TL;DR — Uncover competitive insights from information and analytics using Federal contracting #opendata. Analyze > 100GB’s of US Federal spending open data with a few lines of Python code.
Previous Blog Post in this series can be found at this link.
Python code and the jupyter notebook for this analysis is on Github here.
Several of the most popular and influential HBR articles on business strategy explore the relationship between market share, market growth, and profitability. In this tenth blog in my series, I show you how you can use Federal spending open data (USAspending.gov) to help guide your analysis of the market and shape your business strategy.
For most companies competing in the Federal contracting market (“Federal market”), the ~$600B/year in annual spending seems like an almost infinite amount relative to their annual revenue and a market with unlimited growth potential. With the exception of weapons systems platforms, most of the aggregated Federal market is highly-fragmented, relative to most commercial markets. However, at the Agency level, market share is more concentrated and it is challenging to dislodge incumbents that have solid past performance records. Competition for that work at re-compete time (~ every 5 years) can be intense. Incumbents have huge advantages in the Federal market. Also, most services contracts are long term (typically, one year with four option years) and incumbent recompete win rates over 75% are not uncommon.
Capturing market share for your firm’s services is often a long game and it can be expensive to displace incumbents in terms of bid and proposal to capture the work and managing profitability after the win.
Many Federal contractors thrive on a steady proposal machine fueled by a healthy pipeline of qualified opportunities.
Is it possible to make better capture investment decisions using insights about analytical features in the market the reveal competitive intensity and Agency-level buyer preferences?
Are there ways to use Federal #OpenData and analytical techniques to guide your planning efforts to yield higher ROI with a more focused strategy?
Natural Limits to Market Share?
For some of the largest segments of the Federal contracting market, there seem to be boundaries on market share by product or service category that reflect market competition, customer preferences, contracting practices, and other factors. Exploring those forces at the Federal, Department, and Agency for the segment of products or services your firm concentrates on can help in shaping your investment plans for new opportunities and retention of incumbent work.
Spending by Federal designated product_or_service code for GFY2019 (~$590B total prime obligations):
- PSC_Cat = product_or_service_code which is the dominant type of work within the prime contract.
Selecting a subset of these codes (A, B, D, M, Q, R, S) that are focused more on services than products, you can see the GFY10-GFY19 market share trends for the #1 company each year in that PSC_Cat.
Growing market concentration at the top: The top market share firm in PSC_Cat Q Medical Services has jumped significantly (50% increase— from 20% to 30%) between GFY17 and GFY19 over the ~20% baseline earlier in the decade. PSC_Cat B Special Studies has doubled from ~5% to ~10%. PSC_Cat M Operation of Government-Owned Facility has increased from ~12% to %16. Those categories represent ~$54B/year and ~9% of total Federal obligations.
Stable or declining market concentration: The #1 contractor in PSC_Cat A Research and Development has dropped from ~18% market share to ~13% over the decade while PSC Cat D IT and Telecom remained relatively stable at ~6% (with a significant fluctuation between GFY13-GFY17 of 2x) at the start and end of the decade. PSC Cat R Support Services has seen a slight decline from ~4% to ~3.8%. For GFY 2019 PSC_Cat A + D + R => ~$177B which is ~31% of total prime contracting.
Note how the PSC_Cat market share values are clustered by type of work. It is possible to capture 20%-30% of the spending in Medical Services (PSC_Cat Q), a > $20B/year market, but the #1 contractors in other major spending categories are only able to capture about 5%-10% of the spending in their work segment.
Exploring PSC_Cat D (IT & Telecom), R (Support Svcs), and A (R&D)
Let’s dive into the large PSC_Cat D, R, and A categories and explore those further.
Top Contractor (“Top_1”) in a GFY and PSC_Cat
Top_5 Contractors Total Obligations in a GFY and PSC_Cat
The charts on the left show a clear clustering of market share values by PSC_Cat. The charts on the right show trends in those values over time. They have either become stable or declined over the last decade. For reference, overall spending from GFY10-GFY19 followed this pattern:
Although the last decade has seen significant restructuring and M&A of the contractors in the PSC_Cat D and R segments of work with major growth in PSC_Cat D (IT & Telecom) spending, the market share for the #1 and top 5 contractors in any one year has remained relatively stable. Note that being #1 in GFY2019 for PSC_Cat D is worth $1B/year in additional revenue but only 0.4 basis points increase in market share vs GFY10. The PSC_Cat R #1 contractor in GFY19 saw a decline of ~$0.5B in revenue vs the #1 in GFY10.
Note that the #1 and top 1–5 contractors may be different each year though in most cases it does not change rapidly with the exception of M&A activity and strategic sales of divisions. This next graph shows how much change there has been the PSC_Cat D leadership of Top 10 firms from GFY10–19 through M&A and organic growth:
Link to interactive version of the chart above: https://leifulstrup.github.io/Interactive_Medium_HTML/
Both Fragmented and Concentrated
At the Federal-wide market level, the #1 firm in the IT and Telecommunication Services segment did not exceed 8% market share between GFY2010 (GFY: Government Fiscal Year — Oct 1 through Sep 30)and GFY2019. That was a ~$45B market in GFY2019. For the second-largest category of services spending, “Support Services (Professional, Management, Admin)”, ~$85B/year, the #1 competitor between GFY10–19 reached a high of ~4.4% in GFY10.
$85B x 4.4% ($3.7B) is a big number for sure, but the market is fragmented as seen as a whole.
