Survey of potential users for Snapbasket platform (snapbasket.co), a project out of the USC Annenberg Digital Design and Innovation Practicum

1. SNAP basket Initial Survey Results
Executive Summary
To get an initial look at characteristics and needs of our potential market, we
used SurveyMonkey to conduct surveys of people in the United States 18+ who
make less than $50,000 a year (two lowest income level brackets on Survey Monkey
are $25,000 and $50,000) and filtered the results by those who have been on EBT as
a phase one of research.
We are also partnering community organizations to schedule in-person focus
groups due to the nature of sensitivities of being a SNAP benefit recipient for phase
two of research.
Methodology
We chose to use some of our budget to buy survey results from SurveyMonkey.
SurveyMonkey has had a successful history of getting the right audiences to take
surveys through incentives such as prizes given and charitable donations given
on behalf of survey-takers who disclose demographic or consumer
characteristics. This is a common practice among companies and advertising
agencies such as Prezi, Ogilvy, and 99designs.
o Survey Design and Questions
We designed a short simple survey to capture purchasing behavior, get
insight on what people would want in a technical solution, and gain some
demographic characteristics. The survey appears as such below.

3. o Caveats
The use of SurveyMonkey obviously biases toward people with the means
and knowledge to use the internet in such as way to receive rewards, skewing
toward likely a more educated and young demographic despite lower income
levels and having received SNAPbenefits. The methodology we used is also at a
national level, even though our initial product is targeted for South LA. We did
this purposely to get a large view if there are geographic differences in behavior
and purchasing patterns, but also because getting a sample size through this
method at the city and state level is both costly and untenable. This survey is
also in English, which excludes many SNAPbenefit recipients whose primary
language is not English.
In the initial results presented below for this first pass only takes into
account those who said they current are using or have used EBT benefits in the
past. Others in the survey may also be in similar levels of need and provide
insight to the application build even if they have not received benefits.

4. Initial Results
Below are selected initial results and analysis from some of questions that
provide insight as we develop our product.
The first question validates the idea that people shop at multiple stores to
save money. The core concept of our service is to provide different data points and
eventually crunching numbers to provide the best shopping options across multiple
retailers.

5. Interestingly enough, it appears that people go to the cheapest store most of
the time for most of their groceries, though they maybe selective when they do pick
the store based on the prior answer. Several of the qualitative responses indicated
constraints in transit and time, which determines why many of the respondents end
up going to one store. Again this is an important point to take into account in
building product that provides a feature that enables store-by-store comparisons or
provides a way to save money in an individual store.
This is a very interesting reveal that took away at our assumption of text
messages and mobile apps being more important than a website. However, website
access is very important for these respondents in the survey, perhaps because of the
lack of understanding around mobile phone technology or adoption, with text
messages coming in second.

6. Not surprising here, the gender breakdown is mostly women who handle the
shopping, though a significant number of men, about 25%, also do the shopping,
showing us that we shouldn’t neglect them in product feature or marketing efforts.
There is an unexpected age distribution in terms of the younger people
responding that may just be as result of the technology, but nonetheless represents
an important potential user base. The relative lack of 30-44 may reflect the simple
fact that they maybe too busy to take surveys.

7. Including this result just for context of who took the survey. We want to
address that $25,000 - $50,000 may seem a little high, but given that much of the
survey spreads to the Pacific and Northeast, that income level isn’t high especially
for a family of four in an urban city such as New York City or San Francisco for
example.
The “Some college” number of responders likely reflects a survey bias of
college students or taking the survey, although that doesn’t neatly correspond with
age breakdown. This could mean also reflect a class of people with some education
who have the savvy to opt into a service like this, but did not have the financial or
social capital to finish their education.

8. We include this chart provide a spread of who took the survey and fell under
the EBT recipient category. Some obvious places are missing here, but it does
largely reflect population densities across the United States.
Qualitative Responses
The most important qualitative response were answering the question:
If you had more time or wanted more tools, how would you save money on
groceries? How do you think that website, app, or text service could help you do
that?
36% of respondents mentioned wanting easy access to coupons. Other
significant responses included a desire for location based pricing comparisons as
well as nutrition information.
We also collected the top five staples from each respondent. Thankfully they
fall under much of the staples prices we’ve begun collecting, validating that we’re on
the right track in that respect.