In real life, you take into account three sets of data. You know certain routes will not be a good choice. Traffic will build up by the time you get to that location yet maps says its clear. How do you know? If it rains, you adjust. There is no data except a theory that some certain routes will not work for you. It is all memorization of past events or do you have a feeling about certain things? Maybe those feelings are theories.

In business, you are more likely to use data of the past. Theories sound like business school professors who never had a real job, right?

IMO, Theories are statements of causality. Something might happen. You’re reducing the probability of something happening while increasing the likelihood of success. If you have company series with data of the past, You are well on your way to having the makings of an operating system. If you’re trying to drive that three hours today you need an operating system to get there. Many things take place along that ride and in my opinion, it’s three sets of data along with the operating system that can determine which sets of data to use when.

But why do so many businesses Downplay theory? I believe theories are a very different set of math and logic, not taught in US schools. — not well. You just can’t mix the two easily. Excel does not have those functions. Imagine mixing poetry with accounting. It’s had to do.

Regardless, exceptional businesses add predictive theories to their models and operations. Its smart, it works and its time for more to do it.

How can technology deliver happiness to the organization and its customers?

My mission is to seek a ‘balance of delight’ between the people within the organization and its customers. Without this balance, things are temporary.

Throughout my 23 years, I have helped build organizational capabilities to integrate, disrupt and scale revenue by finding and implementing a technology core.

By converting business theories into practical answers for organizations, understanding the people, product, customers and their needs, a business and its customers can be delighted and happy. An organization that overfocused on certainly is doomed to fail. An organization with no certainty is picked off by competitors. The right OS is designed to avoid the edges.

As Gary Kelly, CEO of Southwest says “all roads lead nowhere if you don’t know where you are going.” You can interpret this number ways. If you’re over focus where you want to go, you’re building a dirt road. If you’re creating more prediction, designed around your capabilities and the demands of customers, you have the ability to scale. I have made it a core belief by combining predictions of the future with real-time data and data from the past. No one version of data is better than the other. Its the combination of different sets that don’t always like to fit together. The overarching operating system that combines them all, as needed, where needed, is what organizations need. I have made an effort to build these systems. First, manually, then by consulting and finally using core technology built into the organization itself. It’s the same way you live life, the way you drive, the way things happen in the real world. We don’t write it down, and in business, we need to start. That core technology begins with who the customer should be and why they buy.

And it’s not consulting. We have to convert business practices into software. I learned from the masters such as David Oreck, who utilized the Sarnoff principles, as well as Clayton Christensen of Harvard. Their theories and practice are part of the way business operating systems should work. They don’t have a technology core but that does not mean you can’t make one.

I helped transform Performics from a 3rd place affiliate marketing technology into the world’s largest search engine marketing company. It became such because I utilized the Sarnoff concepts and scaled search beyond expectations. Brands embraced it, and we grew a company worthy of sale to Google itself.

My company, MakBuzz got 350+ brands online, worldwide, even before the platforms (namely Google itself) was ready. We pushed the limits of what digital was designed for. Built as a direct response medium, digital was and still is limited in driving the complete customer journey. In 2000, we challenged that, broke it and create hundreds of millions in value by 2004. We helped eCommerce establish itself and once drove a sizable amount of eCommerce transactions. But we did not sit still.

At Vodafone, I develop digital best practices in 18 counties. This work touched the majority of their 350 million customers at the time. By 2012, we changed how they measured digital, built global dashboards, created goals, means, and methods, which drove acquisition and how they valued a new customer.

Not content to remain a digital marketing practice, I saw the problems and limitations of digital marketing and moved onto sales intelligence automation and digital transformation. You cant do just one part of the business. The whole is far greater and more interesting.

I helped convert several brands from siloed digital practices into integrated organizations. By understanding the impact of all media to eCommerce, store and call center, I found a way to ‘right size’ the complete customer journey. By 2013, we presented our findings on Google’s main stage in Mountain View and several other venues worldwide. It was then the limitations of digital became apparent. As long as digital eys are cheap, it works. Not so much when the cost of a sale exceeds other means. When looking at CLV, the story gets complicated.

In 2015, I applied our advanced CRM technology at several organizations, discovering something media could never solve — why people buy and what delights customers. By segmenting people based on desire and need, I found a way to bypass media altogether. Creating a look-a-like CRM redefines how big the company is and where to prioritize departments and budgets.

Today, my focus is on running businesses. Many great ideas need sales intelligence and automation and a far better understanding of why people buy. No one part is more significant than any other part of the business. Knowing what to prioritize, And how much can be well-defined by understanding a precise, total addressable market. Not designed for and by media but in a way to solve customer delight and long-term profitability of the organization.

Significant opportunities exist to grow organizations. Most organizations operate at a fraction of capacity (like 20 to 30%) and thus, unlocking that value is happiness for many.

