Farming is one of the oldest professions in the world, but one that has markedly evolved in the past century. Gone are the days where 41% of the U.S. workforce was employed in agriculture; as of 2000, a mere 1.9% remain, of which 93% has to rely on off-farm income to survive (Dimitri et al., 2005).
And although graphs such as the one depicted to the left paint a pretty picture concerning the average farm household income, these numbers do not reflect that more than half of the households operating small farms (gross cash farm income < $350 000) typically incur losses from farming (Prager et al., 2017). This issue is made even more relevant by the fact that 90% of the U.S. farms actually consist of those small family farms. Evidently, only the largest players have the bargaining power and economies of scale to make serious profits in this cut-throat, behemoth-dominated industry.
This exact issue is the one that Farmers Business Network (FBN) is tackling. Created in 2014 by farmers, for farmers, FBN aims to develop an independent, unbiased and objective farmer-driven information source (Farmers Business Network, 2014).
How does it work?
FBN realized that many farmers were investing in expensive data-gathering tools such as drones, satellite imagery and on-the-ground sensors to analyze their crops, but were not reaping rewards from the resulting data. This was due in part to a lack of data-processing knowledge, but mostly because the sample size for each individual farmer was simply too small to draw robust conclusions from. To solve this problem, FBN built a platform on which farmers can upload their farm data and add it to an anonymized aggregate data-set. FBN analyses this complete data-set for the farmers and sends them comprehensive agronomic analytics, reports and mappings of their own farm.
Because FBN contractually agrees that ownership of the data remains with the owner and that the data will only be used for anonymous data analysis, combined with the fact that contracts such as these are respected and enforced in the U.S., the institutional environment lends support to their business model. Moreover, with their “by farmers, for farmers” company philosophy, they build on existing social norms to further legitimize their case.
To better understand how FBN co-creates value, the business-oriented analytical framework with respect to value co-creation as devised by Saarijarvi et al. (2013) provides guidance.
Firstly, value is created for the customer in the form of useful information about seeds and crops that maximizes their profits. Furthermore, FBN increases transparency concerning market prices, leading to a form of collective bargaining power for the farmers. The firm derives direct value from the subscription fees, whilst continuously discovering new ways to generate value from the network for both customer and firm. For example, FBN envisages linking food companies with very specific product-origin requirements to farms that fit those requirements, commanding a premium price for the farmers and receiving a commission in the process. Thus, FBN clearly adheres to the joint profit requirement (Carson et al., 1999).
Secondly, customer resources are aggregated in the value-creation process, meaning the “co” in co-creation is mainly due to C2C resources. The data from the existing customer base is harnessed to gain more accurate insights in best practices and pricing.
Thirdly, the mechanism through which value is created is the refinement and analysis of the aggregate customer resources by FBN, before returning farm-specific data to each farmer. As a result of this mechanism, previously useless data now yields valuable information for each customer.
This mechanism places FNB in the joint value creation sphere as depicted above (Gronroos & Voima, 2013). The farmers independently create the data, but it only gains value when the provider (FBN) joins the process as value facilitator. That is the point where they co-create through direct interaction, in which farmers send their data to FBN, which aggregates and analyses it before communicating the value-having results back to the farmers.
Future growth and value
Although one method of valuing FBN could be the use of income or cash flow multiples, a more fitting way to value the network would be to apply Metcalfe’s law (Metcalfe, 1995). This is especially true for FBN because a requirement for participating in the network is the actual contribution of valuable data. However, as the quadratic growth of Metcalfe’s law has often overestimated the value of these networks in the past, applying an n log (n) scaling rule could be a more plausible option for a community such as FBN (Odlyzko & Tilly, 2005). Moreover, the increase in valuable information derived from an increase in sample data will experience diminishing returns at some point in time, reducing the marginal value added by new participants. On the other hand, every new participant does consistently increase the bargaining power of the entire network.
Valuation methods aside, investors appear to be convinced, as FBN has recently raised another $110 million in a Series D funding, adding up to a total funding of approximately $200 million (Techcrunch, 2017). As a company with a unique value proposition, a business model that meets the efficiency criteria and a potential market in just the US consisting of more than 2 million fragmented farms (USDA, 2012), the future sure looks bright for Farmers Business Network!
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