All posts by Nate Watson

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.

Testing:

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:

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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.

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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.

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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.  (more…)

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 acquisition. Different lead sources vary in the amount of upfront investment, sophistication required, and payback period.
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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.
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