Click (not the one you think) to success

Click book coverIn my experience, the most important factor for success in business is the ability to interact well with other people. Leadership skills, financial skills and technical skills all matter a lot, but they don’t amount to a hill of beans without solid people skills.

The reality is none of us can be successful completely on our own. We need the help of other people — be they peers, staff, managers, vendors or business partners — to successfully accomplish our tasks and goals.

Human relationships are more complicated than Wall Street financial schemes, but we often take interpersonal skills for granted. We rarely study them to the degree we study financial or technical skills. After all, we’ve been talking to people all our lives. We’re experienced. But I’ll argue there are subtleties that make all the difference, and they’re worth studying.

In my opinion, the best business book ever written is How to Win Friends and Influence People by Dale Carnegie — and it’s actually not even classified as a business book. I’ve never read a better guide to the basics of interacting effectively with people.

But I just finished a book that will take its place nicely alongside the Carnegie classic on my bookshelf.

Click: The Magic of Instant Connections by Ori and Rom Brafman (authors of Sway, one of my favorite books from last year) explores the factors or “accelerators” that exist when people “click” with each other. We’ve all had those instant connections with people in our lives, and those types of connections generally lead to powerful and productive relationships. While the Brafmans dig into both the personal and business nature of those connections, for purposes of this post I’ll focus on the business benefits of understanding and fostering such connections.

The book covers a wide range of connection accelerators, more than I could ever cover in this space, so I’ll just address a few that really stood out to me:

Proximity
Simple physical proximity can make a huge difference in our ability to connect with others. A study of a large number of military cadets found that 9 of 10 cadets formed close relationships with the cadets seated directly next to them in alphabetical seat assignments. Another study found that 40% of students living in randomly assigned dorms named their next-door neighbor as the person they most clicked with, but that percentage dropped in half when considering the student just two doors away. Maybe more startling, the students who lived in the middle of a hall were considerably more likely to be popular than those living at the end of a hall.

Why?

The authors explain that these connections are often driven by “spontaneous conversation…Over time, these seemingly casual interactions with people can have long-term consequences.”

I think many of us have instinctively understood the value of placing working teams in close proximity to each other. I’ve personally always attributed that value to the working conversations that are overheard and allow various member of the team to better understand and communicate issues about the work. But maybe that close proximity is also allowing people to better connect with each other. Maybe those connections allow us to better relate to each other and give each other the benefit of the doubt. Looking back at my career, I can think of many instances where office moves have coincided with strengthening or straining my working relationships with people.

Proximity is more important than I ever thought. We should carefully consider office layouts to foster the right types of connections. If close proximity is not possible for certain teams or people, we should understand the negative effects of separation and look for other ways to foster the connection.

Resonance
Resonance “results from an overwhelming sense of connection to our environment that deepens the quality of our interactions.” Huh? For example, the book reports that we’re 30 times more likely to laugh at a joke in the presence of others than if we hear it alone. My friend and colleague Jeff Dwoskin moonlights as a stand-up comedian, and he once explained to me that the difference between a good comedy club and a bad comedy club is the arrangement of audience seating. When tables are close together, people laugh more. When there are lots of booths that separate the audience into tiny groups, it’s much harder to get a laugh and keep the funny going.

Many companies swear by their open seating arrangements. Rich Sheridan, founder of Ann Arbor-based Menlo Innovations, seats his agile development teams on open tables together. No cubes. No walls. He says it’s a huge key to their success. Does that work for everyone working team in all situations? I doubt it. But certainly working environments have impact on working relationships and their resulting productivity, and resonance is a concept worth considering.

Similarity
“No matter what form it takes, similarity leads to greater likability…Once we accept people into our in-group, we start seeing them in a different light: we’re kinder to them, more generous.”

Kinder. More generous. Those sound like good bases for effective working relationships. It’s amazing how finding common ground can bring teams closer and help them work more effectively together. Sure, those of us working for the same company in the same industry all have industry and company in common, but it seems like the more personal similarities are more likely to bring people together. For that reason, we should encourage water cooler chats and other personal interactions in the work place. Everything in moderation, for sure, but a little personal time can actually end up improving productivity by reducing stress and misinterpretations that lead to unproductive miscommunications. The book reports that a “Finnish health survey conducted on thousands of employees between 2000 and 2003 revealed that those employees who had experienced a genuine sense of community at work were healthier psychologically.”