In my career, I have seen moments of pure happiness between the organization and its customers. Those are the times when people talk about what you sell, willingly, without incentive, without fear. The organization roars. It’s loud. Growth and profits come. The best of those times come with predictability. You know where you be in a month, a year and beyond. Confidence and certainty abound. The organization is not ripping people off and found a way to have balance, happiness, and scale.

Is this random? Nope. Organizations that don’t adjust to competitors and customers needs find themselves flat, lost. It’s the classic definition of disruptive innovation.

Understanding the various segments of customers and why they buy, what delights them, we can build an organization that is predictable. That same concept and software define the total addressable market; it defines budgeting and where investment should be spent to yield the most significant results.

To repurpose and distribute budgets and profits based on combining ‘data of the past’ with ‘theories of the future’ is the practical side of the modern organization. A budget is not a yearly event — its based on a genuine, total addressable market that is living and breathing in real time. It is the foundation of where the organization can go.

Just think: your dog knows where the Frisbee will go yet does not know polynomial math. How can the dog predict? Imagine the same for business. Built-in growth OS, not piecemeal point solutions.

Spreadsheets, correlation and ‘data of the past ’….They’re needed. I use them every day.

But I don’t rely on them when it comes to predicting the future.

As an executive, your tasked with predicting the future or more like reducing the odds of failure. If you are guessing, you are more likely to be guessing again a machine or a competitor executive, aided by a computer. That machine is empowered with machine learning, AI or something else your human brain can’t outmaneuver by guessing.

The growth operating system we all want does not exist yet, but it’s getting closer. To get there, you need to balance three data sets asap.

— Data of the past forms a foundation of where you have been

— Real-time data, where are we right now

— Predictive data, not based on the past in any way, shape or form

Today I had an exciting conversation about artificial intelligence. It’s incredible to think in late 2018, data from traffic cameras can change the market share of healthcare companies dramatically. Not because of something that happened in the past but predicting using ways of thinking far beyond a person. In this one example, millions if not billions are at stake.

How much of which kind of data do you need?

Sometimes, depending on who you are and what kind of business you’re in, the organization needs a little bit more of one versus the other.

If there’s no data of the past, whatsoever, you don’t have a choice but to look forward. Maybe you can model something from the past but not likely with too much accuracy. We use to call that assumption.

If you have slow growth or stagnant business, that’s not going many places anytime soon, and with few competitors, you can rely on data of the past. Sadly, that’s the foundation of how schools teach us. Using data from the past models are excellent when it’s a lazy river. Things are changing too quickly. There’s got to be a competitive advantage that is different from the past.

Today it’s very different if you’re in a fast-paced environment. If you’re impacted and disrupted by technology, you’re feeling the pain. People are predicting and connecting things that have never done before. It is a multiplier effect of efficiency and girth.

Plugging in the right operating system at the right time and the right stage is essential to driving the company to its rightful size and volume. It’s also important when creating the right value expectations.

Most companies I helped grow usually start with a ‘data of the past’ business model. Getting people to look at predictive data sets is difficult. It feels unnatural. Excel is such a warmer, more cozy place to be for middle management, who often arm senior leadership with ‘facts.’

By adding in elements of a growth operating system, we combine data of the future with data of the past and sprinkle in a little bit of real-time data. It’s the elements of a pretty good growth operating system. Not perfect but good. Adding in sources of truth and understanding exactly what type of source you are looking at, helps define limitations, anomalies and builds the foundations of an OS.

An example: Very early on I remember pushing a major airline to increase its CPA from $2.50 per ticket to $11.75. I wanted to factor in the early-stage customer journey, as $2.50 was competing with its consolidators, and the result was only adding very few net new customers. My theory was people who were aware of the airline did not shop the airline because of that classic ‘out of sight, out of mind’ issue. Too many options. I had NO DATA to support my claim. Instead, I devised a test, and it worked. Today, over 50% of new customer sales come from a more advanced full customer journey. And it is a system that does not rely on cookies and session IDs to ‘prove’ value.

When we append jobs to be done theory as a driver to defining our total addressable market, a real total addressable market, we have a foundation of an operating system for business. When we know how many people can be customers and should be customers, we can define things like customer journey far better than the limited, linear systems our competitors struggle with today.

Growth operating systems combine complex, multiple sources of data that don’t usually play well together. Smart organizations focus on the right things while others live in fear. It’s time for more people to leap ahead.

I have been studying related work to Jobs to be Done theory, who people implement the theory and what are the results. The theory is well known, there are a lot of variations and options to the process and few results to share from the effects of JTBD.

