History of Predictive Analytics: Since 1689

on August 12, 2013

Recently, Contemporary Analysis (CAN) was asked by the Indianapolis Business Journal to weigh in on how Predictive Analytics is changing the marketing space. We believe by combining predictive analytics and marketing, called contextualized marketing, marketing can move closer to the holy grail of one person, one sale.  Most companies do this by purchasing a software–it’s dactyl, most companies have a line item in their budget, and it gives a third-party marketing company reoccurring revenue. While CAN itself doesn’t have a piece of software to sell, we believe that contextualized marketing is the right move for companies and that those with the edge are “the ones using data scientists to predict who inside of a group of people have the propensity to buy.”

Because CAN doesn’t have the software solution, we approached the solution from a slightly different angle. We provide modeling and results in way that can be easily added to your current tracking software. This way, a company can begin the transition from shotgun marketing to tactical marketing with a low cost of entry. Once implemented, the case can be made for the software using predictive analytics to be purchased and used if needed.

Additionally, because software companies provide a tool and very little in the way on why the tool is important, most software implementations fall flat. We believe our value is helping companies manage the change management necessary to implement the software and to understand how to use them effectively–which greatly increases both the adoption and the ROI from the adoption.

Let us know how we can help you build data science into your marketing. We would be glad to help.

For more information or to gain knowledge as to who and how we have helped implement contextual marketing, go to our website at:

or connect with the president on LinkedIn at:

or send us an email at:


Full article IBJ article:


Voting with Facebook Likes

on August 22, 2014

Omaha Public Schools Pre-Bond Analysis Featured on KETV NewsWatch 7

on August 7, 2014

Contemporary Analysis (CAN)–A new president of CAN was announced earlier this month. Nate Watson, long time employee, Sr. Project Manager, and Head of Sales will take over for Grant Stanley, in early June. Grant said the lead change was a long time coming, “I am staying on as the Chairman of the Board so I can provide vision and strategy, but I am relinquishing the day-to-day operations to Nate.”

This change comes as Grant takes over as CEO of a new startup, Yield. Yield provides a tool for design and marketing companies to better project staff capacity for a given scope of work. It allows management to know how much work a designer has left and alerts the manager to when the designer is running out of work. Yield and CAN will remain close as the two are set to do work for each other for the rest of the year. “It will be a great predictive project for CAN”, says president-elect Nate, “Yield gets the leadership of Grant, we (CAN) gets to keep the strategist and visionary our company is known for, and we (CAN) get to build predictive analytics into a new product slated to change a whole vertical.”

New Leader

Nate has been steadily taking on more and more of the operations since mid-2014 when he began managing the projects he was selling. It was an important step for the company because it no longer meant there was a drop off in knowledge between setting up the project, and the implementation of the project. “We will operate like we always have. We will help companies use their data to understand and get a better handle on how to make decisions faster. When you let data do some of the heavy lifting, it’s amazing some of the insights a leader can get. They still have to make the final decision, but predictive analytics gives them access to relevant data to make decisions in seconds instead of spending hours combing through a pile of reports.”

New Verticals

Nate also brings new energy and ideas to the business. After adding political campaigns to CAN’s capabilities in 2014, Nate managed 2 governor campaigns, 2 local campaigns, and 2 bond issues. In fact, politics became 33% of the total business CAN secured in 2014. “We faired pretty well getting 3 of 6 through the primary and going 3 for 3 in the general.” In fact, CAN predicted the turnout of the primary election to within .27% or 876 votes out of 324,227, and the final vote count to within 2.8% or 1,577 out of 56,324–all 3 weeks before the election. These numbers catapulted them into the spotlight for regional and national campaigns and many took notice. To date this year, CAN has taken on a Governor campaign, two ballot initiatives, and looking to add a presidential candidate later this year.

New Ideas

CAN is working on a number of new ideas as well. Later this year, they are going to release their first piece of software. CAN’s analytical software is designed to help non-mathematical leadership interact with and learn from their data without the need to employ data scientists and includes the ability to run scenarios on live data. This will accelerate political buy-in and implementation time of analytics into a company. Their software will give CAN an entirely new revenue stream and will allow CAN to sell to much smaller companies. “We are hoping this product allows all companies to use their data to create better marketing, sales, customer retention, HR, and forecasts,” says Nate.  The system is slated to come out in the fall.

CAN is also being asked to develop auditing capabilities as well as a predictive analytics and a data science recruitment arm. “Finding, hiring, and training data scientists is a real problem for companies. The lack of data scientists is the bottleneck we think we can solve. We understand how to attract and vet data scientists better than traditional HR and hope we can partner with companies to lend them our knowledge.”

