1 in 1,600,000.

Yep, not very likely.  You are much more likely to die from a dog attack (1 in 86,781) or from a lightning strike (1 in 138,849).

But why odds?

Why not express these likelihoods in terms of probabilities?  Seems like a more natural way to express the chance of an event occurring, doesn’t it?

Odds, however, are commonly used to express event risk.  And of course, the chances of winning a sporting event.

As we write, the odds of the Los Angeles Dodgers repeating as World Series champions in 2021 are 1 in 3.[1]

So, what are odds?

The number of times an event occurs divided by the number of times it does not occur.

In the case of a meteorite strike, for every person that dies, 1.6 million do not.  In the case of the Dodgers, we would expect the Dodgers to win 1 World Series for every 3 they lose.

But this still begs the question, why odds and not probabilities?

Odds and probabilities

It is true that the probability of a low likelihood event is so small that stating it as a % requires a lot of zeros after the decimal (0.0000625% in the case of dying from a meteorite strike).

But that is not an insurmountable objection. For example, the risk of disease is often expressed in terms of rates per 100,000 to make the chances of low likelihood events easier to comprehend. Or we could state the probability of the non-event…not dying from a meteorite strike (99.994%).

A more important reason for using odds is that they facilitate multiplicative comparisons.

A simple example makes clear how probabilities can fall short.

Suppose the probability that Beth will go out to dinner this weekend is 75%. We cannot say, then, that the probability of Jose doing the same is 3 times that of Beth’s probability.

Why?  Probabilities are constrained to lie between 0 and 1. And 3 * 0.75 > 1.0.

So, what do we do?  Enter odds.

Odds are unconstrained

Odds are only bounded on the low end, by 0.  Let’s return to Beth and Jose.

The odds of Beth going out to dinner are 3 or 3/1.  Why 3/1?

Remember odds are the ratio of the events to non-events. Beth is 75% likely to go out. So, if she is faced with 4 opportunities to go out, she will do so 3 times.  In other words, she will go out (event) 3 times for every time she stays home (non-event).  3 to 1.

Now, if Jose is 3 times as likely to go out as Beth, his odds are simply 3 * 3 or 9.  Equivalently, we can express his odds as 9 to 1 or 9/1.

On the odds scale, odds can be 2, 10, 50 times greater…there is no upper limit. And this makes them very useful when we wish to compare the relative likelihood of events occurring.

Two sides of the same coin

It turns out that if we are still interested in the probability, we can easily derive it from the odds.  Odds and probability are two sides of the same coin.

Odds (o) are related to probability (p) by the following:

o = p / (1 – p) = (probability of event / probability of non-event)

Rearranging we find the “other side of the coin” (for an event):

p = o / (1 + o) = (odds of event) / (1 + odds of event)

So, in the case of Beth and Jose we get:

The relationship between odds and probability is shown graphically below.

As the odds increase, the probability also increases but in a non-linear manner.  As shown above, the probability “increases at a decreasing rate” and approaches 1.0 “asymptotically” (i.e., as the odds get very large, the probability approaches but never quite reaches 1.0).[2]

But any finite odds will map to a probability between 0 and 1.

Odds are preferred

When comparing the relative chances of events (or sports teams), odds are the preferred way of expressing how much more likely one event is over another.  We can always derive the associated probability.  But since odds are unconstrained,  there is no issue with saying the Los Angeles Dodgers are 11 times as likely (as of May 31, 2021) to win the World Series in 2021 than the Chicago Cubs.

So, the next time someone tells you the odds of rain during your camping trip this weekend are 5 to 2, you might want to sleep in a tent.

[1] As of May 31, 2021, the reported odds of the Dodgers repeating are 3 to 1 or 3/1.  In the betting world, this is referred to as fractional odds. The number on the left or numerator is typically the number of times the team is expected to lose. 3/1 yields an implied probably of losing 3 times out of 4 or 75%.  Thus, the probability of the Dodgers repeating are (1 – 0.750) or 25%.  Expressing as odds yields 1/3.

[2] In the limit, if the odds = infinity, then probability = 1.

The post Odds and probability…two sides of the same coin appeared first on KDD Analytics.

This dashboard tracks the San Diego COVID experience: new cases, tests, and positivity rates at the county-level as well as new cases for each of the county’s ZIP Codes.

On this first-year anniversary of these daily postings, we thought we would look back at this roller coaster year.

And, although our dashboard does not include US data, we thought that comparing the San Diego COVID experience with the national average would be insightful.

San Diego COVID experience vs the nation

The following figure shows the 7-day moving average of daily new cases per 100,000, for both San Diego County and the entire US.[1]

As shown in the above figure, as a nation we have been through 3 waves with it being too soon to tell if the 4^th wave has crested. San Diego County’s experience was generally similar except for the 4^th wave.

