Why Marketers Love Big Data and Where to Start

Google just launched what it is calling The Consumer Barometer used for providing insight about customer behavior online and offline. The data provided in this tool comes from the Consumer Barometer study and the Enumeration study. It was designed to provide businesses with big data results about how often customer’s research and purchase certain products and services online in addition to consumer behavior trends. Guess what? Marketers are going to love this tool!

Website Magazine recently cited that more data has been created in the last 30 years than the last 5000. We’re living in a world where the amount of data available is exploding exponentially, and it’s not slowing down. If you’re in marketing, you and your staff need to be able to easily and effectively analyze large sets of data in ways that allow for insight. These insights need to provide enhancement to the personal user experience on your website while boosting the profitability and success of your company. Welcome to “Big Data.”


Big data is not only difficult to acquire, but it’s difficult to use correctly. When the stars align though, it can boost a company’s ROI by as much as 20% according to this business insider article sourced from McKinsey & Company. Big data means different things to different people and different industries. A pretty comprehensive look at the topic of big data can be found here, where the term is not defined by the size of a dataset explicitly, but rather the plain fact that traditional database architecture and software is inadequate to store and analyze the volume of data. If you’ve got more data than your traditional system can handle, you’ve got big data. As the meaning of the term morphs with its more widespread adoption, big data has come to refer to the task of taking an enormous dataset and applying it to solve a business problem. Here we will focus on what big data means to marketing professionals and 4 reasons they love it!

Big Reason #1: Big data allows marketing teams to gain deep insight into the backgrounds of prospects or customers by providing comprehensive datasets about consumers and mining them for patterns in the information. This is different than taking a random sample and extrapolating; it’s looking at ALL of the data and finding real patterns.


Big Reason #2: Big data lets your brand speak to the omni-channel consumer and cater to them. By segmenting consumers and creating user profiles then feeding user profile data back into big datasets, your brand can create personalized experiences across multiple brand touch points and channels. Essentially you can achieve marketing agility and personalized marketing experiences per user. An extremely detailed webinar about how to turn big data into personalized marketing experiences can be found here.

Big Reason #3: Big data lends perspective on your entire operation. If you have standardized measurements across multiple consumer channels, you’ll gain a comprehensive understanding of these channels and how your consumers interact with them, how many customers you have, how the data changes over time, purchasing trends and more.

Big Reason #4: Improvement! If you know what your customers want combined with an overall vision of your brand’s marketing channels, you can identify and improve performance in areas that need it.

Stepping up to Bat with Big Data

Consumers should be able to expect the same from your brand no matter where they interact with it; at the same time personalized 1:1 brand experiences are the only way to increase conversions. The goal is to create uniform branded experiences, personalized to a particular user, across all brand touch points. Big data allows you to do this. The more you understand about big data the more you are going to be able to use it to your advantage.

Marketers, let’s get started.  Identify brand touch points and divide them into buckets such as onsite real estate (website, mobile website, mobile app), offsite real estate (social media, search engines, banner ads, emails, in-app ads, native ads) and offline (any point of sale that is not included in the digisphere).


Decide on what you want to measure and approach companies who leverage big data in these areas already and gain access to their wealth of consumer resources and gain insight into your own customer base. There are a lot of companies that have successfully created new revenue streams by selling their large sets of data to major industry players. If you don’t have any data beyond your Google Analytics, looking at the standardized datapoints of big data companies will provide you with clues on where to start. Additional reading about big data and customer experience analytics is highly recommended before getting started.

Once you have identified and categorized all your brand’s measurable touch points and you have standardized the parameters you wish to measure, you’re in a pretty good place to set up a data layer and create the architecture to house and manipulate it. Your data layer will be an intelligent collection of data from all your touch points, combined with a relevant dataset, organized by standardized categories. Think aggregate, manipulate and innovate. The actual way in which you funnel and fuse all these different types of data into a centralized database and then make use of it is the tricky part, despite having a clear idea of what you want to measure and where. If you’re at this stage, contact a company that specializes in enterprise-level data engineering such as Ensighten or Fractal Analytics. The point of collecting data points from all these sources is to be able to use them. Marketers are used to doing this by hand, but as a recent Forbes article points out, we are going to have to start collaborating with machines to decode terrabytes of data and build competitive brand messages.

Let the Data Speak for Itself

Chromogram of 1.2 billion pieces of Wikipedia editing data

Chromogram of 1.2 billion pieces of Wikipedia editing data

For many marketing professionals the unfamiliarity of this area will mean that they are largely experimenting with big datasets: their creation, visualization and manipulation. Once you have a large dataset up and running, who knows what the data will say and what your experiments will uncover about your consumers? Sophisticated sets of data should help you to innovate and create new business models, products or services. Overall your experiments should discover customer insights that allow you to improve brand performance.

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