May 26, 2016
While sleep rituals and pricing science may seem like two very different topics, the need for more evolved methods to understand and execute on data is absolutely crucial to both.
From being a research assistant in a Climate Sciences lab, to researching at a policy organization, to being a part of the research team at Vendavo, good data has proven to be instrumental in creating credibility and presenting results worth viewing.
In almost every field, the quantity of and avenues to accrue data, have intensified. Take sleep statistics for example. Just in the past decade, looking at a mobile device in the middle of the night has become acceptable, if not compulsory, upon hearing that little ding or buzz. Work used to be limited to the work-day, plus possible urgent—yet prescribed and intentional—voice-only calls after hours. Today, the obstructions are limitless.
I am guilty of it, as I am certain many of you are. Sure, there are natural reasons for waking up in the night. However, all too often, it is our phones that are causing our ocular system to be ultra-stimulated from bright unnatural light, our auditory system being piqued due to an abrupt sound, and above all, our minds spinning with an endless stream of thoughts from these disruptions. While it could be a sound from an app that you do not care about, it is often a work e-mail from someone in a different time zone, or a direct team member working late at night. Beyond what happens once you are awake, there are factors leading to one’s night of sleep that are causes for disrupted sleep—high visual stimulation from a bright screen until one falls asleep, lack of a defined sleep ritual, or doing work in a sleep area.
So what do sleep scientists do to accurately track this altered way of sleeping? Traditional sleep-tracking methods to collect data won’t work. Wired said it right, “You’re probably not going to send that work email at 1:00am when you’re in a sleep lab, or own up to it on a questionnaire.” The University of Michigan opted to use a more embedded and natural lifestyle method to collecting sleep data on people—a phone app.
The idea of needing innovative ways to keep track of changing variables applies to almost any field. Just like requiring improved ways of collecting increasingly complex sleep data, the ways to analyze historical pricing data in order to set and execute optimal prices have changed. To stay in the game, it is required to have organized and accurate data to gain historical insights and set future targets. This is not a luxury or futuristic concept; it is an evolved necessity. Like the obstructions to a good night’s sleep, the routes to finding the most optimal price have changed. Raw material costs, foreign exchange rates, macro and micro economic disturbances, discounting, and contracts—the factors going into the best price are endless. Customers and competitors alike are more informed, and without staying ahead of them, sales teams are missing on margins.
It’s not that our methods for collecting sleep data have improved while sleep patterns have remained the same. Sleep patterns have changed as the methods have merely evolved to keep up. Likewise, pricing science must evolve to keep up with the ever-changing, numerous factors that complicate finding the optimal price and avoiding margin leakage. With time has come advancements in technology and manufacturing, all contributing to higher competition and greater knowledge sharing. The demand has never been higher for innovative solutions to accurately account for these fluid and multiplying variables in the pricing world. It’s time to adapt.