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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.

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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A Celebration of CAN’s Best Ideas

on February 11

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Once again it’s that time of the year when kids and parents prepare to hit the streets of Omaha in their ghoulish best looking for the most candy they can find.

For a kid, Halloween is one of the most (if not the most) exciting times of the year. They get to dress up, act like their favorite characters, and eat lots of candy. What could be better?

For a parent, Halloween can be a stressful event. Having to travel up and down several blocks to find enough candy while managing hyped up kids, and on top of that cold weather, can make for an awful mix on Halloween night. But what if you had a way to scout out the best neighborhoods around you to find candy before hand?

This year for Halloween, the data scientists at CAN took on this challenge to help parents and kids find neighborhoods with the most and best candy.

Using Block demographic and spatial data from the 2010 Census and Halloween spending data from the 2012 National Retail Federation, CAN’s data scientists developed a predictive analytics dashboard that allows parents to get a high level view of the expected spend on candy in their neighborhood as well as surrounding neighborhoods. Alongside spending, an index of household density is also included to show which neighborhoods may hand out the most candy on Halloween night.

“Last year we realized we could use the data we were gathering on household demographics and combine it with estimated spending data on Halloween candy for each neighborhood,” Tadd, COO and head of data scientist says. “I worked with another data scientist, Matt Dickinson, who built the concept of the dashboard last year for the Greater Omaha Tableau Users Group, to publish this fun dashboard and give parents the upper hand when planning their Halloween night.”

The tool is interactive and lets the user choose which part of town they are in or which neighborhoods they want to compare. “This is very similar to some of the visualizations we develop for companies to understand their customers and help guide strategies for developing better marketing campaigns,” continues Tadd. “I am happy we get to use our expertise to showcase some fun analytics as well.”

So if you want to take a proactive approach to getting your little ghouls’ bags filled with candy, use this predictive analytics dashboard to make your Halloween night boo-untiful!


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When should you Update Predictive Models?

on December 17, 2012

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on January 21, 2013

After writing my previous post, “How Often Should You Update Predictive Models”, it was appropriate to followup with a post regarding the consequences of not updating predictive models.

Predictive models use the patterns in historical and transactional data to identify risks and opportunities. Since the conditions and the environment are constantly changing the accuracy of predictive models need to be monitored. Once a predictive model no longer reflects reality it needs to be updated. Most of the time this is because the assumptions behind the model need to be updated.

Take for example a community bank. Internally every new transaction, deposit, withdrawal, application, or transfer creates new data. For most individuals, these transactions are occur several time every day, and that means you’re compiling thousands of new data points. Over time the customers environment is changes, this  is reflected in each data point collected. Did they get a raise or a new job? Is there car breaking down? So although this community bank may have a relatively modest customer base, their customers are experiencing change all the time.

Also, their are external changes that impact a customer’s behavior. For example interest rates change, new competitors enter markets, competitors invest in marketing, consumer confidence changes, and competitors merge. It makes sense then that they would need to update their predictive models to keep up with all of these changes. When these changes start to represent structural changes a new model needs to be developed.

For a typical community bank, strategic sales, marketing, and planning decisions happen at least once a quarter. If a bank doesn’t update their predictive models in preparation for these events, they are at a high risk of using obsolete information when making decisions.

What are the consequences of using this obsolete information?

  • Your pricing models don’t reflect changes in the competitive environment.
  • You recommend outdated products.
  • Your marketing material isn’t targeted at the right groups. They might not exist any more.
  • Your business development team begins chasing the wrong types of leads. For example, it might not be a profit environment to pursue new home mortgages.

So if you’re planning on making an investment in predictive analytics, make sure you consider the implications of using your data as well as the consequences of using outdated information.


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Share of Wallet & Predictive Analytics

on August 7, 2013

Many people credit the rise of predictive analytics to the technological advances of the last 50 years. However, The history of predictive analytics starts in 1689. Its true that record keeping standards, relational databases, faster CPUs, and even newer technologies such as Hadoop and MapReduce have made predictive analytics an accessible tool for decision making. However, the history of predictive analytics show that it has been used for centuries.  Read more…


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About 20 years ago, a sociologist named Scott Feld discovered an interesting phenomenon where on the average, people have less friends than their friends do.  However, most people believe they have more friends than their friends do.  This is the paradox.  The friendship paradox is a form of sampling bias. Read more…


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The Predictive Analytics Revolution – Are you sitting on the sidelines?

on October 10, 2012

New clients often have questions about why and how frequently CAN needs to update their predictive models.  Predictive models need to be updated because everyday new data is being created.  For example, your customers are buying more, subscribing or unsubscribing.  The environment is constantly changing.  While predictive models can handle a lot of new new data, overtime environmental changes build up causing predictive models to lose their effectiveness.  After a month, quarter, or a year it is necessary to update predictive models with new data.