It’s a great theory and the sophisticated capabilities of some people make it possible for the biggest and best. I want to see startups embrace jobs to be done as a means to get answers and results faster than ever before — even in real time as they make changes and pivot their products to meet the needs of customers who have jobs to be done. How can this get done? Software. It starts not nearly as sophisticated as current abilities but it becomes affordable, accessible and easy to use. Maybe not all the answers but 80% in 1 hr. In 2 hrs, a look-a-like CRM that feeds into your CMS for both web and app development.

A summary and point of view of other methods:

The Forces of Progress

The forces of progress are the emotional forces that generate and shape customers’ demand for a product.

Very true but clusters of people will have far more push than pull, making them easier to convert without too much pain. Segmenting people by traits is a clear way to discover how much push, how much pull. Quantify the segments.

They can be used to describe a high-level demand for any solution for the customers’ JTBD or the demand for a specific product.

Events do create demand. But demand will trigger many options based on how you see the world. Your traits and personality pre-select options. We are not just dumb blobs that buy based on what is shown to us.

An event might trigger a far different result, based upon who you are. What is in your deep ways of thinking, your traits, will define what might be a solution to a job to be done.

Push. People won’t change when they are happy with the way things are. Why would they?

Push is a difficult subject. Many brands push all day long, with price, time based offers, etc. Because people can find better, always, people will change if they can be happier, even if the offer is the same or less. Many products over serve their market. I am happy with Verizon but would be more happy if it find a trustworthy alternative at 30% less cost. It can be a tad slower. I might even deal with a few dropped calls like we all did 15 years ago.

External Pushes– Our circle of influence, such as our children might influence our shopping patterns.

An external push is a classic. Of course that will change a job to be done. We find subtle changes can keep a customer loyal. This example Is a good extreme case but many are subtle. I once helped a company that was intimidating introverted minded customers. It was very subtle use of words and imagery. It did not take much for customers to avoid the company which was otherwise a great fit for so many. In many cases it does not take much to see when you’re leading edge customers are dropping off and not converting anymore, while the mass middle might be happy as clams. By segmenting based on traits you can see problems developed early and stop them before the damage is done.

Internal pushes. These pushes ranged from frustration with the homogeneity of a peer group to parents who wanted experiences for their children that would teach life lessons.

Internal pushes or extern a push is a push.How that push develops is often connected to traits that can be quantified and used to determine the real reason why people make decisions. Having software allows you to quantify in real-time and change the environment before real damage is done. Don’t wait for a survey. There is a time and place for that. For many of us who are trying to build a business and take market share, we can’t buy time, we can’t afford consultants and we need to remove as much bias from our system. Software does not solve it all but having it usually means you have a technology core in which to disrupt the hell out of others with last year’s model.

For me, AI helps most when predictive capabilities are defined well from the beginning.

Judgment to outcome are all based upon defining the problem best at the very beginning. AI, machine learning and any other thing you want to put in front must be defined well or it’s just more junk in the system.

For example, if you’re using cookie-based or session based web data inside of an AI engine, you could be informing the machine poorly. It’s something that it can’t recover from at all. Just because you have data does not mean AI can self correct and discover something great.

By understanding the question and having good predictive theories that are causal in nature, you help create great business decisions that have a solid foundation, resulting in value.

I recently ran a crawl of people and organizations that self-proclaim some understanding of Jobs to be Done theory.

It is hard to pinpoint any one type of organization. The list is diverse and no one organizations has any more than 25 people spelling out ‘jobs to be done’ in their Linkedin profile or website. If you had to pick, it would be, as expected, consultants. A common theme is worldwide work in management consulting. No one organization owns more than 1% of mindshare on this topic. If we count the people within the organization, very, very few people proclaim JTBD in anyway. In total, around 2,500 people can spell out jobs to be done on their Linkedin profile. I would assume the number is higher given some limitations of Linkedin search.

After crawling all the websites of businesses spelling out JTBD theory, I pulled meta-data from each site and created a text file of all text. I counted word and phrases frequency. below are the high indexing words and phrases.

Here is the file.

Here are phases associated with companies found. Keep in mind, people work within the companies. The companies are not necessarily in the JTBD theory business. One surprise was a major manufacturer of peanut butter.

So why do this?

For me, it tells me a few things;

1. JTBD theory is not household, not yet. I want it to be ubiquitous.
2. JTBD has a diverse following but does lean toward consultants.
3. In my research, many people struggle to implement JTBD into different departments, after the work is done.
4. These companies must have ‘issues’ or supply results. For the issues companies, I doubt someone would take on a difficult project like JTBD without having a burning need to solve something.
5. For JTBD to be ubiquitous, it needs a technology core. That has been my passion for some time. It works, it follows the classic process of disruption, starting out basic. We need more people to get this concept or some variations of it. We need more businesses to get the benefit of JTBD.

for more information, please contact me; [email protected]