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“Common bonds and that sense of community don’t just foster instant connections — they help to make happier individuals.” The Brofmans provide numerous examples of teams that performed significantly better than others primarily due to the interpersonal dynamics of their members. We simply cannot succeed in life without the support of other people. It’s worth taking the time to understand how to improve those relationships for the betterment of all parties. And pick up Click, it’s well worth the read.

What do you think? Is this all hogwash? Do you have stories of how personal relationships have led to success in your life?

11 Ways Humans Kill Good Analysis

Failure to CommunicateIn my last post, I talked about the immense value of FAME in analysis (Focused, Actionable, Manageable and Enlightening). Some of the comments on the post and many of the email conversations I had regarding the post sparked some great discussions about the difficulties in achieving FAME. Initially, the focus of those discussions centered on the roles executives, managers and other decisions makers play in the final quality of the analysis, and I was originally planning to dedicate this post to ideas decision makers can use to improve the quality of the analyses they get.

But the more I thought about it, the more I realized that many of the reasons we aren’t happy with the results of the analyses come down to fundamental disconnects in human relations between all parties involved.

Groups of people with disparate backgrounds, training and experiences gather in a room to “review the numbers.” We each bring our own sets of assumptions, biases and expectations, and we generally fail to establish common sets of understanding before digging in. It’s the type of Communication Illusion I’ve written about previously. And that failure to communicate tends to kill a lot of good analyses.

Establishing common understanding around a few key areas of focus can go a long way towards facilitating better communication around analyses and consequently developing better plans of action to address the findings.

Here’s a list of 11 key ways to stop killing good analyses:

  1. Begin in the beginning. Hire analysts not reporters.
    This isn’t a slam on reporters, it’s just recognition that the mindset and skill set needed for gathering and reporting on data is different from the mindset and skill set required for analyzing that data and turning it into valuable business insight. To be sure, there are people who can do both. But it’s a mistake to assume these skill sets can always be found in the same person. Reporters need strong left-brain orientation and analysts need more of a balance between the “just the facts” left brain and the more creative right brain. Reporters ensure the data is complete and of high quality; analysts creatively examine loads of data to extract valuable insight. Finding someone with the right skill sets might cost more in payroll dollars, but my experience says they’re worth every penny in the value they bring to the organization.
  2. Don’t turn analysts into reporters.
    This one happens all too often. We hire brilliant analysts and then ask them to spend all of their time pulling and formatting reports so that we can do our own analysis. Everyone’s time is misused at best and wasted at worst. I think this type of thing is a result of the miscommunication as much as a cause of it. When we get an analysis we’re unhappy with, we “solve” the problem by just doing it ourselves rather than use those moments as opportunities to get on the same page with each other. Web Analytics Demystified‘s Eric Peterson is always saying analytics is an art as much as it is a science, and that can mean there are multiple ways to get to findings. Talking about what’s effective and what’s not is critical to our ultimate success. Getting to great analysis is definitely an iterative process.
  3. Don’t expect perfection; get comfortable with some ambiguity
    When we decide to be “data-driven,” we seem to assume that the data is going to provide perfect answers to our most difficult problems. But perfect data is about as common as perfect people. And the chances of getting perfect data decrease as the volume of data increases. We remember from our statistics classes that larger sample sizes mean more accurate statistics, but “more accurate” and “perfect” are not the same (and more about statistics later in this list). My friend Tim Wilson recently posted an excellent article on why data doesn’t match and why we shouldn’t be concerned. I highly recommend a quick read. The reality is we don’t need perfect data to produce highly valuable insight, but an expectation of perfection will quickly derail excellent analysis. To be clear, though, this doesn’t mean we shouldn’t try as hard as we can to use great tools, excellent methodologies and proper data cleansing to ensure we are working from high quality data sets. We just shouldn’t blow off an entire analysis because there is some ambiguity in the results. Unrealistic expectations are killers.
  4. Be extremely clear about assumptions and objectives. Don’t leave things unspoken.
    Mismatched assumptions are at the heart of most miscommunications regarding just about anything, but they can be a killer in many analyses. Per item #3, we need to start with the assumption that the data won’t be perfect. But then we need to be really clear with all involved what we’re assuming we’re going to learn and what we’re trying to do with those learnings. It’s extremely important that the analysts are well aware of the business goals and objectives, and they need to be very clearly about why they’re being asked for the analysis and what’s going to be done with it. It’s also extremely important that the decision makers are aware of the capabilities of the tools and the quality of the data so they know if their expectations are realistic.
  5. Resist numbers for number’s sake
    Man, we love our numbers in retail. If it’s trackable, we want to know about it. And on the web, just about everything is trackable. But I’ll argue that too much data is actually worse than no data at all. We can’t manage what we don’t measure, but we also can’t manage everything that is measurable. We need to determine which metrics are truly making a difference in our businesses (which is no small task) and then focus ourselves and our teams relentlessly on understanding and driving those metrics. Our analyses should always focus around those key measures of our businesses and not simply report hundreds (or thousands) of different numbers in the hopes that somehow they’ll all tie together into some sort of magic bullet.
  6. Resist simplicity for simplicity’s sake
    Why do we seem to be on an endless quest to measure our businesses in the simplest possible manner? Don’t get me wrong. I understand the appeal of simplicity, especially when you have to communicate up the corporate ladder. While the allure of a simple metric is strong, I fear overly simplified metrics are not useful. Our businesses are complex. Our websites are complex. Our customers are complex. The combination of the three is incredibly complex. If we create a metric that’s easy to calculate but not reliable, we run the risk of endless amounts of analysis trying to manage to a metric that doesn’t actually have a cause-and-effect relationship with our financial success. Great metrics might require more complicated analyses, but accurate, actionable information is worth a bit of complexity. And quality metrics based on complex analyses can still be expressed simply.
  7. Get comfortable with probabilities and ranges
    When we’re dealing with future uncertainties like forecasts or ROI calculations, we are kidding ourselves when we settle on specific numbers. Yet we do it all the time. One of my favorite books last year was called “Why Can’t You Just Give Me the Number?” The author, Patrick Leach, wrote the book specifically for executives who consistently ask that question. I highly recommend a read. Analysts and decision makers alike need to understand the of pros and cons of averages and using them in particular situations, particularly when stacking them on top of each other. Just the first chapter of the book Flaw of Averages does an excellent job explaining the general problems.
  8. Be multilingual
    Decision makers should brush up on basic statistics. I don’t think it’s necessary to re-learn all the formulas, but it’s definitely important to remember all the nuances of statistics. As time has passed from our initial statistics classes, we tend to forget about properly selected samples, standard deviations and such, and we just remember that you can believe the numbers. But we can’t just believe any old number. All those intricacies matter. Numbers don’t lie, but people lie, misuse and misread numbers on a regular basis. A basic understanding of statistics can not only help mitigate those concerns, but on a more positive note it can also help decision makers and analysts get to the truth more quickly.

