# data[].attributes.body
Shops in big quantities; Potentially has a family

# data[].attributes.body
Like pre made food

# data[].attributes.body
Healthier than no. 1

# data[].attributes.body
Eats less healthy food

# data[].attributes.body
Cares more about taste and convenience than health and quality of what they eat

# data[].attributes.body
More wealthy (fancier products and a car)

# data[].attributes.body
Might be younger (fancy trendy coffee)

# data[].attributes.body
Cares about health and quality (willing to pay more for it)

# data[].attributes.body
Doesn't cook as much

# data[].attributes.body
Can cook (they have cooking supplies)

# data[].attributes.body
Hydrated, and always eating healthy organic food.

# data[].attributes.body
May exercise and spend time outdoors

# data[].attributes.body
Always moving (Meal supplements and water bottle suggest they don't have time to sit down to a proper meal sometimes)

# data[].attributes.body
Possibly wants to loose weight (diet)

# data[].attributes.body
Most likely lives close to grocery store to get pre-made meals

# data[].attributes.body
Looks for convenience (pre-made meals)

# data[].attributes.body
Cares about nutrition

# data[].attributes.body
in his early 20's (maybe in college)

# data[].attributes.body
Potentially can only afford lesser quality food

# data[].attributes.body
Wealthy enough to own a car

# data[].attributes.body
Likely a family (roast chicken, whole pizzas, etc)

# data[].attributes.body
Interested in Perrier

# data[].attributes.body
May live in a city with less large grocery stores

# data[].attributes.body
Shops at Sobey's

# data[].attributes.body
Heavy user of perrier

# data[].attributes.body
likes pizza

# data[].attributes.body
younger, well-educated city singles

# data[].attributes.body
Young, active

# data[].attributes.body
younger, upper- middle- income suburbanites

# data[].attributes.body
Young, active, interested in art, low income

# data[].attributes.body
Busy, Low income, Eat fast food

# data[].attributes.body
young, single urban renters

# data[].attributes.body
Active, low income, young

# data[].attributes.body
diverse, lower-income , younger city- dwellers

# data[].attributes.body
-Live in a city, near grocery store
-Large household