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.

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.

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.

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.

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.

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.

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.