It is always incredible to see the training, skill, and excellence displayed at the Olympics. However, the passion, focus, and commitment are even more impressive. When working toward a goal, we want to see that our efforts are producing successful change. Yet, too often, the majority of our focus is centered on analytics, expertise, skill, profits, and science. Unfortunately, these tools sometimes ignore other critical requirements for successful change and better decision-making, such as: passion, focus, trust, effort, risk, and commitment.
These elements of successful change are frequently lower priorities because they are difficult to measure and make analysts uncomfortable. For example, decision makers frequently hate considering risk, despite the fact that it is present in almost every issue. Additionally, due to the high levels of uncertainty involved, we are often slow at measuring results in periods of rapid change like a pandemic, inflation, and new innovations.
Change is hard, even when it’s successful change, we are often hesitant to adapt.
As a business consultant, I constantly hear, read advice, and see comments focused on worries, concerns, caution, etc.—basically, all the old paradigms related to achieving business success. In my experience, more attention needs to be given to the areas that are difficult to measure. Some suggestions to accomplish this include:
Positive thinking is vital. A good chance at success requires a balance between reality, paranoia, action, and positive thinking. An interesting tactic is to focus more on how you succeed with some clients than fail with others.
Positive thinking does not necessarily mean avoiding or ignoring negatives. Instead, it involves making the most of the potentially bad situations, trying to see the best in other people, and viewing yourself and your abilities in a positive light.
Create a positive culture. Say please, thank you, and show that you care about people via praise and encouragement.
Accept that operating a small business is a process. Recognize that you will make mistakes. Your goal must be to develop, test, measure, and adapt rather than give up after the first or second problem.
Encourage open communication, a sense of realism, and focus on problem solving. Be sure to constantly assess your situation. Develop expert support and, when appropriate, have discussions with outside and inside colleagues.
Be prepared to pivot quickly. The market changes constantly and so do your customers’ lifestyles. So, you need to be able to shift along with it. By expecting that your market can change from year to year, you’re being proactive in your thinking, and can create flexible plans to adapt to these changes.
Know your sh*t, but be ready to listen. There is extensive research supporting the idea that people don’t change unless they believe in it.So, when given the opportunity to argue your case, try to emphasize the benefits for the other party. It’s well proven that tactics like collaboration, trust, and listening work better in decision making than dictating, lecturing, and proclaiming false expertise.
Develop, test, measure, and adapt. Many plans, forecasts, and proposals are done in a static format with one dimensional analysis and results. They’re usually flawed because we live in a more dynamic and interactive world. For example, branding, marketing, pricing, and operations all must be viewed as an integrated program rather than separate and isolated activities. Similarly, businesses need to have alternatives at the ready, as well as a process in place to adapt. Mistakes will occur, but remember, Steve Jobs got fired and Tom Edison tested thousands of light bulbs before succeeding.
Understand your goals, resources, and risk. In particular, really understand your market analysis, competition, how and why your company is different, and why customers should care. Are you focused on long-term growth or quick profits? While testing alternatives is a great strategy, ensure that you are focused on priorities that you can execute well and that will have the most potential.
Analytics is an incredible tool for improving progress, developing alternatives, and measuring outcomes. However, in order to achieve successful change, it needs to be supplemented with passion, effort, commitment, and focus. Without these, it’s much easier to throw in the towel when things get difficult. You may have been born with the innate skills necessary to win countless gold medals, but without the drive, determination, and dedication to go for it, those natural abilities may not reach their full potential. It’s the passion that pushes you to succeed.
Dr. Shlensky is a graduate of Sloan School of Management at M.I.T. He servedas the president of WestPoint Pepperell’s apparel fabrics business & President and CEO of Sure Fit Products. Having provided counseling to over 2,000 clients, he now focuses on working with select startups and small businesses.
Whether it’s implementing a business strategy or taking a family vacation, we all want to plan accordingly. We try to rely on analytics and intuition. We look at business trends in an attempt to make educated decisions and we check weather forecasts hoping we won’t get stuck in the rain. And, with so much technology and Artificial Intelligence (AI) are our fingertips, the ways in which we can make these assessments are abundant. But, how can we know what the best strategy is? When is Analytics most reliable and when should we ignore technology and stick with our instincts?
