Personal Quantification (PQ) is the use of mobile apps or tracking devices (Fitbit, Trax) to monitor, and sometimes react to, personal statistics. People are using PQ to lose weight, run faster, and manage their finances. PQ’s data-focused learning also presents opportunities for research. From Wikipedia:
The Quantified Self, also known as lifelogging, is a movement to incorporate technology into data acquisition on aspects of a person’s daily life in terms of inputs (food consumed, quality of surrounding air), states (mood, arousal, blood oxygen levels), and performance, whether mental or physical. In short, quantified self is self-knowledge through self-tracking with technology. Other names for using self-tracking data to improve daily functioning are self-tracking, auto-analytics, body hacking, self-quantifying, self-surveillance, and personal informatics.
PQ often involves self-comparison (how much faster did I run today compared to yesterday?) OR social-comparison (how much less do I spend on clothing per month than a typical person my age?). It’s a growing trend and people trust it. According to Future Source Consulting, the worldwide wearable devices market was worth $5.9 billion at retail. Nearly thirty million wearable devices were sold globally in Q4 2016.
The human desire to compare, compete, improve and evolve hasn’t changed, but oodles of data is energizing it.
Now, four ways that Personal Quantification can be used in research…
As a recruitment tool
For marketers seeking to understand their customers (or potential customers), self-trackers are a good place to start. In-app behaviour provides “digitally hard” evidence that these are in fact, hardcore customers, or at least hardcore runners, dieters, or lovers of their babies.
If in-app behavioural data is combined with online surveys, focus groups, or bulletin boards, brands can get an increasingly in-depth look at who these hardcore users are and what drives their behaviour.
As a data source
Combining behaviour-oriented PQ data with attitude-based survey research or transactional-based customer analytics can help brands determine need-states, purchase drivers, and the ways attitudes lead to changing behaviours.
A sample of self-trackers is rife with self-selection bias, but it’s a sample that’s deeply engaged, which in turn may mean higher participation rates and lower costs.
As a measure of engagement
As PQ becomes increasingly prescriptive, customers may be more predictable. For example, a grocer could benefit from the incidence of using a dieting PQ-app, as users could be prescribed certain foods or a calorie count for that week. Aiding that journey will be key.
RESEARCH TIP: When trying to measure a behaviour that someone may feel sensitive about, ask about recent behaviour, not regular behaviour. Consider these two versions of the same survey question.
On average, how many times do you exercise each week?
How many times did you exercise last week?
Sure, you “normally” exercise 5 days a week, but last week it was only 3 because you’re busy and life gets in the way … but doesn’t it always?
As an incentive
Desires to compare, compete, improve and evolve drives usage of PQ. Before offering a $5 gift card or “a chance to win” in exchange for participation, consider appealing to customers’ competitive side.
A key piece of data, such as where users stand in comparison to other participants on physical fitness, movie knowledge, or mobile data consumed may be a cheaper and more engaging way to generate insights.
Although, Personal Quantification, or “Little Data” may not provide the broad changes that Big Data promises, Little Data is reaching people on a 1-to-1 basis – helping them lose weight, run faster, and manage their diabetes. This data can help researchers understand these journeys and help marketers support their progress.