Wave #1

The initial rise in daily new cases crested at a 7-day average of 10 per 100,000 for the US on April 12, 2020. San Diego’s first wave crested about a week earlier on April 4^th at about 4 per 100,000.

The US new case 7-day average fell to 6 per 100,000 by mid-June. San Diego’s briefly fell a bit but then rose back up to a daily rate of 3 to 4 per 100,000 till mid-June.

So, San Diego did not really experience the same recovery from the first wave as the US.

Wave #2

For both the US and San Diego, the second, much larger wave began in mid-June 2020. The US new case rate increased from a 7-day average of about 6 per 100,000 to a peak of 21 per 100,000 on July 23^rd. San Diego’s rate increased from about 4 per 100,000 to a peak of 16 per 100,000 on July 2^nd.

Both the US and San Diego new case rates declined through the end of the summer. The US new case rate bottomed at a 7-day average of 13 per 100,000 on September 13^th. San Diego’s bottomed at the end of August at about 8 per 100,000 and stayed essentially flat till mid-October.

Wave #3

The US third wave began in mid-September – a full month before San Diego was hit. Rising from a 7-day average low of about 11 per 100,000, the US new case rate increased throughout the fall and early winter, peaking at 76 cases per 100,000 on January 11, 2021.

San Diego’s third wave began in mid-October. From a 7-day average low of about 8 new cases per 100,000 on October 20, the new case rate peaked at a high of 109 per 100,000 on the same day that this third wave peaked for the country.

As the figure shows, San Diego (as well as Los Angeles) suffered much higher new case rates than the national average.

But daily new cases started to decline just as steeply as they increased. San Diego’s 7-day average new case rate fell from this high of 109 to around 7 per 100,000 by April 20, 2021.

The US new case rate fell as well, from a 7-day average of 76 to about 16 by March 19, 2021.

Wave #4

Until this point, San Diego’s experience, though different in severity, matched the general pattern of the country. However, a 4^th US wave began in mid-March 2021, driven by new outbreaks in Michigan and New Jersey. It is too soon to tell if this 4^th wave has crested but the most recent peak is a 7-day average of 21 new cases per 100,000 on April 13^th.

San Diego has been fortunate to escape this 4^th wave (so far).

Fingers crossed…

[1] San Diego new case data are from the San Diego County Health Department. US new case data are from the CDC.  The 7-day moving average is the average of the current and preceding 6 days. 2019 population is used to normalize case counts so we can compare San Diego with the nation.

The post San Diego and COVID-19 … A Very Challenging Year appeared first on KDD Analytics.

But oh, if we call the whole thing off
Then we must part
And oh, if we ever part
then that might break my heart

—Ira Gershwin

The eye-popping efficacy rates reported for the Moderna (94%), Pfizer (95%) and, to a lesser extent, the Johnson & Johnson (66%) COVID-19 vaccines have undoubtedly not escaped your attention.

But what is vaccine efficacy and how is it calculated? And how does it differ from vaccine effectiveness?

Moderna vaccine efficacy

First, consider efficacy. Using Moderna’s reported clinical trial results as an example, we see that it is a straightforward calculation.

Moderna reported results from it’s COVID-19 vaccine trial in November 2020. The results are shown below in a 2×2 “contingency” or “cross-tabulation” table. The columns show the number of subjects who were infected (or not); the rows show the number who received the vaccine (or the placebo). And the cells show the intersection of those two events.

Relative risk

The strength of the association, or the effect size, between receiving the vaccine and not getting infected is measured by the relative risk.

The probability or risk of a vaccinated subject being infected is 0.08%. That is, (11 / 14,134) or the expected number of events / sum of events and non-events. For a subject receiving the placebo, the probability of infection is higher at 1.31% (i.e., 185 / 14,073).

So, using the placebo group as the reference group, the relative risk is (11 / 14,134) / (185 / 14,073) or 0.059.[1]

In other words, the risk of a vaccinated person being infected is 94.1% lower compared to a subject who received the placebo (i.e., (1 – 0.059) * 100)).

It is this calculation of 94.1% that was reported by Moderna as the vaccine’s efficacy rate.[2]

Vaccine effectiveness

So, what about vaccine effectiveness? The term effectiveness refers to how the vaccine performs in the real world.  Efficacy refers to how the vaccine performs under “optimal” conditions of a clinical trial.

Clinical trials are based on a sample of subjects who may not be fully representative of the general population (e.g., all comorbidities are not controlled for). In addition, the COVID strain that existed in the population during the clinical trial period may not be the same that occurs when the vaccine is released. Also, vaccine transportation, storage and delivery may differ from the more controlled environment of the clinical trial. Thus, the effectiveness of the vaccine may be different from what was found during the clinical trial.