As these new patterns emerge its important to periodically take time to investigate your data, update your models, and challenge your assumptions about your business. But how often should you do this?

Read more…


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I recently had to make a deposit and fix a small issue with my bank account. I think my community bank has maybe 5 locations in the entire midwest, which means that everyone is cheery and I can always expect christmas lights in the winter and maybe cookies on the table when I walk in there. The staff are attentive and wonderful and always call me sir. “Is there anything else we can do for you, sir?”

Yes. Yes, local community banks, there is something you can do. But it’s less for me and more for you.

The smiles are great and sometimes I consider visiting the bank if for no other reason than to be surrounded by people who’s job seems to be to boost my self esteem at all costs and make me forget the cruel, harsh realities of this world. It is icing on an otherwise mundane task of paying the bank a visit.

But do you think this is why customers are staying with you? Read more…


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Since 2008, we have been helping our customers learn how to get more sales from their current customers.  One of the quickest, easiest, and most profitable ways to start is using the data you already have.  Using data from your accounting and CRM system it is possible to determine which of your current customers can be made more profitable, more loyal, and which are the most likely to buy more from you.  Learn more, Download our Case Study.

Contemporary Analysis specializes in using predictive analytics to forecast consumer behavior.  Using a statistical technique called multinomial logistic regression, we can use patterns in your past data to predict future events. Read more…



Grant Stanley, CEO of Contemporary Analysis, stands in front of his wildly smart team at a recent company meeting to unveil his strategic plan for 2013. The plan includes comparing critical performance numbers internally against given benchmarks. Grant and CAN’s HR Director Nino Natasti have developed ways to track and classify the active SaaS projects in our pipeline, the productivity and workflow of our Data Scientists, and the traffic on our website in order to “determine whether or not we are adding meaningful value to the company and our people in the coming year.”

One data scientist astutely remarked, “It’s like we’re taking our own medicine.”

Speaking of medicine, I would say CAN’s operations are akin to operations in a hospital. No we don’t save lives every day, but just like a team of medical professionals, our data scientists use industry-leading technologies to inform decision-making processes. With your data, we can build decision-making models to predict the most important factors in your business.

And we won’t let red tape and redundancy stand in our way. There is no doubt we’re ready to solve your problems. Our internal analytics department and HIPA, FERPA, SSAE16, and PCI compliances are in place to make sure your Business Intelligence tools are accurate, effective, and easy to interpret. We’ve checked into the new age of Analytics, have you?


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The greatest improvement in the productive powers of labour, and the greatest part of skill, dexterity, and judgment with which it is any where directed, or applied, seem to have been the effects of the division of labour. – Adam Smith

Ever heard of Instagram? How about Reddit, Urbanspoon, Pinterest, Spotify, Dropbox, Tumblr, Netflix, or Yelp? These companies, and many others, owe their existence to a young technology quietly advancing the web.  The technology is Amazon Web Services (AWS).  The impact has been enormous.  Amazon Web Services (AWS), an umbrella for a handful of cloud computing platforms, has democratized super computing for anyone with a visa credit card.  Now, companies can get their websites and products delivered faster and cheaper than ever before, because they only pay for what they use and when they use it.

Technologies like elastic load balancing and auto scaling allow anyone to use, and pay for, only what they need of the processing or storage capabilities of AWS. Rates start at $0.06 per hour. Security, maintenance, and backups are also taken care of so you can rest easy knowing that the nominal rate of pennies-per-hour also buys you peace of mind you wouldn’t have with the massive overhead of running your own computing platform. These benefits have made AWS the go-to-standard for things like modeling DNA, aerospace research, jumpstarting web companies with between 0 and 1,000,000+ users, and countless other uses.

Contemporary Analysis brings the same ethos to data science. How would your business change if you had the ability to immediately solve the most mission critical problems, predict future scenarios, or run a few experiments without worrying about the nightmare of setting up an expensive, massive, in-house data analytics team?

With Contempory Analysis, you have on-demand access to some of the brightest minds in data science with backgrounds in economics, engineering, finance, law, philosophy, and neuroscience on a monthly, quarterly, or yearly basis to help solve your most pressing business questions. And we start right away.

Most of our solutions start at prices that don’t require an OK from upper management. So pull out that Visa and we’ll show you what we can do.

Additional Reading:

The Economics of AWS vs Owned Infrastructure



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