When it comes to predictable events, Analytics is fantastic for providing insight and additional analysis. Currently, there is significant hype for new AI tools. GPS, improved forecasting, trend analysis, and selection have all experienced dramatic gains. I am amazed, for example, how GPS systems monitor traffic and predict an arrival time. However, it’s noteworthy to ask ourselves if we’re simply using them for efficiency and ignoring important considerations. This is one of the problems of using analytics and intuition.
There are two questions we must ask when using AI and Analytics:
First, are the assumptions, data, analysis, and conclusions really valid?
Second, do we limit the use of intuition and small measures in using these tools?
One of the biggest issues with AI is that we simply accept the results because they are impressive or too complicated to understand. We need to review the validity of the data, measurement, and analysis.
For example, the pandemic will require adjustments for data analysis. How do you compare changes from 2019 to 2020 and 2020 to 2021? In particular, how do you forecast 2022 and beyond? How important is an annual average and should you use 2019 or 2021? The analysis is highly dependent on issues like assumptions, demographics, time periods, etc. The answers can also be more dependent on a specific situation rather than general rules. Forecasting things like workers going back to the office, students going back to the classroom, airline passenger growth, business meetings, entertainment, and apparel trends all have different parameters.
We frequently just assume cause and effect when the relationship can be nonexistent. Statistics make it very easy to assume that a relationship among factors is a straight line. However, most relationships involve a variety of factors, as shown in the chart below:
Significant issues with analytics and intuition also occur when intuition, risk, and low probabilities produce better results than analytics. We all know the lottery is a bad bet, but some people do win. Similarly, many billionaires like Gates, Bezos, Jobs, and Must have achieved fame by pursuing high-risk and out-of-the-box alternatives. Many analytical recommendations encourage the “most likely” rather than the best alternatives.
More importantly, the reality is that outliers create much of the innovation, excitement, and change in our society. Steve Jobs probably said it best: “The people who are crazy enough to think they can change the world are the ones who do.”
In their new book, Noise, Daniel Kahneman, Olivier Simony, and Cass Sunstein point out how Analytics can fail to include key metrics. For example, mood, bias, mental state, etc. can alter judicial decisions. Variables like hunger, how much sleep we got, and personal preferences can all affect decisions.
While using Analytics based on AI has limitations, here are several suggestions to make it more effective:
Keep the goal in sight to improve your decision-making. The goal of Analytics is to improve decision-making and identify great alternatives. Focusing on satisfying investors, suppliers, employees, etc. is simply an invitation to long-term problems. Similarly, you need to understand the goals, timeframe, and precision in your research. Are you simply trying to make a living in a short time or build a giant business that you know will lose money in the first few years?
The biggest problem with decision-making is bias. Whether we admit it or not, we all have biases. Analysists love to discuss mathematical formulas and measurement in affecting bias; however, most bias (especially in small businesses) is simply human. For example, our most recent experience can have a significant impact on decisions.
Keep it simple. Simplify wherever possible. Focus on factors that really affect your business so you can understand them and estimate factors that are not as significant. For example, look at aggregate costs and administrative expenses rather than trying to forecast small items like telephone, utility, and insurance costs.
Be more open. Organizations need to be open to measurement and feedback. Observing, understanding, and sharing financials, operations reports, and sales reports is the first step.
Develop, test, measure, and adapt. Many plans, forecasts, and proposals are done in a static format with one-dimensional analysis and results. Often, these end up being flawed because we live in a more dynamic and interactive world. For example, branding, marketing, pricing, and operations must all be viewed as an integrated program rather than separate and isolated activities. Remember the 80-20 rule, which states that 80% of your sales will come from 20% of your products and/or customers. Are you measuring your sales, key items, and customers?
Embrace change. Don’t just talk about change. Take action! Responding to disruptive change like the pandemic requires finding a way to incorporate data, analysis, and pre-existing models while also embracing out-of-the-box thinking and flexibility.
Don’t neglect key elements of success. Operations, customer service, and logistics are just as important as traditional functions.They present huge opportunities for a business to become more efficient and differentiate itself (i.e. selling on Amazon or bundling products).
Relax. You can’t do everything in one day. Pace yourself and remember that there will always be uncertainty and change. Stay focused and take it one day at a time.
Always be willing to improve. What are your biggest challenges? Where are you overlooking potential opportunities? In what areas could you do better? Remember: more Analytics is generally useful for small businesses; however, one must be sure the foundation, reliability, data, and processes of the Analytics have a firm base.