Studies on COVID vaccine effectiveness

So, do we have any data yet on the real-world effectiveness of the COVID vaccines? It takes time to collect data, but we do have some indication that vaccine effectiveness is very high.

An early study appeared February 24, 2021 in the New England Journal of Medicine.  The study examined the Pfizer vaccine performance in Israel. The sample was matched data from over 1 million people, half who were vaccinated between December 2020 to February 2021 and half who were not. The results of the study suggest a symptomatic infection effectiveness rate of 94% 7+ days after the second dose.

A more recent study released by the CDC on April 2 examined both the Pfizer and Moderna vaccines.  This study used US data from December 2020 to March 2021. The sample consisted of 3,950 health care personnel, first responders, and other front-line workers.  The study found that the vaccines were 90% effective against COVID infection 14+ days after the second dose. Even 14+ days after the first dose the vaccines were 80% effective.

As a point of comparison, according to the CDC, effectiveness of the annual flu vaccination ranges between 40 and 60%.[3]

So, the effectiveness rate, after 2 doses of the Pfizer and Moderna vaccines, appears to be very close in magnitude to the efficacy rate.

Very good news indeed!

Tomato, tomahto?

[1] A relative risk ratio of 1.0 would mean no difference in effect between the treatment types.

[2] A summary of efficacy rates across the range of current COVID vaccines can be found here.

[3] One reason for the range is that the flu strain that is in circulation can differ from what was predicted when the annual flu vaccine was developed earlier in the year.

The post Efficacy vs Effectiveness of the COVID Vaccines…”tomato, tomahto”? appeared first on KDD Analytics.

It goes without saying that every business is keenly interested in knowing what the future will bring.

Will sales grow next year? By how much? Will suppliers increase their prices? How fast will be the adoption of a new IoT product? How much warehouse capacity is needed for the next holiday period? Will some international event plunge the global economy into a recession?

Predicting the future is an exercise in probability rather than certainty. Businesses engage in various levels of sophistication in trying to bound the likelihood of future states to support their business plans.

Some have teams of economists and data scientists tasked with building complex forecasting models.

Many businesses, however, likely rely on less sophisticated means centered on spreadsheet models, trends and moving averages (or even educated guesses).

Time series methodology is a moderately sophisticated yet cost effective way to generate forecasts. It is a statistical approach which bases forecasts on the past behavior of the data series in question (e.g. monthly sales).

And it accounts for other characteristics of a time series which can yield a more accurate forecast than, say, a simple straight-line trend model.

More time, more data

We have all heard the forecasts about data growth, the proverbial “hockey stick.”

By one account, human and machine-generated data is growing at 10x the rate of traditional business data. And machine-generated data is growing at 50X the rate.

A good portion of this machine-generated data has a time dimension. Internet of Things (IoT) devices are proliferating, each of which has a potential to collect data over time.

A washing machine can monitor, collect and post performance data to the cloud. Using these data to forecast product failure can lead to a pro-active maintenance visit by your friendly but lonely Maytag repairman.

Similarly, a household’s electricity usage can be monitored, modeled and forecasted leading to cost-savings suggestions by an energy service provider under a time-of-day pricing scheme.

The electric utility company itself can use the data from all the IoT appliances in households to generate better residential load forecasts and help better manage the electricity grid.

Even if a more traditional businesses source like sales, inventories, deliveries, workforce utilization, IT usage and the like, advances in data collection, storage and proliferation are making time series data more readily accessible.

Thus, there will be an increased demand for product managers, economists, statisticians and data scientists to make use of these data and tell us what will happen next.

Time series methods

The premise of time series methods (and of most quantitatively-based forecasting methods) is that the future will be much like the past.

If sales have been growing at a consistently healthy rate with strong seasonal variation (e.g. holiday periods) for the last year, then it is likely the next year will be similar, all else constant. If done correctly, the methodology can yield a defensible forecast of likely sales each month during the “forecast horizon.”

But, as with all forecasting methodologies, there are pitfalls of which one should be aware.

Practical time series methods

This is the first of a series of articles on practical time series methods for short-run business forecasting.

There are abundant, excellent resources covering the basics of business forecasting including time series methods, ranging from blog posts to online courses to open-source textbooks.

And time series methods are a mainstay of advanced courses in econometrics and business forecasting (resources we recommend are Elements of Forecasting by Diebold and Econometric Models and  Economic Forecasts by Pindyck and Rubinfeld).

Rather than being a treatise on forecasting, this series of articles will present a practical methodology and some of the lessons we have learned performing time series forecasting for clients.

