier to the Executive than it is to the Analyst/Manager ... this isn't right/wrong, it "just is".  If you want your data-driven argument to have a better chance of succeeding, publish a predicted net sales / profit range associated with your idea ... for instance, you expect net sales to increase by between $1,000,000 and $2,000,000 a year and you expect profit to increase by between $400,000 and $800,000 a year.  This frames the argument for the Executive.  Heck, be willing to publish the probability of failure ... you expect your strategy to generate a positive return on investment 70% of the time and you expect it to fail 30% of the time.  Your Executive will likely appreciate your candor.
  5. Repercussions:  This is a big deal.  Back in 1994 at Lands' End, I was a Manager recommending a strategy that had significant repercussions on the company ... my recommended strategy was more profitable, but would result in the loss of jobs, among Executives, Managers, Analysts, and Call Center / Distribution Center staff.  My recommended strategy had painful repercussions, and was ultimately rejected.  Nobody wants to go through pain, and people sure don't want to lose their job so that the company can be more profitable!  In other words, you may have a great idea, but some "HiPPO" goes with gut feeling because she knows what the repercussions of your data-driven strategy are.
  6. Risk and Comfort and Bridges:  How many times have you heard somebody say "that's not how we do things around here?"  Sometimes, the Analyst/Manger doesn't have the experience to "build a bridge" between the past and the future ... the Analyst/Manager simply blurts out a data-driven strategy that has risk associated with it, and is uncomfortable to the Executive.  Think about your mobile phone ... your phone was the end result of a chain of events over two decades that allowed you to feel comfortable purchasing something via m-commerce.  Take your Android phone and introduce it to somebody in 1992, and you'd cause that individual to freak out!!  A "bridge" was built between land lines and cell phones, between old-school commerce and e-commerce and e-mail and m-commerce, enabling you to see the future and feel comfortable.  An Analyst/Manager is well served to build a bridge between "the way we've always done things" and "the future".
  7. Pure Evil:  You are going to run into this.  There are Executives who operate under self-interest.  They lie, the deceive, they manipulate, they do everything possible to benefit their own agenda.  Believe it or not, there are Analysts/Managers who operate this way as well.  In the short-term, Evil has a way of winning.  In the long-term, Good tends to prevail.  Where possible, identify whether you're dealing with typical human shortcomings and forgive typical human shortcomings, or identify the behavior as Evil and deal with it accordingly.
"The HiPPO" problem isn't really a problem, it's a myth that inexperienced folks on Twitter like to perpetuate ... it doesn't address the real issue.

The real issue is "communication".

We have Executives who are unable to or unwilling to communicate what is really going on.

We have Analysts/Managers who are inexperienced, and are unable to communicate effectively.

So instead of labeling somebody as a "HiPPO", think about the reasons that decisions are made, decisions that are opposite of what data suggests.  Could you (Executive or Analyst) communicate differently, in order to facilitate a more reasonable outcome?


  1. Hi Kevin,

    Some great insights into the mind of the Executive there and you have raised issues I hadn't previously considered. I am forced to agree that in many instances, data is dismissed because of poor communication or inaccurate data like you say.

    But I think it is a bit strong to simply dismiss the concept of the HiPPO that quickly. I use the term in all my training sessions but I primarily refer to situations where data is not even considered. Where decisions are made entirely based on opinions, with the most senior or loudest person's opinion counting for the most. And this situation is very common as well.

    In these situations/companies, data does need to be introduced to counter and improve upon opinion based decision making. I would not say the HiPPO problem is a myth, more that (as you say) ppl should think if there is a reason their data has been rejected instead of assuming it is due to a HiPPO. But for many companies, data is not present at all at their executive meetings and this is what I commonly refer to when I use the term HiPPO, on twitter or elsewhere.



  2. Great post Kevin!

    As an analyst, i have had many idea rejected in only 2 years of work (so far). There are always reasons (both valid and invalid) to reject a project. Where it gets me mad is the communication back and forth. If the C-Suite would communicate back down on why a project is rejected, I think it creates more knowledge to make better recommendations.

    Unfortunately, I dont think this happens enough because of a variety of reasons. I often always ask why a project isnt accepted, and probably only get a reason half of the time, But in those half of the times, they often help me construct a better recommendation in the future which better fits the company's strategy.

  3. Fully agree, Kevin. Risk of being wrong in particular is a powerful driver of decision-making (and rightly so). We need data-driven insights, but as you point out, the data needs to be right, and this is why our data mining / predictive analytics results always need to be vetted by domain experts. Always. Algorithms don't have common sense, and they naively believe the data.

  4. Hi Kevin,

    This is a great perspective into the world of decision makers. In my experience the biggest challenge that I see is #1 that is mentioned in your list. Data accuracy. In the world where there are more than 1 source people always tend to compare data across sources and this causes more confusion than benefit. People tend to spend time fighting about the accuracy of the data than anything else. I strongly feel that "direction over precision" should always prevail. If you don't have anything to bench mark against then tis hard.

    Most of the executes that are in their position are there because they are good at something or lots of things. Its a culture shift where

    1. People need to start looking at data for decision making
    2. People need to start focusing on the precision but focus more on direction.

    Thanks for sharing great deal of details.



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