Understand diversity. Demographics are affected by age, location, socioeconomic status, race, gender, etc. Current events have certainly affected trends relating to racial and female groups. Staying up-to-date on your target consumer and their habits will help inform your decisions. Do you know who your customers are and what demographics they belong to?
Analytics provides astute insights for business decisions and should not be underestimated. However, its value is highly dependent on how effectively it is used and the recognition that intuition is still an important factor. In particular, the more creativity and uncertainty involved in any given situation, the more intuition will be required. It is important to use both analytics and intuition.
Contact us for a FREE evaluation and get an alternative perspective on your business. We’d love to help you identify ways to adapt to current trends. No one has time for BS—so we’ll cut straight to the point and answer any questions you have. Reach us at:
Dr. Bert Shlensky, President of StartupConnection.net, has an MBA and PhD from the Sloan School of Management at M.I.T. He served as the President of WestPoint Pepperell’s apparel fabrics business & President and CEO of Sure Fit Products. More than 2,000 clients have benefitted from his business acumen over the course of his long career. He now focuses on working with select startups and small businesses. Please visit our website: www.StartupConnection.net for more information.
Seems
a bit like an oxymoron, no? Well, that’s exactly what analytics have become
these days: an oxymoron. A real conundrum. On one hand, data helps us predict
change and plan for the future. On the other, that data can be wrong or
misleading and, therefore, really screw things up. So, I say, take it all in,
but then let (most of) it go.
There’s an ongoing debate regarding the roles of data and entrepreneurship. In particular, the increased availability of analytics data and tools is making planning, scheduling, and analysis much simpler and more accurate. Amazon is one of the best examples of using analytics to improve logistics (i.e. more one-day shipping).
In contrast, the argument stands that these tools are less effective than originally expected. The most significant instances are incorrect data, method, and change. If the data is wrong, access to more data does not improve analysis. Mistakes like Boeing, Afghanistan, WE WORK, G.E. and retail stores represent diverse examples where people simply focused on wrong information. The existence and use of the phrase “alternative facts” supports the unnerving idea that it’s easier to make up lies than it is to refute those lies. That alone does not bode well for analytics and data.
Data can also be misleading when a dramatic change occurs.
Disrupters like E-Commerce, ride share apps, and food delivery dramatically
affected markets and parameters. Consequently, significant shifts in culture,
politics, and buying habits also make economic forecasting much less reliable.
Additionally, analysis is dependent on using the right
tools and methods. Many assumptions and approaches may not be appropriate. For
example, investment advisors frequently tout their individual excellence while
changes in the overall market are usually the largest factor in investment
success. Mathematics shows that the more history one has on a topic, the more
accurate the analysis. However, if parameters change, history may become
irrelevant.
This is why we take it all in. Think on it. Absorb it. Let
it all sit for a bit. And then throw most of it out the window.
You should absolutely consider what they teach on the first
day of a statistics course (Validity, Reliability, and Accuracy) rather than
ignore it.
A recap in case you need a refresher:
Validity is simply focusing on whether
your methods are valid. While sampling, correlation, and other tools can improve
performance, the analysis must be valid. For example, many of us predict that
our team will win. However, the odds in most professional leagues are that
about 3% of approximately 30 teams will actually win.
Reliability is the repeatability
of results. Differing results in political polls or verifying results of
medical tests are examples of reliability issues.
Accuracy is just the correctness of
the measurement process. The most violated rule of accuracy is that you are
only as accurate as your least accurate number. There is a famous story about a
museum guard answering a child’s question about how old a dinosaur was. He said
280 million years plus 39 years and 20 days. When asked where the number came
from, he said, ”When I started, they told me it was about 280 million years
old. I have been here 39 years and 20 days.” While this number certainly seems precise, it probably isn’t very accurate.
I would add a fourth factor to this list, which is probably
the most important: Bias. On one hand, bias is a complex mathematical
term correlated with sampling, randomness, analysis, and other things. On the
other, it is how our culture, background, gender, age, and preconceptions etc.
affect our attitudes and decisions. For example, many studies have shown that
we form an opinion about a presentation within 90 seconds of it starting. I
highly recommend that, in dealing with bias, you manage its existence rather
than trying to deny it.
Finally, tools as well as methods of reporting are
dramatically changing. A colleague of mine recently challenged my website saying
it was “too dependent on PowerPoint and Excel.” While these are both great
tools and are the most dominant analytical and presentational methodologies,
they can have many limitations: The information can be old, longitudinal
analytics is frequently lacking, they are not interactive, they are not visual
enough, and they can be very boring and/or misleading. Nothing is worse than being
forced to sit through a PowerPoint presentation that is too long and loaded
with endless Excel sheets.