The post Practical Time Series Forecasting – Introduction appeared first on KDD Analytics.

Turns out that in the LATAM region, Tableau is not the preferred data visualization platform. In fact, it is not even in the top five. As shown in the chart below, among the business sites in the TUDLA database for which an indicator of business intelligence software exists, Tableau ranks 7^th. The number 1 vendor is Qlik Technologies.

This preference does appear to vary by country, but not in Tableau’s favor. In Peru, Qlik is 7x more likely to be present as compared to Tableau – the largest relative penetration in the TUDLA database. Tableau is never more likely to be present relative to Qlik. The smallest Qlik differential, among countries with sufficient data, is 2.4x in Colombia.

The TUDLA data are corroborated by an analysis of web searches conducted by Kalinax. Their infographic (shown below) indicates that in terms of impressions, Qlik is more popular than Tableau in the LATAM region. The exception is in Colombia where there are more searches for Tableau than for Qlik. Kalinax posted several analyses of Tableau and Qlik which can be found here, here and here.

You never know what you are going to find when you start to dig. You can do your own exploration of the TUDLA database free of charge here.

At the core of the TUDLA database is a 70,000+ phone verified panel. Data from this panel include firmagraphics, contact information and full technology profiles. Surrounding this core are an additional 1 million+ business locations. Associated with these expanded data are basic firmagraphics (company name, address, URL) and expanded firmagraphics, where available (employee counts, industry and revenue). Indicators of what technology may be used at these business sites are also available.

The post Tableau vs. Qlik in Latin America appeared first on KDD Analytics.

A 23% compounded average annual rate of return.  Not too bad.

During his career Shelby advised Thomas Dewey on economic matters when he ran for president in 1940 and 1944 and served as Governor Dewey’s First Deputy Superintendent of Insurance from 1944 to 1947 in New York. Later, he served as US ambassador to Switzerland under Presidents Nixon and Ford (1969 – 1975).

A philanthropist, he was head of the Shelby Cullom Davis foundation which disperses funds to higher education and to research on public policy and economics. Princeton, his alma mater, was one of the beneficiaries of his grants. He also served as chairman of the Heritage Foundation.

Shelby got around.

But what the heck does this have to do with Fintech?

In a previous post we found that the Tucson, AZ metro area made our top 15 list of top addressable Fintech Securities & Investments markets on a per employee basis in 2015 (ranked 14^th).

Can a contributing reason for this can be tied directly back to Shelby Cullom Davis?

Securities & investments sector in Tucson, AZ

Tucson, AZ probably does not first come to mind when thinking about hot spots of financial activity. So, what is going on in Tucson?

As we have done in previous posts, let’s start with a high-level view of Tucson’s Securities & Investments sector. As shown below, 62% of the 2015 addressable Fintech market is due to portfolio management. Another 27% is due to securities brokerage. This is generally consistent with the West regional average of 28% for securities brokerage we saw in an earlier post. But it reflects a much higher concentration of potential Fintech spend in the portfolio management sub-sector (62 vs 48%).

Again, our analysis uses ZIP Pointe Market Sizer.  Market Sizer is our Tableau-based market sizing tool based on ZIP Code-level Census data on over 7 million private sector business locations.

In terms of business location size by sub-sector, nearly 80% of the locations are small (1 – 4 payroll employees). This is a recurring theme. The sub-sector is dominated by small locations though these are not necessarily all stand-alone companies (i.e. they could be formal branch locations).

There are only 29 locations with 10 or more payroll employees. A bit over 40% of the locations are involved with securities brokerage, another 26% are portfolio management and another 18% are investment advice (see below).

With respect to “hot spots” of potential Fintech spend per employee, it is clearly the portfolio management sub-sector that stands out, with potential spend ranging from $5,337 to $7,682 per payroll employee. The top hot spot consists of 3 locations with 50 to 99 employees.

Note: The US Census source used by Market Sizer tracks only payroll employees. Independent contractors are not reflected in these numbers. Hence, the actual employment level can be higher, possibly much higher, in sectors that make extensive use of contract labor.

ZIP Code level view

As we have done in prior posts, another way to drill into the addressable Fintech market in Tucson is by ZIP code. The following map shows the ZIP codes in the metro area color coded for addressable market size per employee. At the ZIP Code level, addressable Fintech market size ranges from $1k to $6k per payroll employee.

But there is a distinct hot spot in ZIP Code 85706 (circled in red) at $6k per employee. According to Wealthminder.com, the largest Tucson financial advisory firm (in terms of assets under management) is located in this ZIP Code:  Davis Selected Advisors.

