Data can be represented in so many forms, but its proper use can make a major difference in an organization’s productivity. Following is an op-ed piece highlighting the importance of data and its use in serving the chess community. The era of “big data” is upon us and being able to make use of it creates a steady flow of good information by which chess policymakers can make timely and more principled decisions.
Chess is a data-driven activity. We spend hundreds (even thousands) of dollars on subscriptions, books, and software to improve our skills. We analyze databases to prepare for opponents. We resort to chess engines to get an idea of optimal choices. We also play on chess servers to test this data. Data has become an integral part of chess and knowing how to use it properly can make a difference in efficiency and progress. We are in the era of “big data.”
What is “Big Data”?
Data (plural) are raw facts contained in historic archives, audiovisual media, statistical databases, computer files, and other storage formats. These data are taken from these places and processed using some type of statistical or analytics software after which it becomes usable information. In the current era, we rely heavily on access to data for many of our day-to-day activities. Good data has to have certain characteristics if we are to be confident in its reliability. The following infographic gives an idea of data characteristics:
In the era of big data, companies are integrating it as a major part of their supply chain to increase productivity and profitability. Others are using it for targeting new customers while improving the retention of old customers. Companies are enlisting analytic consultants, purchasing analytics programs for customer relationship management (CRM), and setting up entire departments to handle the increasing demands on data. There is a precedent.
In the 60s and 70s, the term “data processing” was a bit broad, but the outdated term was meant to cover a wide range of tasks in information technology. It has made a comeback to mean something very specific. There is obvious importance to understanding the qualitative and quantitative value of data. With the emergence of e-Commerce and its use as a dominant platform in the 1990s, organizations are required to be more responsive and adaptable to market trends. For non-profit membership, the goal may be similar.
“Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming.” — Chris Lynch
Data Processing’s Comeback
For an organization, maintaining close ties to the members and ensuring that services are provided is an essential part of organizations’ strategic plan, big and small. While “followers,” “friends,” and “subscribers” represent a gold standard of social media relevance, decision-makers are discussing “churn rates,” “click-throughs,” and “conversion rates” of websites. In addition, some key performance indicators (KPIs) used on the e-Commerce platform are relatively new. While the revenue from membership is invaluable, the data from the members may be just as valuable.
For some organizations, there is a bit of hesitancy to deal with mounds of data. Some organizations feel it is unnecessary and not within the scope of their organizational needs. Others believe it simply takes specialized skills to make use of analytics programs on the market today. Whatever the reason, it is becoming more apparent that organizations increasingly rely on data, its accuracy, and the ability to turn queries into meaningful information. In addition, platforms like Tableau and Power BI are powerful tools that give organizations a much more comprehensive view of membership dynamics.
Looking at the U.S. Chess annual reports, one is impressed by the professionalism of the statements (and annual report), the beauty of the vintage photographs, and the progress of the organization. In addition, when looking at the Delegates Report, there are entries by the various committees such as College Chess, Ethics, Election, FIDE, and Ratings to name a few. Membership is a prominent factor in the operating budget of U.S. Chess.
There was reported an upward membership trend peaking in 2020, but it declined due to the pandemic and the suspension of over-the-board tournaments. Upon closer inspection of membership reports, it is obvious that so much more than the basic demographic data trends are presented. Being able to target more efficiently for recruitment, increase retention and reduce attrition, are some pressing issues.
The Case of U.S. Chess
Collecting data: What and how much data is important?
At this time, U.S. Chess employs basic research methods for collecting and processing its members. Data collected from membership applications (besides the name and identification number) are birthdate, gender and one’s address (geography). There is also the type of membership desired. In a membership report published by Dr. Alexey Root on ChessBase.com, the following demographic stats were given.
(12/31/2020 or later expiration date), 12.4% are coded as female, 85.4% are coded as male, and 2.2% are uncoded (M. Nolan, personal communication, February 23, 2021). From 1972–2000, that percentage was 5% or lower.
Al Lawrence, Executive Director of USCF from 1988-1996, wrote a memorandum, distributed to the USCF Policy Board and the Women’s Chess Committee, about “Female Membership in USCF.” The memorandum is listed internally at USCF as “100 BINFO #93-397” with a date of October 22, 1993. There’s a hand-written note about some members not being coded for age or gender, affecting the percentages. According to his memorandum, in 1993 girls and women combined were 4.65% of the total USCF membership (3,340 of 71,794 members). Of those 3,340 females, only 612 were women ages 21 or older. In other words, adult women were .0085 of the total USCF membership in 1993.