In summary, analytical tools offer great potential for success, but they need to be utilized properly and in conjunction with intuition to be effective. So, gather all that data and pay close attention to it, but don’t be afraid to toss it all out.
Now, before anyone gets up on a soapbox with an opinion
about whether or not stereotyping is “politically correct,” let’s just take a
step back. Of course there are bad stereotypes—ones that cultivate hate,
encourage inequality, and perpetuate racism. This article is not about those. That type of stereotyping is
ignorant, misinformed, and detrimental to society as a whole, in addition to
being harmful to your business.
The stereotyping
we’re dissecting today relates to trends and analytics. The bottom line is: negative
and harmful stereotyping stems from ignorance, assumptions, fear, and
misunderstanding, while a healthy stereotype comes from research data and an
analytical point of view.
A large part of marketing revolves around segmenting and focusing on selected consumer groups. The “stereotype” that older people are less likely to utilize technology is a helpful bit of information (supported by research) that may influence your marketing strategy, especially if your target audience is over the age of sixty (of course, there are always exceptions to the rule, but you can see where I’m going with this…) This demographic is also more interested in things like adult diapers, medical services, and reverse mortgages. They may not even understand things like streaming services, apps, or YouTube. These may sound like generalizations, but these particular stereotypes, when supported by data, are useful to your marketing strategy.
When You Stereotype
Others
Stereotypes, traditionally, have been used to divide people.
They create an “us and them” mindset. However, I argue that stereotypes can be
used for good if they come from an attempt to unite people and find
commonality. For example, a
recent study showed that teachers were more effective with students who shared common
demographics like sex and race. Educators can use that information to find the
best academic fit when they are seeking employment.
Another
positive way to utilize stereotypes is to find ways to relate to others. In
business particularly, creating rapport with investors, customers, co-workers,
or vendors is an important element to success. Finding common ground—whether that’s
background, hometown, religion, etc.—may help you connect with others. We infer
things based off of what we know about others. So, you might ask a person from
Chicago if they’re a White Sox or Cubs fan. The assumption that he or she is,
perhaps, a fan of a particular sports team based off their hometown isn’t
offensive in any way and it may spark a conversation about rival teams.
A
common stereotype, that I find beneficial, is considering the implications of
whether someone is “right brained” or “left brained.” In particular is someone
more creative or intuitive (right brain) or rational and analytical (left
brain). Factors like fact, logic, emotion, and passion can vary depending on
the audience and situation. These two types generally excel at very different tasks
and have specific ways in which they work best.
When Others Stereotype You
You can
argue all you want, but looks matter. Studies have shown that it takes just 30
seconds for someone to form an opinion about another person upon first meeting
them. We’ve all heard it before, but how we choose to present ourselves makes a
difference. And like it or not, people will make assumptions and stereotype you
based off what you wear and how you look. It may work for or against you, but
the key is knowing that it will happen and working to present yourself in the
way you’d like to be perceived.
When it comes to business, I suggest knowing your goals,
understanding your consumer’s needs, and keeping your audience’s perceptions in
mind. Perceptions are imperative.
This includes perceptions of you, your product/service, your brand, your
marketing, etc. Whether you’re selling a product, developing a relationship, or
impressing an audience, you need to consider how your and your message come
across, what assumptions people will make, and the impression you want them to
walk away with. You can’t control what people think, but you have the power to
influence their inferences.
Have you ever been a victim or beneficiary of
stereotyping? What stereotypes have been applied to you? Were they offensive?
Have you ever judged a book by its cover and been wrong?
I’d especially love to hear how stereotypes have helped you develop more effective messaging. Contact me today, and let me know your thoughts.
Dr. Bert Shlensky, President of The Startup Connection, directs all small business clients toward maximum sales and profit thanks to his 40 years of high-quality experience. Though technological, social, and online integration, he can help launch your business to the next level.
There are two seemingly conflicting trends in organizations regarding technology and innovation. The first is a trend towards autonomy, which focuses on organizational goals, as well as cooperation and empowerment. The second is a trend towards automation, which simplifies work requirements and can result in fewer workers. I argue you can have both autonomy and automation… You simply need to focus on improving the autonomy at all levels as you increase the automation.