Other top firms in the Tucson metro area (indicated by red arrows) are Tci Wealth Advisors in ZIP Code 85718; Sonora Investment Management in Zip Code 85719; Strategic Equity Management in Zip Code 85715; and Sterling Investment Management in Zip Code 85718.

Which ZIP codes account for the bulk of potential Fintech spend?

We have already discussed two of the top 3: 85706 at 16% and 85718 at 20%. But these are surpassed by ZIP Code 85712 (indicated by the green arrow) which accounts for 27%. These three ZIP Codes together account for 63% of Tucson’s potential Fintech spend in the Securities & Investments sub-sector.

Another way to drill into the addressable Fintech market is on a per business location basis.  Dividing potential spend by the number of locations is another way to normalize the data. This map (shown below) looks quite a bit different and one ZIP Code stands out above all the rest: 85706. Although the ZIP code overall accounts for 16% of total potential Fintech spend, on a per location basis, it ranks at the top with $230k in potential spend per site.

ZIP Code 85706

Turns out at there are only 3 locations in ZIP Code 85706.  At least one of these (the larger one) is associated with, yep, Davis Selected Advisors. And it may be that the other sites are also associated with Davis as we could not find any other Securities & Investments firms (with at least 10 employees) in this ZIP Code (If anyone knows, please let us know!)

Davis Selected Advisors

So, the Davis name is a major player in the Tucson metro Securities & Investments sector. Any relation to Shelby Cullom Davis perhaps?

Davis Selected Advisers was started in 1969 in New York by Shelby Davis…not Shelby Cullom Davis but his son Shelby Moore Cullom Davis. Following his father’s footsteps, Shelby M.C. Davis graduated from Princeton and rose through the ranks of the New York financial industry – specifically at the Bank of New York.

In the late 1970s the non-portfolio operations of Davis Selected were moved to Sante Fe, N.M. In 1997, a service center was opened in Tucson and personnel were hired locally and moved from Santa Fe. By 2000, Tucson had became Davis Selected’s global headquarters, specifically in ZIP Code 85706 where the company operates out of its own commercial property.

Today, Davis Selected Advisers is led by Shelby’s son, Chris Shelby, grandson of Shelby Cullom Davis.  “Davis Selected controls the nine-fund Davis group, the Selected group of funds, which is a no-load, four-fund group, private accounts of no less than $5 million, and other managed money programs.” (Inside Tucson Business).  Currently, Davis Selected has about $30b in assets under management.

So, there you have it…from Switzerland to the Sonoran Desert! 

Ok, maybe New York to Arizona…

The post Top 15 Metros – From Switzerland to the Sonoran Desert – Fintech Securities & Investments appeared first on KDD Analytics.

But for those of us old enough to remember, we knew it thanks to Mutual of Omaha’s Wild Kingdom. The show aired on NBC TV just before Walt Disney’s Wonderful World of Color on Sunday nights. Hosted and narrated by zoologist Marlin Perkins, Wild Kingdom featured Jim Fowler showcasing wild animals.

Or maybe you know it for the corn? Nebraska and Iowa have always been synonymous with corn.

But now, Omaha will also be famous for making the Boulder Equity Analytics’ Top 15 List of the Largest Securities & Investments Fintech Markets, ranked by per employee revenue. In fact, as we discussed in an earlier post, it ranked #2 right after the New York metro area.

So, is Omaha’s ranking due to Berkshire Hathaway? Mutual of Omaha? Or maybe it’s companies like TD Ameritrade, Wealthplan, CWM or Securities America? Let’s dig into the data.

Securities & Investments sector in Omaha

What does the Securities & Investments sector look like in the Omaha-Council Bluffs metro area? To start, as shown below, 70% of the 2015 addressable Fintech market is securities brokerage. Another 13% is portfolio management. This is much more skewed towards securities brokerage than the Midwest region overall (36%). So, Omaha is a relative hotbed of securities brokerage business.

Again, our analysis uses ZIP Pointe Market Sizer. Market Sizer is our Tableau-based market sizing tool based on ZIP Code-level Census data on over 7 million private sector business locations.

In terms of business location size by sub-sector, nearly 76% of the locations are small (1 – 4 payroll employees). There are only 10 locations with 100 or more payroll employees. A bit under 40% of the locations are involved with securities brokerage, another 22% are in portfolio management and 24% are in investment advice (see below).

With respect to “hot spots” of potential Fintech spend per employee, business sites with over 20 employees in securities brokerage are at the top. Thirty (30) sites have a potential Fintech spend of at least $6,000 per employee.

 

Another way to drill into the addressable Fintech market in Omaha is by ZIP Code. The following map shows the ZIP Codes in the metro area color coded for addressable market size per employee. At the ZIP Code level, addressable Fintech market size ranges from $1k to $8k per employee.

