Consider an individual who posts often about the highbrow shows he’s watching. If you peeked quickly at his Netflix reviews you’d find a list of documentaries and period dramas and you might leap to some fairly quick conclusions about this individual’s demographic. Passive monitoring, however, would take it one step deeper and look at what he’s actually watching to find out that, yes, he did watch the documentaries that he reviewed, but he also watched “Dumb and Dumber” multiple times, along with the entire Three Stooges collection this month, so a different profile emerges.
Passive data collection can help marketers draw a more well-rounded picture of an individual or population.
Passive data collection
The Marketing Research Association notes that passive data collection “occurs without any overt consumer interaction and generally includes capturing user preferences and usage behavior, including location data, from personal mobile devices.”
As the market research industry adopts more mobile strategies and methodologies for data collection, passive data collection is a tactic that’s being used more often. Passive data sources include clickstream, search, and social data along with details such as app usage, location, mobile browsing behavior, any kind of search that’s going on, and depending on permissions, social media posts and activity. It’s even possible now to get information on battery life of the device being used.
The kind of data that’s available to marketers now is getting to be too big and broad to stand up to traditional strategies. For example, if you have user identification and timestamp information for various activities for search and mobile browsing as well as social activity, it would take manual analysis by a team of people to look at a website or URL to determine what high level categories various activities falls into. Introducing automation is the logical next step to becoming more efficient in market research.
Text classification engines
When humans are relied on to look at and classify data, there is always inconsistency, human error, or ambiguity in judgement. A machine is consistent, predictable, fast, and reliable, and allows you to be confident in the fact that each activity is classified into the proper category and assures you that you’ll have consistency across all channels (search, mobile, social.)
With structured data, you can now add the human element to analyze it more quickly and make good decisions about how to act on what the data is telling you. There are some interesting applications for passive data collection. KantarHealth published a helpful infographic that illustrates the application of passive data collection techniques in the healthcare industry. By pulling data from sources as diverse as social media channels and biometric devices, Kantar can help their clients deliver better user experience to patients and improve quality of life.
eContext is a text classification engine that allows you to take any kind of of text data—from any source such as click stream, search, social—and wind up with data classified, in real time, in a uniform fashion. eContext features particularly deep classification, to 21 tiers and into more than 450,000 available categories, so you can draw deeper, more accurate conclusions about your data, and move onto step two, which is pulling insights from the data and making smarter marketing decisions with the results.
eContext’s ability to bring a heightened level of clarity to your unstructured data means you can have faster, more effective access to your data and the marketing insights it can give you. In the face of more traditional research options, including digital research techniques, passive data collection combined with accurate data classification can give you an amazing edge and help you make valuable connections faster and with less of a financial investment.