Objective 1: To identify opportunities for participating farms to increase sales based on consumer transaction data. Our primary objective is to collect and analyze detailed customer transaction data from a group of small scale vegetable farms in the Ithaca, NY area. We will provide each farm with a detailed report on their transactions complete with interpretation and suggested methods to increase customer spending.
Objective 2: To pilot and develop a methodology for collecting data on customer transactions. In order to complete our primary objective, we will need to develop and implement the methodology for programming the POS system and collecting data consistently across all farms and markets. A plan for this method is described in other sections of this proposal. Once developed, our method will be described in a guide so it is repeatable.
Objective 3: To develop generalized marketing advice based on conclusions drawn from individual farm and aggregated data. We will aggregate data and create benchmarks for farmers market sales. We plan to disseminate our findings through articles, sharing recommended marketing changes which seem broadly applicable.
Our Marketing Channel Assessment Tool (MCAT) research was among the first to measure performance metrics of farmers markets for small-scale vegetable growers. In that project, we measured each channel’s performance for five factors: sales volume, labor required, profit, risk (of lost sales), and lifestyle fit. We ranked the relative performance of each marketing channel utilized by a farm and created benchmarks for dollars of gross sales/hour of marketing labor for channels. The data-driven approach of MCAT gave farmers information to make informed marketing decisions, specifically, which channels to expand, improve, or drop to increase overall efficiency of marketing labor (dollars gross sales/hour of marketing labor).
Hardesty and Leff (2010) found that the labor to revenue ratio was highest in the farmers market channel. Similarly, our team, LeRoux et al. (2010) and Schmit and LeRoux (2014) found that farmers markets had the lowest overall financial performance rankings relative to other channels. This work led us to look for ways to improve the farmers market channel because it remains popular with small farms. Increased sales at markets can have impacts on farmer qualify of life. Increased sales increase channel profit since nearly all costs for market participation remain fixed. Also, as one farmer expressed in his support letter, increased sales in two markets may allow him to drop a third.
MCAT determined that farmers markets perform poorly for farms, but they are easy to enter and remain popular among farmers. So, what can vegetable farms do to improve the profitability and gross sales/hour of farmers markets? We determined that studying customer spending, measured in dollars/transaction, is the best opportunity for individual farms to make improvements.
This proposed project is a logical next extension of MCAT. While the determinants of average customer transaction size have been evaluated previously using more aggregated data (Schmit and Gomez, 2011; Varner and Otto 2008) or farmer surveys collected during the off-season (FMFNY), we know of no prior farmers market study utilizing transaction-specific data akin to the scanner data utilized in retail stores. If FMs are to provide improved farm returns, further research is needed to investigate the determinants of their success, and more micro-level data on customer transactions is needed. By utilizing transaction-level data from vendors one can ascertain more precisely the nature of individual purchases by consumers rather than relying on aggregate daily/weekly sales and customer number estimates.
Our reports for participating farms will recommend actions which can be categorized similar to work done in the retail industry and used to increase customer spending. Recommendations in the report follow the work of Lobel of SwiftIQ with suggested improvements in five key areas:
1. Localization: Product price and placements,
2. Inventory optimization: market-specific product assortments,
3. Fact-based merchandising: Patterns in consumer behavior inform signage and promotions,
4. Specific, relevant promotions at the market or by email, and
5. Labor management: number of employees needed at a market at any given hour.
In 2018 the project launched and can be described in three main sections: creating the methods, data collection in the field, and early data analysis.
Creating the Methods
As soon as we received funding, work began writing detailed instructions on how to collect data in an accurate and consistent manner across all farms. We first purchased four iPad Mini’s and installed the Square application on each. We then set up Square for a test farm, including all of their items and prices, and joined them at market to collect data. We worked one or more markets with each farm to assure that set up was correct and that we were capturing the data as intended.