The autonomy approach is described by Fred Kofman, who promotes cooperation and voluntary exchange for mutual gain. According to this theory, motivation, culture and collaboration produce better solutions than pure self-interest. In short, organizations should focus on winning for the organization, and not just the individual silos of participants.
Organizations in Silicon Valley often devote their attention to things like automation and AI. However, they are held accountable for the trend that some jobs are being replaced by robots. This includes jobs like taxi drivers (replaced by self-driving cars), hedge fund managers (replaced by algorithms), or financial journalists (replaced by chatbots).
This idea was brought home to me a few weeks ago, during a visit to an 1850’s restoration community Sturbridge Village. They had little cottages doing various tasks to make clothing (like cleaning wool, spinning, weaving and sewing). The work that went into production of a few yards or one shirt was incredible. In contrast, my experience in the apparel industry was that we could weave millions of yards in short periods, thanks to automation.
Similarly, Google and others are developing AI programs to write and develop artistic works. They argue that this technology will greatly enhance an artist’s ability to create, while others argue that it will just replace artists. My own experience in the apparel industry is that automation greatly enhances the artist’s potential by reducing mundane tasks. Instead of it regretting the displacement caused by automation, we need to focus more on realizing its potential for individuals. For example:
Don’t let automation or analytics give you one simple answer. Programs and situations are diverse, and require a variety of solutions. A great example is the success of the Golden State Warriors and LeBron James in basketball. The Warriors win by an integrated team that gets the ball to the open man, and passes more than any team in history. LeBron’s teams have won by making LeBron the focal point, and supporting with complimentary plays and personnel.
Similarly, organizations need to consider their goals and processes. Do you need more expertise and experience, or more creativity? Are you maximizing the potential of your stars and developing collaborative solutions? Do you need diverse expertise on a problem?
Most people I see working care about their jobs and try to do them well, regardless of pay or status. A very simple recommendation is just to consider how can we can empower our staff to do even better. We should acknowledge that there will be mistakes, but they will be far less than the total gains.
“Need to know” should be a dead phrase, so help staff understand goals and strategies. The more we trust staff to understand these strategies, the more likely they are to embrace them.
I believe “leadership” is an obsolete term. The best leaders I have seen are people like head nurses, restaurant expediters, triage managers, and legal assistants. They coordinate and manage various (and frequently much higher paid) participants. The process involves gaining their cooperation and motivation to execute a great final result. In contrast, authoritarian (rather than expert or professional leadership) is usually more harmful than helpful.
Many financial and analytical models focus on a single or best solution. I recommend focusing on the parameters of alternative models. Then you can manipulate the model to evaluate alternatives. For example, we have developed a dynamic operating profit model that allows you to analyze the interaction and impact of various factors like price, cost, margin, distribution marketing etc. It has been effective in helping clients understand retail and online opportunities. Download it here.
In summary, automation and AI offer great opportunities to improve performance, especially when used with analytics. These strategies should also include empowering the organization. In particular, we should continuously challenge assumptions, review alternatives and evaluate progress.
Dr. Bert Shlensky, president of Startup Connection ( www.startupconection.net ) has an MBA And PhD from the Sloan School of Management at M.I.T. He served as the president of WestPoint Pepperell’s apparel fabrics business and President and CEO of Sure Fit Products. Having provided counseling to over 2,000 clients, he now focuses on working with select startup and small businesses.
When you want to stand out, reach out to Bert for the tools that will build your “sticky” brand. My focus is on understanding and analyzing your dilemmas and challenges, so your company becomes profitable faster.
Call (914) 632-6977 or email me at bshlensky@startupconnection.net. Don’t leave without signing up for our useful free eBook!
Feeling stumped or overwhelmed? Contact Bert at (914) 632-6977 or Email to start the process. Thanks!
There are two seemingly conflicting trends in organizations regarding technology and innovation. The first is a trend towards autonomy, which focuses on organizational goals, as well as cooperation and empowerment. The second is a trend towards automation, which simplifies work requirements and can result in fewer workers. I argue you can have both autonomy and automation… You simply need to focus on improving the autonomy at all levels as you increase the automation.
This idea was brought home to me a few weeks ago, during a visit to an 1850’s restoration community Sturbridge Village. They had little cottages doing various tasks to make clothing (like cleaning wool, spinning, weaving and sewing). The work that went into production of a few yards or one shirt was incredible. In contrast, my experience in the apparel industry was that we could weave millions of yards in short periods, thanks to automation.