With these new ideas and growth, CAN is also going to need more staff. Currently open are positions for two new data scientists and a sales person. “We are looking for those individuals who are gritty, and can solve a problem when the solution isn’t easily found. This goes for both data scientists and salespeople.” “After all,” says Nate “finding solutions to problems–really hard problems–is how CAN has been known for the last 8 years.”


More data on Contemporary Analysis can be found on their website at: or by connect with Nate Watson on LinkedIn at:



Nate Watson named new President of CAN.

on May 15

Omaha Public Schools Pre-Bond Analysis Featured on KETV NewsWatch 7

on August 7, 2014
Voting with Facebook Likes

As campaigns this year gear up their marketing efforts on all the social platforms, it begs the question, how can campaigns measure the success of their efforts on each of these platforms and translate that success to the state of the race?

Let’s take Facebook as an example. On Facebook, a campaign is limited to a few metrics to track performance. These metrics include total “Likes”, average post likes, average post shares, and total number of people talking about the page. Marketing efforts are best measured by looking at the reach of each post, but it seems that the campaign as a whole tends to race towards getting as many “Likes” as possible. For example, campaigns frequently post about milestones they’ve reached for Facebook likes and promote it as a metric for success for the performance of the campaign. There’s certainly nothing wrong with this. Campaigns should be doing everything they can to increase their reach across their network of constituents by getting more of them to like or follow their page.

However, does it actually translate as a predictor for a winning campaign? We decided to embark on an experiment to find out the effect of voting with Facebook likes.

Our Sample:

For our initial experiment we decided to focus on races in the 2012 election cycle at the national level for U.S. Senate and House seats, and at the local level with Gubernatorial races. We didn’t focus on smaller races because the Facebook data tended to be sparse. We also couldn’t analyze races farther back than 2012 because the time series data through Facebook only goes back so far. Initially we gathered information on approximately 106 races for our sample. After eliminating races where Facebook data was sparse or non existent, we were left with 76 cases for our analysis. We also excluded cases where Independent seats were the incumbents, any new seats that opened up for that election (this would be caused by redistricting most likely on the House side), and any cases where less than 100 likes were found on someone’s Facebook page.


Next, we wanted to isolate those who won their race in 2012 and also had the most Facebook likes as the group we were trying to predict. We assume that Facebook likes don’t translate to wins explicitly and that there are other factors or dimensions within races that might also be good predictors. The predictors we decided to test in this experiment were Race Type, Incumbent Status, and spread of Facebook likes between the competing campaigns.

Race Type: Senate, House, or Governor

Race Type indicates the type of race at the national or local level. We wanted to test the different races to see if one type was more predictable than another.

Incumbent Status: Democrat, Democrat OPEN, Republican, Republican OPEN

Incumbent status indicates whether a current party has a seat or if they are leaving the seat because of term limits. Our reason for testing incumbent status was that it would give us another dimension around estimating the impact of an established incumbency or the impact of fresh new candidates running in an open seat race.

Facebook spread

Facebook spread is the numeric difference of likes between competing campaigns. Our theory here was that maybe closer numbers of Facebook likes would be more likely to be inconclusive for prediction purposes.

Our Results:

After testing the variables mentioned previously, we found none of them to be significant predictors of winning. So what does that leave was with? Well although we might not have any good predictors for a winning campaign based on Facebook likes, mathematically we’d still estimate that a campaign leading in Facebook likes would have approximately a 63.2% chance of winning the election. With better and more extensive data we estimate the percent change of winning to be closer to 70%.

Would you like to learn more about using Predictive Analytics in Politics? Download our Top 10 Reasons to Make Predictive Analytics Part of Your Campaign Strategy:


Nate Watson named new President of CAN.

on May 15

Voting with Facebook Likes

on August 22, 2014

Recently we have been working, in conjunction with NorthStar Campaign Systems, with Omaha Public Schools to determine likely support and public opinion leading up to a possible bond referendum in the November election. The results of our analysis, along with the recommendations of other consultants working on the project, were featured last night on KETV NewsWatch 7. Watch the clip here.

Our analysis of the public opinion poll showed the 67% of voters likely to vote in the upcoming election were likely to support an OPS Bond. We were also able to determine the demographic makeup of those most like to support a bond issue.

Those most likely to support the issue have a median income between $36,876 and $48,000, and a median home price between $122,401 and $147,600. Likely supporters are most likely Democrats, and those between the ages 50 to 59 have a higher likelihood of support.

In addition we were able to determine the support for the most needed improvements to the Omaha Public School system. The strongest support being for safety and security upgrades, a high school with a career focus, and air-conditioning in all OPS locations.


Nate Watson named new President of CAN.

on May 15

Voting with Facebook Likes

on August 22, 2014
Why to become a data scientist

Too few of today’s college students realize they want to be data scientists when they graduate. We believe that data scientists are the future, and that we are on edge of a data science revolution. Therefore, we decided to explain why to become a Data Scientist.

