A guest post from Deren Baker, CEO of Jumpshot and Randy Antin, VP of marketing.
Spending on analytics is expected to skyrocket over the next three years, from 4.6% of budgets to nearly 22%. Despite this, marketers only use about one-third of the data they collect. As data becomes more critical to successful marketing, marketers must learn to parse usable data from fluff.
When I buy a car for my family, I want to know that it has seven seats and gets 32 miles per gallon. I don’t care whether it goes from zero to 60 miles per hour quickly. All those data points might be accurate, but if they aren’t relevant to my interests, I don’t need them.
Marketing data works the same way. As companies’ customer relationship management, or CRM, data becomes more robust, including points such as purchase history, GPS data and browsing activity, it’s not a question of whether the data is there but of which parts of it to use. The answer to that question depends on which programs marketers run, what they can integrate and how they perform.
The importance of upstream analytics
Web analytics solutions such as Adobe and Google are valuable, but they don’t provide complete views of consumer activity. Because they restrain themselves to last-click attribution, the customer engagement funnel is limited to a single step prior to the customer’s visit to the marketer’s website. For direct response programs, this is enough, but other initiatives need more information.
Enter upstream marketing. Using upstream analytics, marketers can track the customer journey across multiple websites to bring clarity to the funnel process. Upstream analytics works the same way as desktop and mobile tracking: Data science teams use algorithms to look at a user’s browsing history to see all the steps he or she took during a specific time frame before visiting a website, filling out a form or taking another specific action a company identifies as important.
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