At the upper end, ZIP Code 68101 shows a per employee value of $8k. There is only 1 site in this ZIP Code and it only has 1-4 employees, in securities brokerage. This is probably a branch office associated with a much larger company. The other 2 top ZIP Codes are 68128 and 68154 with over $6k in potential Fintech spend per employee. We will focus in on them in a bit.

 

However, if we measure addressable market size on a per business location basis (another way to normalize the data), the map looks very different but yields similar conclusions. A shown below, the top ZIP Code in terms of potential Fintech spend per site is 68128 with over $700k.  In second place is ZIP Code 68154 with a total of $126k per site. 

 

But these two ZIP codes are quite different.  ZIP Code 68128 has only 3 sites while ZIP Code 68154 has 81. And in terms of total potential Fintech spend, the ranking is reversed. ZIP Code 68154 accounts for 54% of the addressable Fintech market in Omaha while ZIP Code 68128 accounts for 12%. In third place is ZIP Code 68130 with another 9% in total potential Fintech spend.

A tale of two ZIPS

ZIP Code 68128 has the largest potential Fintech spend per business site at over $700k. But this is due to one large location as shown below (values in the chart are potential Fintech spend per employee). 

Turns out this one large site is the headquarters of Securities America.

We caution that our estimates of potential Fintech spend at a company level are directional. Our Fintech estimates are derived from market aggregates and not from specific company revenue accounts. 

Securities America, founded in 1984, helps financial advisers create more profitable practices. Through a series of acquisitions, the most recent by Ladenburg Thalmann Financial Services, it also offers more traditional services such as equity research, brokerage services and investment banking. On their website, the company says they currently have 575 employees.

We should note that the Census data only track payroll employees. Contractors are not included in these totals. So, the total employee count at any given location could be higher than what is shown here, especially sub-sectors that rely heavily on contracted labor.

ZIP Code 68154 has a much more varied representation of business sites, both in terms of size and in terms of sub-sector, as well as a larger number of sites (81). Securities brokerage accounts for 38% of the business sites with all the large sites (100+ employees) being in this sector. Portfolio management accounts for another 27% of sites and investment advice another 24%.

Wealthplan Partners, who provides financial planning and asset management services, is based in ZIP Code 68154 as is Carson Wealth CWM LLC, another asset management company. Although the representation is more varied, there does appears to be a dominant company in this Zip Code: TD Ameritrade.

TD Ameritrade consolidated employees from around the Omaha metro area into this ZIP Code in 2013. About 2/3 were relocated to the company’s new headquarters building and the remainder in other buildings nearby. At the time, the company says that 2,000 employees were relocated to the ZIP Code.

Amerivest Investment Management, owned by TD Ameritrade and the largest financial advisory firm in Omaha according to Wealthminder.com, is co-located with TD Ameritrade in its ZIP Code 68154 headquarters building.

So what about Berkshire Hathaway and Mutual of Omaha?

Berkshire Hathaway’s headquarters is in ZIP Code 68131 and is not classified as being in the Securities & Investments sector; it is a holding company. Mutual of Omaha is classified as an insurance carrier so is also outside the Securities & Investments sector.

So, the Fintech Securities & Investments market in Omaha is powered by companies in the securities brokerage sub-sector, like TD Ameritrade and Securities America.

But in case you are wondering whatever became of Mutual of Omaha’s Wild Kingdom…it is back on the web with Stephanie Arne hosting.

The post Top 15 Metros – Wild in Omaha – Fintech Securities & Investments appeared first on KDD Analytics.

Previously, we estimated the potential Fintech spend in the Securities & Investments sector to be a little over 16% of the total Fintech market of $18.8b. As of 2015, this was $3.1b.

In our continuing series on the addressable Fintech market we dive deeper into the Securities & Investments sector. Again, our analysis uses ZIP Pointe Market Sizer.  Market Sizer is our Tableau-based market sizing tool. It is based on ZIP Code-level Census data on over 7 million private sector business locations.

Portfolio Management tops the sector

Within the Securities & Investments sector, portfolio management accounts for the largest share of the addressable Fintech market at 38%. Securities brokerage (27%) and investment banking (16%) round out the top 3.

This breakout, though, varies across regions. As shown below, portfolio management companies account for the largest regional share of the market in the Northeast (42%) and the West (48%). In all other regions, securities brokerage accounts for the largest share. Across all regions, portfolio management and securities management together account for 64% to 78% of total potential Fintech spend, respectively.

As shown below, in terms of business location size by sub-sector, 77% of the locations are small (1 – 4 payroll employees). And nearly 94% have fewer than 20 payroll employees. The Census data also indicate that there are nearly 1,200 sites with 100+ employees, representing a bit over 1% of all business sites in the Securities & Investments sector.

