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When we dig a little deeper into their platforms, though, we find that they’ve collected mountains of data that goes unused. Here are some examples within the Email Marketing industry: Why aren’t email marketing companies able to provide benchmarked retention, click, open and conversion data for consumers and businesses to gauge their success? I should be able to easily see how my list acquisition and retention efforts compare to similar companies with similar firmagraphics to see if I’m doing well or not. Why aren’t email marketing companies able to provide predictive analysis that forecasts sales based on the growth and quality of the subscribers on your email list.
Do you even know the value of your subscribers based on their recency, activity, geography, Mobile Phone Number List and demographics? Why aren’t email marketing companies able to build central email repositories that automatically update email addresses across accounts, or remove them when they bounce on one account? Why doesn’t the email marketing company ask them if they’d like to update their information across all shared clients on a single platform? If you begin to dig into the data, you’ll immediately see how absolutely amazing it would be to have these processes and data for any company. Imagine the decisions you could make based on having access to the intelligence across all marketers rather than the silo of your own lists? Here are some examples within the Social Media industry: Why doesn’t a platform like Twitter build link intelligence? Regardless of any shortener or who promotes a link, that would provide a complete report to businesses on the impact of their content, promotion, and advocacy programs.
Imagine being able to see a fabulous tree of data that provides the lifetime of a link – from generation, to sharing, to reach, to clicks… across every Twitter user who shared or retweeted it?! I mentioned this to a business last week and they said they would absolutely pay for access to this data. Instead, Twitter doesn’t provide anything and we’re forced to rely on dark data and link shorteners to try to trace back the impact. Here’s an absolutely amazing example from Foursquare. When Chipotle had issues with food safety, Foursquare was able to monitor trending foot traffic across stores and ultimately, predict losses: chipotle-foot-traffic The result? Chipotle has announced its first quarter earnings and Foursquare’s predictions were on target – with a 30% drop in sales. Foursquare was not only able to predict losses, they’re also able to make an even bolder prediction: We believe the 23% decline in same store foot traffic is the more meaningful number that shareholders should focus on, rather than the 30% decline in sales. It shows that Chipotle is building trust back with customers, which is more important to its success long-term.
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發表於 14:22:56