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Introduction

Articles like this cannot be written without spending a lot of time “in the chair”. To understand the significance of this title, it took me decades of continuous work history to develop and understand the story behind these words. If you want to understand what those things mean, simply read the article.

 

Background

I’ve been sitting in a chair (or standing at a workstation) staring at computer screens since the late 1970’s. During this time, I have been interacting with software to get work done.

Almost all of my time “in the chair” has been spent trying to produce professional work results. I have spent less than 1% of my time “in the chair” playing games or not being focused on work. Obviously, this has been a big mistake because the gamers are now making millions of dollars playing against each other!

The Software I Have Used

During the thirty-year period from 1980 to 2010, I had to use a variety of software to get my work done. The software ranged across many disciplines, including (but not limited to):

• computer-aided design, mapping and visualization (e.g., Autocad, ArcGIS, MapInfo, Tecplot, GraphiC etc);
• computer language compilers (C/C++, Fortran, Pascal, Basic, etc);
• symbolic and computational math programs (Maple, Mathematica, Matlab, etc);
• word processing (Word, Wordperfect, LaTex, etc);
• spreadsheets (Excel, Lotus 123, Quattro Pro, etc);
• presentation software (Powerpoint, custom programs, etc).

All of these tools were used to complete scientific work by drawing maps, processing data, building and running computational models, and writing reports. I became a very proficient user of most of these products. I became a highly skilled programmer in over 10 languages that were used to build computational models, build the model input and write the graphical post-processing engines needed to visualize results.

The funny thing is that I now rarely use any of these products, although I still do a lot of the same type of work. I want to explain how this change has happened and what it means to me as I continue my computational career in modeling, data science, and advanced analytics.

The Software User Experience

If I were to examine the list of software I used to do the work throughout my career, it is easy to see how each tool filled a specific niche. Wordperfect and Word were used for writing reports, Autocad was used for making maps and drawings, while Excel was used for processing data and creating charts and figures. Within each of these domains, I spent at least 10 years becoming an advanced user of these tools.

Some of the tools were replaced over time with better products. For example, LaTex was used to produce scientific reports like this one, instead of using Word. Some tools simply went away because I learned to use other tools to do the same work. As my toolbox of knowledge and skills continued to grow, the number of software tools I used was shrinking.

What this meant for me in terms of software usage was that many of these tools failed to evolve to continue to meet my increasingly demanding needs. In the beginning, I had many years of high user engagement with the stated products. I used many of the software products for most of my working days. Now, my user engagement curves have reached zero for these products. This means that those products have failed me. I am no longer a client of these companies.

In the parlance of product development teams, over time these products lost their stickiness for me. For example, for more than 15 years, I depended upon Microsoft Excel to do an enormous amount of work. I have used it for the simplest of tasks all the way to writing a 30,000 line program to compute results for Plackett-Burman experimental designs. With a built-in scripting language (VBA), Excel was a formidable tool in the data processing and visualization space.

Considering the total scope of my usage of Excel over that time, I was nearly 100% stuck on Excel. My engagement was daily over many years, with the product. Now I use Excel for less than 1% of my total work time. There are many days that go by without me even using Excel. I recently wondered, how could that have happened?

The answer to that question has a lot to do with what I call a “software revolution” which is empowering us to complete more analyses in less time than ever before.

The Stickiness of Tableau

Early in 2008, I started using Tableau. For six years, I used Tableau to replace many of the software tools I mentioned above. I used Tableau for my primary data engine, for visualizing results and for producing graphics for reports. Since the beginning, I have used Tableau during almost every workday, and even on many weekends. I will say that Tableau stuck to me for over 11 years at nearly a daily usage level of 100%.

Why did Tableau essentially wipe-out my Excel usage and become so sticky for me? The answers are numerous but I outline only my top 10 reasons. These reasons address the greatness of Tableau but do not discuss the shortcomings of Excel. For those reasons, read-on.

Tableau is:

1. Fast (read more)
2. Intuitive and fun to use (read more)
3. Flexible and extensible (read more)
4. Capable of connecting to almost every common data source (read more)
5. Stable and continuously undergoing expansion and development (read more)
6. A tool that makes me a better data scientist (read more)
7. A tool that instantly improves my data comprehension (read more)
8. A product that allows me to study, understand and publically publish world-wide results on important topics like global warming (read more)
9. A product that infused me with such enthusiasm that I decided to write over 220 articles on its usage (read more)
10. A paradigm that allows me to teach younger workers how to achieve insights and career goals in a short amount of time (read more).