We should note that the Census data only track payroll employees. Contractors are not included in these totals. So, the total employee count at any given location could be higher than what is shown here, especially sub-sectors that rely heavily on contracted labor.

In terms of potential Fintech spend per employee, the chart above shows a range of $598 (commodity contracts, 500 to 999 employees) to $7,041 (securities exchanges, 50 to 99 employees). The “hot spots” of potential Fintech spend are in the investment banking sub-sector among sites with 100+ employees, and the securities exchanges sub-sector among sites with 10+ employees (though there are only 36 sites in this sub-sector).

No Surprise, NYC Metro tops area ranking

In terms of total potential Fintech spend, the usual suspects are again at the top of the addressable market list in the Securities & Investments sector. The New York metro area leads the list at $1,090m. Boston $191m, Chicago $183m, Los Angeles $172m, and San Francisco $171m round out the top 5.

But in terms of per employee, San Francisco jumps from #5 to top the list.

In 2015, the top 15 metro areas in terms of addressable Fintech market per employee in the Securities & Investments sector are shown below:

We limited our analysis to metro areas with 150 or more business locations in the Securities & Investments sector. This helps reduce the “noise” in our analysis and minimizes (but does not eliminate) the likelihood that a metro’s normalized estimate is unduly swayed by a few extreme business locations.

Omaha is #2?  There’s a surprise

The San Francisco metro area heads our list of the largest addressable Fintech markets on a per employee basis in the Securities & Investments sector at $5,313. Rounding out the top 5 are Omaha-Council Bluffs, NE-IA ($5,298), Bridgeport-Stamford-Norwalk, CT ($5,122), New York ($5,107) and Worcester, MA ($4,685). Other notables in the top 15 are Durham-Chapel Hill, NC ($4,94), Charlotte-Concord-Gastonia, NC ($3,381), Austin, TX ($3,256) and Tucson, AZ ($3,130).

If you already knew Warren Buffett’s hometown of Omaha, Nebraska was going to be the #2 market in the US for Securities & Investments, give yourself a pat on the back. We’ll break that down for you in future posts, along with two other surprises on the list, Durham-Chapel Hill and Tucson.

This post was written with Tom Marsh, CTO at Boulder Equity Analytics (BEA).

The post Breaking Down the Fintech Securities & Investments Sector appeared first on KDD Analytics.

In regions where data are less complete, like Latin America, a descriptive approach offers a very cost-effective way to reveal what the size of the prospective market might be. In fact, such an approach may be the only feasible approach depending on fill rates in the database.

Once identified, this population of prospective sites or look-alikes can then be pulled from the database, matched with a contact database or passed to a call center for verification of firmagraphics, contact details and technology usage.

TUDLA’s Latin America database

As we discussed in a previous post, TUDLA maintains a database of over 1 million business sites in Latin America and the Caribbean. The data include basic firmagraphics (company name, address, URL) and expanded firmagraphics where available (employee counts, industry and revenue). Also, indicators of what technology may be used at these business site are available.

TUDLA also offers free access to the data through interactive Tableau dashboards. This allows users to explore the data at an aggregate level and get a sense of TUDLA’s coverage. It also allows users to perform their own descriptive look-alike analysis.

Descriptive look-alike analysis

As a simple example, suppose I am SAP and I want to know how many prospective customers there might be for an SAP branded ERP offering in Brazil. (Alternatively, I could be an SAP ERP competitor and I am looking for possible SAP customer sites for a competitive kill campaign). Consider the following screenshot from TUDLA’s interactive Tableau dashboard.

TUDLA’s database shows (lower left-hand corner) that there are 2,828 sites in Brazil that are known to have SAP ERP. And the charts show the characteristics of these sites (i.e. the employee, revenue and industry distributions).

Also in the left-hand corner is shown that there are 424,693 sites in Brazil that share the same characteristics (employee count, revenue size and industry) as these 2,828 sites known to have SAP ERP (i.e. these are SAP’s look-alikes). So, this is the size of the prospective market for SAP in Brazil.

Further refinement to a specific firmagraphic, say, revenue size, will more narrowly focus the prospective market. For example, suppose SAP is only interested in sites with enterprise revenue greater than $50m. In this case, the prospective market size is reduced to 19,144 sites, nearly 50% of which are in the manufacturing industry (see below).

Interactive visualization

Interactive visualization is an excellent way to get a comprehensive view of exactly what is in your database. Lack of coverage in firmagraphics, geographies and other business attributes, such as technology presence, can quickly become apparent.

