data

Differences in Data

Have you ever been pre-judged by someone before they had a chance to really get to know you? When I look at where data-sets are today, that’s kind of how I feel. We are taking our best guest at personifying individuals based on a series of various data sets that semi-fit together. In our last blog we talked about the importance of content personalization. If we are going to get there, data interpretation is just as important as the data collected about a person.

Different Types of Data

Qualitative vs. Quantitative

The biggest distinction in reading quantitative vs. qualitative data, is whether something can be easily categorized or not. Quantitative data is data that can be categorized numerically. Your shoe size, your height, your income, your zipcode etc. etc. In other words, it’s the demographic information that can be easily collected.. Qualitative data, however, cannot be categorized numerically. In the words of Isaac Newton, “every action causes a reaction”. This reaction, or emotional response can be classified as qualitative data.  

Target, for example, takes to the twitter-world to see the emotional reactions to new product launches. They collect this qualitative data about the individual responses to better understand their customers and move one step closer towards content personalization.

These two data sets go hand-in-hand because you can infer many correlations such as, location eludes towards cultural responses, affluence levels indicate certain buyer behavior, and so on.

First Party Data

Finders keepers! Anything you collect about your customer, is yours to keep. This means your brand gets the first glimpse into your customer’s interaction with your brand. First Party Data is one of the most valuable data sets because you can deploy any data aggregation strategy to understand the exact relationship between you and your customer during the buying journey.

For example, if you want insight into how your customers interact with your website, deploying a heat-mapping strategy to collect data on what images individuals click on might be the best route for gathering this first-hand intel.

Second Party Data

Second party data is like the ultimate tease. A customer may be in a data relationship with someone else, but you’re still benefitting from that relationship. For example, a customer releases the rights for Google AdWords to track their search history but you’re still benefitting from that same relationship with AdWords. It’s common for brands to strategically partner in a data sharing strategy to obtain information that otherwise might be too costly to collect on their own. This is why second party data becomes valuable, and knowing what data you’d need to further complete the personalization puzzle will help define the strategic partnerships you can create.

Third Party Data

This data is the most widely adopted data collection strategy. Marketers depend on data collectors to aggregate intel on customers that they can use to develop a variation of marketing strategies. Unfortunately for third-party data, it’s becoming less common in strategy development as marketers want more first-hand insight aka. first party data.

Knowing what type of data you are collection, can help you figure out what pieces of data are missing that will help you complete the puzzle towards content personalization.

References:

http://www.huffingtonpost.com/advertising-week/turning-intentions-into-c_b_8137128.html

http://www.getelastic.com/beyond-product-recommendations-big-datas-role-in-personalization/

http://www.b2bmarketinginsider.com/strategy/are-you-using-first-party-data-to-drive-personalized-customer-experience

http://www.emarketer.com/Article/Marketers-Put-First-Party-Data-First/1012663

http://marketingland.com/can-marketers-find-best-customer-data-noses-139308

http://marketingland.com/second-party-data-digital-marketers-128254

http://www.signal.co/blog/data-sharing-second-party-data/

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