What Does Big Data Mean To You?
I recently participated on a panel at Brand Innovators E-commerce. The title of the panel was “Big Data Meets E-commerce,” moderated by Mike Peralta, COO at MediaMath, and it included panelists from Kraft, Dell and New York Life Insurance. The panel discussion provoked thoughts around the value of each user, customer interaction, ad creative, and […]
I recently participated on a panel at Brand Innovators E-commerce. The title of the panel was “Big Data Meets E-commerce,” moderated by Mike Peralta, COO at MediaMath, and it included panelists from Kraft, Dell and New York Life Insurance.
The panel discussion provoked thoughts around the value of each user, customer interaction, ad creative, and the impact on one channel on another, from TV to online.
However, what I really started to think about later that day was not just data meets e-commerce, but this idea of Big Data.
What really does this all mean and how should we define it? The fact is, data is defined differently by different groups of people – definitions that are driven by their own personal and corporate goals for using the data.
From an e-commerce-based company to a search marketer, from display teams within an ad agency to a luxury brand, data is at the center of business decisions today.
Yet, while data is used differently, the goal is often the same –to drive more effective and efficient marketing messages through enhanced relevancy and data-driven advertising.
For me, I see search data as the highest indicator of intent, but for a luxury brand, it might be more focused on demographic data. Using data to further your campaigns and initiative is great, but only if you are diligent about which types you are using and strategic in the way you match data to your goal.
Define Your Objectives & Pick Your Data Accordingly
If you’re running a branding campaign with a goal to get in front of 18-34 year old males, you should be using demographic data. Companies like Bluekai, Exelate, and others offer data either a la carte or through their DSP partners. This idea of demographic data was one the initial topics in the data game.
However, demo data only goes as far as narrowing down the age range and gender of the audience. Alone, it is great for awareness campaigns, but its value is diminished if your campaign focuses on driving lower funnel activities.
If you’re looking for large swaths of users who have visited specific types of sites, and you feel that a user who has visited those types of sites are in market for a specific product, you should be using behavioral data. Behavioral data is a bit more “lower funnel” than demo-based data because a user has visited sites that seem to indicate an interest in a particular product or service type.
However, let’s be sure we carefully define interest-based data. It can be difficult to say that just because a user visited Rollsroyce.com as well as similar types of sites, that the person is in market for luxury goods.
For example, I visit Rollsroyce.com all the time, and my NYC apartment is about 200 square feet. I literally couldn’t even fit a Rolls Royce into my bedroom, let alone afford one. I visited the site and sites similar to it, but I’m more interested in checking out the latest model than actually purchasing one. If Rolls-Royce is focused on driving awareness, then I’m a key audience, but if they are looking for an actual purchase, they may have wasted their marketing dollars.
If you’re looking for users who are in market for a specific product, you should be using search data to target your campaign. The reason that SEM works so well is because users who are searching for your product are in market for it.
Search Data & Search Retargeting
Using search data outside of search engines is what search retargeting is all about because you can target users who have searched for your product once they leave the search engine. While search data is great for purchase intent, it must be combined with demo-based data if demographics are important to the performance of the campaign (which is not always the case).
For example, we can tell that a specific user searched for “fake teeth” and is therefore in market. However, a college student searching for “fake teeth” is probably looking for some accoutrement for their theme party costume. A 70-year old searching for the same term is looking for a very different product.
Additionally, search data can also be used to target based on interest. Let’s use Rolls-Royce as another example. If I visit mototrends.com and search for “Rolls Royce,” the search preformed is likely more interest based than intent focused. While search data drives response, it’s also become a vehicle for targeted brand awareness.
Long story short, this idea of big data is too large to be defined.. Whether you are an e-commerce company, an ad agency working with direct response or branding campaigns, luxury advertiser or so forth, many aspects to data might apply. Before you settle in on your data sources, think about the idea of “big data” and what it means to you.
(Editor’s Note: Magnetic has a new whitepaper on the Future Of Search Retargeting available for download here.)
Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.