The picture of market share fragmentation can change quickly when one drills down to view spending at the Department (e.g., US Department of Agriculture), Agency (e.g., Defense Information Systems Agency of DoD), or Office level. At the level of an Agency or Administration, it is not unusual for the top contractor to have a much higher percentage of spending in that category — a 20%-40% market share range for #1 is not atypical as seen in the histograms of #1 contractor market share by Agency (from GFY10-GFY19) below:
Starting from an existing prime contract with a Federal customer in an Office, Agency, or Department, one can explore the relative attractiveness of capturing more local market share for like-work (e.g., IT Services) at the same Agency, like-work at other Agencies in the same Department, or venturing into other types of work (e.g., PSC_Cat R) in the same or a different agency. The relative odds of success and return on investment (ROI) depend on the competitive intensity that surrounds a firm’s existing work and the target of opportunity. That ROI is also affected by the relative growth rate of spending for work in that category and the contracting type preferences of the customer (e.g., time and materials, fixed price, cost-plus, etc.).
Department Level Analysis: DoD
Let’s explore the data at the Department level with the largest contracting Department in the Federal Government — the Department of Defense (DoD). Here is a snippet of the Python pandas code (see this Github repo and jupyter notebook for blog #10):
PSC Cat D #1 contractor market share at DoD reached a high of ~12% just before the overall market bottom in GFY15 and has now settled into ~6%. PSC_Cat R has remained relatively stable fluctuating around 6%. PSC_Cat A (R&D) fluctuates significantly relative to the others. PSC_Cat A was a $48B market in GFY19. PSC_Cat R includes a wide range of services and many smaller contracts while PSC_Cat A includes very large and small contracts. A deeper dive is warranted to understand these segments at the next levels of granularity (specific lower-level codes with the PSC_Cat, NAICS of the contractor, named-programs, and contracts).
Though the top 5 contractors' total market share is relatively stable for R and A. It is interesting to note that for PSC_Cat A, the #1 contractor share is growing, yet the Top 5 is relatively stable. GFY20 final figures will provide a complete picture of that seeming trend.
Another interesting ratio to examine is the relative power of being #1 in your segment. For instance, the ratio of the Top 5 market share/#1 contractor market share is a clear indicator of the value in reaching that #1 spot in your segment (lower is better).
For GFY19, DoD Top 5/#1:
PSC_Cat D ~25%/6% => ~4
PSC_Cat R ~19%/5% => ~4
PSC_Cat A ~ 36%/16% => ~2
Agency/Administration Level: DoD Defense Information Systems Agency (DISA) and Social Security Administration (SSA)
PSC_Cat A is excluded since DISA nor SSA has much spending in the Research and Development category.
Social Security Administration (SSA)
It is interesting to compare PSC_Cat D market share patterns between DISA and SSA. For GFY19, DISA’s #1 contractor had ~18% of a ~$3B market (~$0.5B) while SSA’s #1 contractor had ~35% of a $0.7B market (~$0.25B). For the top 5, DISA had ~33% of $3B (~$1B) and SSA ~70% of $0.7B (~$0.5B).
Five (5) contractors at SSA capture 70% of PSC_Cat D spending while it is less than half that for the top 5 competitors at DISA. Not only is the DISA market ~5x larger, but it also is not nearly as concentrated. Which segment of the market is more likely to generate the higher profit margins and volume of profit for a particular market share rank?
DISA Top 5/#1 = ~1.9 and SSA Top 5/#1 = ~2
The lower that ratio, the more value captured by being #1 in that segment and, thus, the importance of retaining that #1 position. How might this be a predictor of competitor behavior when new entrants appear and others compete for this work at contract refresh points?
Understanding the relative differences in the market dynamics at the Agency level is an important context for opportunity investment decisions. Knowing this context will help you within an Agency that you already serve and want to capture more market share. It is especially useful when you are venturing into new Agency territory and have less experience and insight on buyer behavior and competitive forces.
What can be learned from Federal contracting market share analysis that can inform business strategy?
Although the analysis above is a high-level view of the overall market, it demonstrates that the choice of market share analytics (not just information such as obligation growth trends) to assess market segment attractiveness can be extracted from the USAspending.gov open data. Potential insights:
(A) Understanding Agency-level market share dynamics reveal whether there is a major advantage to being #1 or in the top 5 at an Agency.
(B) Different PSC_Cat segments of the market can have very different implications in terms of future market share and volume of prime obligation capture (revenue and profit potential) potential.
(C ) The stability of contractor market share rankings (not explored above but in the data source) over time can reveal customer preferences. Their preference for a broad or narrow contractor base and how open they are to new entrants.
What do you see in this series of analytics that I missed?
I will explore more analytical and market topics in future blog posts — including more market-share trend analysis and NLP techniques using USAspending.gov and beta.SAM.gov award and opportunity textual data and mashups with other open data sources.
Previous Blog Post in this series can be found at this link.
The Python and pandas code used in the examples above and more can be found here: https://github.com/leifulstrup/USAspending_Medium_Blog_Analytics_Series/blob/master/Blog_10y_Market_Share_Analytics_read_parquet_github.ipynb
Blog series Github repo: https://github.com/leifulstrup/USAspending_Medium_Blog_Analytics_Series
MIT Open Source License Copyright 2020 Leif C Ulstrup
When working with USApsneding.gov data, see this note embedded on the USAspending.gov site about attribution of D&B data and “D&B Open Data” that is embedded in the download data and USAspending.gov website reports such as the one below — https://www.usaspending.gov/db_info