The Stickiness of Alteryx

In 2014, I started using Alteryx. Similar to Tableau, for nearly six years Alteryx has stuck to me at a daily level of 100%. Last year, I even quantified how much I use Alteryx each day.  That study has allowed me to understand the role that Alteryx now fulfills in my professional career.

Why has Alteryx engaged me at the level of 100%, despite me having access to a variety of data processing technologies, especially within a big data enterprise? The answers are again numerous but here are my top 10 reasons.

Alteryx is:

1. Extremely Fast (read more)
2. Intuitive and fun to use (read more)
3. Flexible and extensible (read more)
4. Capable of connecting to almost every common data source (read more)
5. Stable and continuously undergoing expansion and development (read more)
6. A tool that makes me a better data scientist (read more)
7. A tool that instantly improves my data comprehension (read more)
8. A product that allows me to study, understand and publically publish world-wide results on important topics like global warming (read more)
9. A product that infused me with such enthusiasm that I decided to write over 120 articles on its usage (read more)
10. A paradigm that allows me to teach younger workers how to achieve insights and career goals in a short amount of time (read more).

 

The Complete Story of Stickiness

The observant reader will immediately recognize that the top 10 list for both products is essentially the same, with the exception of a few words and the links included in the (read more) articles. Despite the Tableau and Alteryx companies having different cultures, missions, and communities, they each have achieved rapid growth and high valuation in a short amount of time. The reasons for this have a lot to do with my top 10 lists, but there is more to the story than that.

Both products have been built to be data agnostic and not designed for any industry in particular. What these two things mean is that both tools can easily connect to almost any data source in any industry. Furthermore, the tools themselves allow workers in any industry to rapidly gain insights into the data that they are studying.

This gives both Alteryx and Tableau the capability of being sold worldwide to quantitative workers in any discipline. Both of these tools make working with data so much easier than doing it in Excel. They both also release workers from the data size limitations of Excel. These facts are the reason that my Excel user engagement has gone from 100% to 1%, despite me being a highly trained Excel worker that has used the tool literally from the day it was released.

Both Alteryx and Tableau energize their user base in multiple ways, including giving recognition to people that have become proponents of their products. This naturally occurs because each product makes our work easier, more fun, and gives us the ability to extend our skills beyond where we were when we began using them.

There is a natural excitement and curiously that users of these products experience when they first discover them. That excitement leads to high product engagment through the first few years of usage, as people strive to do better things on their jobs. There is no doubt in my mind that company-sponsored programs such as the Tableau Ambassadors, the Zen Masters, and the Alteryx Aces all have helped the growth of each company.

There is also plenty of extensibility in each product and many ways for people to become product masters. Some people show extraordinary skill and willingness to promote these products because of the energy that they have gained by learning these products. This is a highly motivating tactic used in developing a sticky product because each product user becomes an ad-hoc salesman for each company.

The communities around each product are also fantastic and make learning new tricks and techniques very fast. Each company has well-developed community websites coupled with exciting and engaging annual conferences in the US and abroad. All of these items help keep people connected with the software because solutions to challenging problems can rapidly be discovered. Users also look forward to attending user group meetings, company-sponsored events, and writing about their experiences with these tools.

Both companies listen to their user-base and rapidly respond with new product features and basic improvements. Neither company is satisfied with the state of their products, so they spend a lot of money on research and development. This approach keeps users engaged, invigorated, and excited for the future. This is one of the key secrets of developing sticky software.

Both products allow us to get things done really fast. When you hit the state of “flow” when working with these products, time flies by and work gets done. It is very satisfying to use these tools and people become accustomed to delivering results in short amounts of time compared to other products. People are naturally attracted to methods that make them better. I do not know of too many people that want to go backward in their career, by having to use products from previous decades. People want to get better at what they do, and Alteryx and Tableau allow them to do that.

Possibly the most important reason for stickiness, in my opinion, is that each product is brilliantly designed for the tasks that it was intended to do. Both software packages have been able to remove usage drudgery by offering expertly designed graphical user interfaces that connect us to very powerful data engines.

Alteryx gives us unlimited capabilities in blending disparate data to create custom data sources, as well as using these new data sets to perform predictions. Tableau allows us to beautifully display such data to achieve full data comprehension. For these reasons, both Alteryx and Tableau stick with the people that have learned to use them because they have become dependent on the ease of use, the speed and overall comprehensive nature of the software.

For all of these reasons, both the Alteryx and Tableau companies have managed to develop sticky products. These tools continue to get better because of the excellent staff and leadership at each company.

Finally, it was nearly five years ago when I realized the potential synergy of Alteryx and Tableau and how these products could transform my abilities when working with data to solve business and scientific problems (Figure 1). It only took me about over 1,700 days to write about 250 more articles that explained and proved my vision. As always, thanks for reading!