Though some might object, most marketers would likely agree that relevance is an important factor in driving optimal email program performance and revenue. Marketers are frenetically capturing, then filtering terabytes of customer data in an attempt to find that needle in the haystack that will tell them where/why/how each individual is most interested. The difficulty is that there are an almost infinite number of data sources, and much of it is only relevant at the point in space and time at which it is captured. Given the time it takes to structure and filter the data, then trigger or launch a campaign, the opportunity to most effectively leverage it is often long passed.
Typically, marketers do not use their email database as their Database Of Record (DBOR). Instead, customer data are captured from various points (web clicks, views, purchases, forms, social, apps, 3rd parties, etc.) and stored in a DBOR, while email interaction data (opens, clicks) are stored within a separate email database. It isn’t until all these data are merged that we can begin to tell a story of the individual customer. Though some claim a “real-time” connection between the two, most email platforms only merge the data once every 24 hours. In our era of perpetual motion and continual connectivity, 24 hours, or even 2-4 hours is sooo 2000-LATE. Static and historical data certainly has value, but it is often out of context to the present moment.
Marketers understand this challenge, and many have turned to predictive analytics to optimize relevance. While this often does bring some lift, one quickly reaches the ceiling because the practice still largely relies on static or aging data points, continual data refreshes, and a big fat guess. So much time, effort, and budget is expended in trying to control the data, and usually it’s a losing fight. Marketers can’t control data any more than we can control the individual customer. Truthfully, the only one controlling customer data is the customer. The customer has ultimate control over when the email is opened, where it is opened, or on what device, and such contexts are some of the most important factors to relevance. If you believe that, then you probably also have to believe that the most critical moment occurs not when the email is sent, but rather when it is opened.
Marketing is a continual evolution, and those at the front of the March of Progress have grown beyond Predictive and have begun using Agile email solutions, such as Movable Ink, that detect and leverage “time-of-open” data, such as time, location, device, even the weather to render contextually-personalized experiences. For the uninitiated, I’ll explain by way of example… In the average database, my profile would tell you that I’m male, 38, live in the Bay Area, where it is often cool (I don’t mean in a hipster way…grrr) and foggy, and past purchases indicate a penchant for outerwear. It might seem a reasonable assumption for a retailer to send me an email compelling me to take advantage of a remnant inventory sale on Member’s Only windbreakers, and if I download and purchase via their super-slick mobile app, I can celebrate an additional 15% off! I’d be powerless to resist, right?
But remember, only the customer controls the data. It just so happens that I’m writing this blog post on my laptop, while visiting clients…in Chicago…where it’s presently 86F with 90% humidity. Given my current disposition, what would I want with a jacket…or an app download? (I mean, I would want that Member’s Only jacket, but sure as hell not right now.) Time-of-open data would have told the marketer that I’m not home, it’s hot where I am, and that I’m reading it on a laptop rather than an app-enabled device. What I really want right now was the t-shirt/shorts email, linked to the specific product page. Predictive Analytics can’t predict such vagaries and variables. Open-time optimization can.