As the season progressed, additional directions were written describing Square set up, collection in the field, downloading data to Excel, and handling Excel spreadsheets to create reports for the farmer. These documents were created to make sure that data was collected and handled in a consistent manner, but also so that others wishing to repeat the work have clear instructions.
Data Collection in the Field
Summer interns were dispatched to farmers markets nearly every day of the week, “shadowing” the busiest farms and entering sales into the POS. In addition, project staff checked in with each participating farm and handled downloaded data in the office. With nine actively participating farms, each doing multiple markets per week, the data quickly piled up. We focused on data collection at markets from June through the end of September, to capture the “peak season.”
Early Data Analysis
Beginning this summer and for the remaining part of this year, we have been processing data and creating Daily Market Summary Reports for each farm, each market day. The data downloaded from each farm’s Square account is first “processed” which involves adding additional data points, such as number of other vendor, number of items, and weather and by making calculations. The processed data set is then used to make a summary report, which is given to the farm and shows customer spending, item sales, and other totals and averages from the day’s sales. As of the end of 2018, we have over 75% of all usable market data processed with summary report created.
Using the POS software, we collected detailed sales data at farmers markets including item name, price, quantity, time, and customer order total. Short of daily market summaries, data analysis for this project is on-going and will mainly occur in the last quarter of the project. Thus, we do not have conclusions or discoveries from the data yet. However, we have several lessons that we learned from our process of collecting and handling data.
Testing our methods in the field early in the season led us to quickly update and retest as we encountered new situations. Our original method include a paper “daily market log” to record observed market traits such as weather. The paper log proved difficult to consistently collect from farms. We tested an online survey tool application with one farm, with the app installed on the iPad. The app worked well enough, but we are still searching for a better method including a custom-built application.
Education & Outreach Activities and Participation Summary
At this stage of the project, we are working with a small number of farms, most of whom had written a letter of support for the grant application. We held a group meeting early in the Spring to orient the farms to how we would proceed. Besides that and a few emails to the group, most project outreach has been in-person at markets. We anticipate writing about the project to share with a broader audience near the project’s end.
Some farms took some real convincing to adopt POS technology at market. Of the nine participating farms, one has stated that they have no intention to use POS after the project. A few farmers really grasped the value of using POS to capture sales data. Thus far in the project, we have distributed only a fraction of the Daily Market Summary Reports to farms, thus most haven’t seen the results. We will finalize the first level of data handling and distribute reports to farms in the coming quarter.
It is still too soon to document the outcomes of this project. What we are waiting for is to distribute detailed analysis and recommendations to participating farms. However, just in the course of the data collection activity, we feel we have seen some success in changing perceptions and practices among cooperating farmers. Notably, none of these farms were tracking customer sales before the project. Mainly farms were tracking gross sales total for each market day. A few were also recording the number of cases or items for each crop that was brought to market and left over afterward. Additionally, one farm used an old-fashioned, handheld counter device to count customers. After a few weeks experience with the POS software, these farms abandoned their notebooks and counters when they recognized that POS could capture the data with greater accuracy and added detail. We count this as a success, because it means that some participants began to recognize the value of our methods, of recordkeeping, and of POS.
As we work through the project we have learned how reality differs from our plans. The main two changes we adopted are detailed here.
We originally tried to get all farms to begin data collection during the same week and planned to assign intern “shadows” to farms with the highest volume of sales only. When repeating this, I recommend bringing farms on-board a few at a time, staged across a few weeks, and giving them an intern only during the initial 4-5 markets of data collection. Once all farms are on-board, intern assistants would be assigned based on farm need and sales volume.
To keep our student interns busy, we were asking farms to download and submit their POS data each week. In that way, students could process the data and create a report while the week was still fresh in the farmers’ minds (to address questions that might come up). This is a good system, however is time-intensive. When conducting this project again, we will ask interns to process data at the end of each month, and then split the processed data up into files for each individual market. This will cut down on the amount of time needed to handle the data.