Moreover, it can be used, as in the case of TUDLA, to examine your look-alike market. You can quickly and cost-effectively (don’t need to pay to have a predictive model built) see the size of your prospective market based on the characteristics of your current customers.

Are your sales territories aligned as they should be?  With just a few clicks, an interactive visualization can provide the answer.

For more information on providing an interactive visualization for your data, give us a shout.

KDD Analytics is providing analytical services to TUDLA.

The post Finding B2B “Look-Alikes” in Latin America appeared first on KDD Analytics.

We’ll come back to Springfield.  But first, let’s slice that $18.8b into some smaller pieces.  Nearly 84% of this market is in the banking sector; the other 16% is in the securities & investments sector.  In absolute terms, the New York metro area (unsurprisingly) leads the list at $1,090m.  Other key financial centers that make the top 15 are Boston ($191m), Chicago ($183m), Los Angeles ($172m) and San Francisco ($171m).

Normalizing market data

Focusing on total market size misses the hot spots or concentrations of “spend potential” for the segment you’re after.  And while the best targets may not even reside in the list of large markets, you can bet a lot of your competitors haven’t figured that out.

A first step in prospect targeting is to “normalize” your data.  The New York market is #1 because of the substantial number of financial companies based in the metro area.  But on a per employee basis, there are other metro areas that rank higher than New York.  To delve into a per employee analysis, we again use ZIP Pointe Market Sizer.  Market Sizer is our Tableau-based market sizing tool using ZIP Code-level Census data on over 7 million private sector business locations.

Fintech market size per employee

We limited our analysis to the 80% of metro areas with 250 or more business locations in the banking and securities & investments sectors.  This helps reduce the “noise” in our analysis and minimizes the likelihood that a metro’s estimate is unduly swayed by a few extreme business locations.  In 2015, the top 15 metro areas in terms of Fintech market size per employee are shown in the figure below.

Aside from the fact that Chicago and Los Angeles didn’t make the list, look at the concentration in the Northeast region. 10 of the top 15 are in the Washington DC – Boston corridor.  Washington DC is the largest Fintech market in terms of revenue per employee at $12,863, followed by Springfield MA ($10,848) and Worcester MA-CT ($9,094).  Rounding out the top 5 are Boston MA-NH ($8,381) and Bridgeport CT ($7,851).  New York is 6^th with $7,844 per employee.

Washington, Boston and New York are not surprising.  But what about Springfield Massachusetts?

Savings and Loans in Springfield MA

According to 2015 US Census data, there were 4,254 employees on the payrolls of banking and securities & investments companies in the Springfield MA metro area.  Roughly 40% of these employees worked at 143 savings institution locations. Savings institutions (“savings and loans”) are apparently quite popular in New England, and quite lucrative.  85% of the total Fintech market in Springfield MA is attributable to savings institutions.

Savings institutions also account for most of the Fintech addressable market in neighboring Worcester metro (71%); but only 33% of Boston’s addressable market (although still is the top sub-sector).  In the neighboring Hartford CT metro (ranked 11^th), savings institutions account for 65% of the Fintech addressable market; and in the Portland ME metro (ranked 13^th), 67%.  So, indeed, savings institutions are popular in New England.

Although caution is always warranted when drilling down too deeply into market segments (due to low counts), this heat map reveals several interesting characteristics of the Springfield metro market:

(The heat map shows per employee Fintech market size by employee size of location and sub-sector.  A darker color and larger size indicate a larger market size per employee).

• Savings Institutions have the largest Fintech market size per employee across all location sizes in which savings institutions operate;
• Fintech market size per employee for savings institutions is over $20,000, while the average for Springfield is $10,848;
• While there are many other financial institutions in the smaller groups, savings institutions dominate the largest location sizes.

Making use of this analysis

How is this information useful?  Suppose your company has a solution for savings institutions. The Springfield MA metro has 143 prospects.  It is very accessible to the rest of the major financial markets in the Northeast.  And it most likely is ignored by your competitors located in those markets.  These prospects are likely very accessible and would be a great beachhead, providing enough revenue and referenceable customers to launch your business.  Similarly, if your mission is to disrupt the savings and loan industry, then Springfield has a concentration of customers you can target.

As always, data have different stories to tell.  Revenue per employee can also be used as a metric for productivity within the segment.  If you’re selling technology to improve employee productivity, your best market would be at the low end of the range.  In this case, savings and loans in Springfield would not be the best sub-sector to target.

We’ll continue taking apart the data on Fintech and offering a few observations along the way.  If there’s an angle you’d like us to explore in a future post, let us know.

This post was written with Tom Marsh, CTO at Boulder Equity Analytics (BEA).

The post Fintech, Savings and Loans and Springfield MA appeared first on KDD Analytics.