This report seemed to be making the point of the abysmal membership numbers of women and girls. There are data up to May 2022 showing membership at 82,177. The peak pre-pandemic was in February 2020 at 94,948. Of course, the numbers are clear, but one of the problems is understanding these numbers is finding out why. Despite the success of the miniseries “Queen’s Gambit” and the explosion of Twitch, membership suffered. Was there any initiative that could’ve prevented the loss of 12,000 members during the pandemic? It’s hard to say.
It is easy to do an “eye test” at tournaments to see who is there, but the other issue is finding out who is not there. For example, countless questions have been directed toward me about the scarcity of Black players at tournaments. While we once saw the same negative trend along gender lines, there were always numbers to analyze. For ethnicity, there were not. The focus on gender in chess is now widely embraced. Several initiatives have been launched worldwide to spur more engagement for women and girls.
Even if we look at the number of new members and growth trends along gender lines, what do the numbers say about attrition/retention? Why do players stop/continue playing? What is the membership renewal rate? We need the data. U.S. Chess has actually done a good job in recouping the lost membership post-pandemic (rebounding 52% as of May 2021). The Membership Committee reported that U.S. Chess has employed a CiviCRM system (starting July 2020) designed to help in membership management. It has also created an online international membership targeting Americans living overseas and the global segment.
The Prism of Data
One of my suggestions is to make analytics a more significant part of the marketing thrust for membership management. We don’t know the membership numbers of ethnic groups in U.S. Chess, although it is a valuable metric when combined with other variables and a timeline to see trends. We can see periods of activity of different groups (age, gender, ethnicity, educational level). We can “slice the pie” in many ways to get a more nuanced analysis. For example, we can look at retention and attrition to detect patterns in membership trends. It allows for more precise targeting and more efficient use of the marketing budget.
Many years ago, U.S. Chess informed me that the organization does not keep ethnic data. So the question becomes, “Why not?” Perhaps, there is still a bit of uneasiness with the question of ethnicity, and it continues to be a contentious issue. If chess must increase the number of women and girls, it should also be important to increase the numbers of other demographic groups. Without this data, we miss opportunities to serve different membership segments and optimize marketing efforts for new members.
For example, how do you engage the different demographic segments? Women 21 and older? Low-income families? Seniors? Incarcerated? Disabled? Why do members quit? What has been the growth rate of women from 1991-present? How many chess households do we have with more than one player? What has been the retention rate of boys/girls after high school? Do we know the average educational level of adult chess players? There are countless queries to be run on data… all waiting to be converted to useful information.
How Does This Help?
The question becomes how the data are used. It starts with the types of questions you want to answer (hypotheses) and then a collection of the type of data needed. It will take some planning of a research methodology, but it may be worth the effort. Analytics software can make the job easier, but a complete data set is needed.
As a chess journalist, I am often in need of and frequently asked for a variety of data on particular players. The questions are primarily about the African Diaspora, and in many cases, I can furnish an answer. However, in some cases, the answers are unofficial because of the limitations of the data. For example, U.S. Chess has rating data going back to 1991, and for FIDE, the cutoff is 2001. So any data that precedes this is unaccounted for. Thus, I cannot answer with 100% certainty.
“Without a systematic way to start and keep data clean, bad data will happen.”
— Donato Diorio
In the last delegates meeting at the 2022 U.S. Open, there was a mention of entering pre-1991 rating supplements for membership access. Having access to this data could result in less time in the office fielding calls requesting historical data, as I have had to do. In the final analysis, better analytics can result in quicker and more accurate responses to inquiries, efficiency in decision-making, increased staff productivity, and improved engagement with the membership base.
The U.S Chess organization has come a long way since it leased computer processing time from SECOS (in the 1970s) to compile its ratings. Unfortunately, that company went bankrupt, and the USCF’s rating froze for nine months. There is no need to base one’s entire operation on an antiquated data management system. Using data to understand current members, membership peaks and valleys, and how to target untapped markets could result in U.S. Chess hitting the iconic 100,000-member mark for the first time.