Big Data, Email, and the Presidential Election

“Yes!!!!” was the shout that I heard on the morning of November 11 in our office from my colleague. “What happened?” I asked. “Obama won” was the answer. This presidential election as you know was one of the closest in terms of results and the most expensive in history. What many of you may not know is that the campaign was not won by “horses and bayonets” (I have to admit that was funny). Obama’s campaign was very analytical in its nature and used big data to its advantage.

Obama’s campaign employed dozens of “data crunchers” that analyzed information collected over two years, which helped them “raise $1 billion, remade the process of targeting TV ads and created detailed models of swing-state voters that could be used to increase the effectiveness of everything from phone calls and door knocks to direct mailings and social media.” It helped the campaigners to predict behavior and results, and by this changed the content of the campaign to drive better results.

Remember the contest that offered dinner with George Clooney? Ever wonder why it was with him and not someone else? Results collected from big data analysis showed that women aged 45-49 from the West Coast are likely to spend money on a chance to win a dinner with George Clooney.  Obama’s campaigners looked for a celebrity with a similar profile that would drive similar objectives.

Before we jump into the world of business and marketing, I wanted to draw your attention to the fact that most of the money raised came from voters. It was raised by email campaigns! The data crunchers developed a metric-driven email campaigns. Testing different versions, analyzing results, changing content, resending and so on, were a big part of the success. By the way, Michelle Obama’s emails were the most successful ones.

I won’t delve into the politics of the election, but I am bringing it up as it is a great case study that shows how collection of data, analysis, and implementation of big data helped campaign productivity and success. Apply it to the business world and you have a winner.

Let’s now relate it to business and most importantly to the campaigns we’re running.

Big data is defined by Wikipedia as “a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. The challenges include capture, curation, storage, search, sharing, analysis, and visualization.

If 66 percent of B2C companies ranked targeting recipients with highly relevant content as their number one challenge (Marketing Sherpa), then using simply first name, last name, country, or city in your email campaigns isn’t enough anymore to drive interaction and to engage your customer.


The above image illustrates the data that can be integrated. Some examples are: social, retail, web analytics, purchase information, and many other sources of information. The rationale is clear, the more we know about our clients, the better options we have to drive the right message at the right time to the right customer. Imagine what you could do with integration of social information to your customer profile.

But why do we need it? Our ultimate goal is to increase ROI and lifetime value from our clients.

Putting a theoretical model into a simple and workable practice, here are steps to follow:

Data Sources

The first thing to do is to decide which data can and should be integrated. What is available and how much work is involved. If you have a web analytics platform, social analytics platform, or shopping information available, it will be a good place to start. If you are in e-commerce, or a retailer, the most straightforward way to start is with historical purchase information (product categories, frequency, number of items, total spend). Ensure that your tech team set automated periodic synchronization rules so that the data is always updated.

Set the Segments

Let’s think for a second beyond the usual demographic segments like age, gender, and location. It is important, but we may be missing a client stage of interaction with your brand. Is she a prospect? Did she buy once? Twice? Is she a regular, or a VIP? Or maybe she stopped buying with your brand?

I’d recommend looking into the customer stages as segments:

  • Leads (you can also look into cold leads)
  • First-time buyers
  • Second-time buyers
  • Loyal customers
  • VIP customers
  • Defective customers

Automate Your Programs

The next stage is to set campaigns that will push the clients into the next stage of the funnel, e.g., providing limited-time incentives to newly registered members with the objectives of turning them into first-time buyers.

Eventually using big data models to collect data, process it, and use it in an automated manner will help you to increase revenues and customer satisfaction, decrease the time you’re spending on campaign production, and reduce your customer churn.

Till next time, stay tuned!

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