1. As a data scientist, you have incredible access across the business.

Your job of modeling specific business strategies and forecasts requires you to have broad access across your company. People look to you to bridge the gap between business theory and relevant data.

This is a tough role because it requires you to develop and implement a strategy to create consensus in order to implement the results of your work. Since the days of the English Luddites (the anti-technology loom weavers) there have been people who are against technological progress and the efficiency it brings to the economy. The best data scientists will be able to manage the political and social change that comes from their work. Data Science success isn’t only about making work more productive, it is also about helping other people adjust and succeed.

Read more…


Why I work at Contemporary Analysis

on January 16, 2012

Why Corporate Hierarchy is Important

on December 4, 2012

On Entrepreneurship, Risk and Uncertainty

on January 8, 2013

Today’s flexible work trends favor the clever, well educated and self–motivated. Trends such as BYOD, MOOC’s, results-only workplace, and Holocracies such as Valve and Spotify emphasis the importance of creative, well executed ideas developed by self-motivated employees.

Flexible work trends have emerged because “scalability” allows organizations to realize large gains from ideas, instead of only operational efficiencies. For example, one clever Tweet can reach millions of people, while thousands of mediocre Tweets can fail to ever be read. Today the value is in creativity — efficiency and even automation are just prerequisites.

Today’s flexible work trends are the opposite of the trends of the 80’s and 90’s that emphasized efficiency and cost cutting: six-sigma, just–in–time, out–sourcing, the great moderation, and leverage buyouts. All of these strategies were about extracting more value from what was already being produced. While, today’s trends and technology place a premium on quality and cleverness over efficiency: typically by creating flexible work environments.

However, today’s work trends are not without problems. We have created a flexible work environment at CAN and here are several of the challenges we have experienced.

Read more…


An Analytical Dashboard: Nebraska’s Workforce

on December 16, 2013

Data Driven Decision Making & Camping

on November 10, 2013

The theory of insurance states that the healthier a population of people, the less they should pay in insurance premiums. Right? We built this dashboard to investigate: do healthier states pay less in insurance premiums? What we found is that average weighted monthly premiums depends less on how healthy a state is, and probably more on average cost of living, geography and political affiliation. What do you think?  Read more…


Generating Sales Leads

on March 30, 2014
10 Questions to Ask Before Buying Sales Leads

Thinking about buying sales leads? Here are 10 questions that you should ask first.

1. What is the minimum purchase?

List brokers try to capture as much of your marketing budget as possible. They do this by setting minimum purchase amounts and charging for filtering: both encourage larger purchases. So while you might find a broker with low minimum purchases, there is a good chance they charge high fees to filter their lists.

The key is to find balance. Often, buying an extra thousand sales leads won’t cost as much as the first thousand. However, you might not want to use them all. You want to avoid using sales leads that don’t fit your target audience, because interrupting the wrong people is a good way to erode the credibility of your brand (and is a waste of your time and resources). Buying names and contact information is the cheapest part of marketing and selling. You should only use the leads that are the best fit for what you sell; even if that means not using every name.  Read more…


10 Questions to Ask Before Buying Sales Leads

on April 7, 2014

Predictive Analytics improves M&A Activity

on January 22, 2013
Generating Sales Leads

Every sales organization requires three things: sales managers, salespeople, and sales leads. In principal, the formula is simple: the sales team will meet their quota if the sales manager focuses the salespeople on the right sales leads.

Most sales organizations know how to find salespeople and sales managers, leaving sales leads. There are 4 sources of sales leads: 1.) referrals, 2.) conferences and trade shows, 3.) inbound marketing and 4.) proactive sales. Each sources has its pros and cons: the key is selecting the right sources for what you sell.

For example, there are businesses where referrals are often the best or the only way to grow. These “word-of-mouth” businesses tend to offer services that are intimate, offer solutions to frequent problems, and have limited marketing resources.

However, most businesses need more than one sources of leads to maximize revenue. Not having the right combination of sources stagnates growth and increases your cost of client acquisitionDifferent lead sources vary in the amount of upfront investment, sophistication required, and payback period.

Read more…

Contemporary Analysis's Quicken Loans Billion Dollar Bracket Challenge Entry

Every so often we get the opportunity to apply our knowledge to something a little different. It’s a great opportunity to break out and have a little fun with our data modeling. (For instance, our Halloween candy prediction last October.)

The Billion Dollar Bracket

In this case, we used our skills to complete a bracket for the Quicken Loans Billion Dollar Bracket Challenge, funded by Omaha’s own, Warren Buffet. Using past game data, and information about each team, we created unique models to predict the probability of each team’s chance of winning a particular match.

For our models, we took into consideration the significant variables that determine the typical likelihood of success for each team, things like assists, free throw averages, turnovers, and rebounds. We then applied these values to the respective teams for each matchup — letting us pit each team against each other more accurately.

Read more…

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