    Analysts should learn the language of the business and work hard to better understand the nuances of the businesses of the decision makers. It’s important to understand the daily pressures decision makers face to ensure the analysis is truly of value. It’s also important to understand the language of each decision maker to shortcut understanding of the analysis by presenting it in terms immediately identifiable to the audience. This sounds obvious, I suppose, but I’ve heard way too many analyses that are presented in “analyst-speak” and go right over the heard of the audience.

  9. Faster is not necessarily better
    We have tons of data in real time, so the temptation is to start getting a read almost immediately on any new strategic implementation, promotion, etc. Resist the temptation! I wrote a post a while back comparing this type of real time analysis to some of the silliness that occurs on 24-hour news networks. Getting results back quickly is good, but not at the expense of accuracy. We have to strike the right balance to ensure we don’t spin our wheels in the wrong direction by reacting to very incomplete data.
  10. Don’t ignore the gut
    Some people will probably vehemently disagree with me on this one, but when an experienced person says something in his or her gut says something is wrong with the data, we shouldn’t ignore it. As we stated in #3, the data we’re working from is not perfect so “gut checks” are not completely out of order. Our unconscious or hidden brains are more powerful and more correct than we often give them credit for. Many of our past learnings remain lurking in our brains and tend to surface as emotions and gut reactions. They’re not always right, for sure, but that doesn’t mean they should be ignored. If someone’s gut says something is wrong, we should at the very least take another honest look at the results. We might be very happy we did.
  11. Presentation matters a lot.
    Last but certainly not least, how the analysis is presented can make or break its success. Everything from how slides are laid out to how we walk through the findings matter. It’s critically important to remember that analysts are WAY closer to the data than everyone else. The audience needs to be carefully walked through the analysis, and analysts should show their work (like math proofs in school). It’s all about persuading the audience and proving a case and every point prior to this one comes into play.

The wealth and complexity of data we have to run our businesses is often a luxury and sometimes a curse. In the end, the data doesn’t make our businesses decisions. People do. And we have to acknowledge and overcome some of our basic human interaction issues in order to fully leverage the value of our masses of data to make the right data-driven decisions for our businesses.

What do you think? Where do you differ? What else can we do?

Retail: Shaken Not Stirred by Kevin Ertell


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