We created a pool of genetic material to engineer our beef with superior performance characteristics to prepare for the inevitable global warming.
Historically, the beef cattle industry has had two main drivers that determine success: Taste and the costs to feed the cattle. In particular, corn and other grains that are becoming much more expensive are not well suited for cattle that are in high-heat areas and high-heat stress areas, which will become more common as global warming progresses.
So, our cattle have been genetically engineered, mixing genetics from cattle in India and Africa – both of which have a natural selection that allows the cattle there to compensate for heat stresses – with some European cattle breeds. Doing so allows us to extract the most profitable genetics to bring higher feed conversion rates and create a higher quality and better-tasting meat than we currently have. Others have tried to accomplish this by crossbreeding cattle with Buffalo.
One of our competitors, who delivers frozen meat to your door, still uses corn to feed their cattle. We can say that our cattle are grass-fed, as well as a healthier cut with less fat, sharing the same palatable characteristics as corn-fed beef.
Our clients are moving into the world of retail, and we are looking into developing into retail as well. To do this, we need to make sure we get our research right. We are looking to find ways to determine chatter around our product so we can respond in a methodical manner. For this reason, we turned to web data collection. We want to make sure we are ready to enter the retail industry with the best offering and build a strong brand. We are currently using the Bright Data Web Scraper IDE to determine if there is an event or an article to guide us.
But web data’s most important use would be to identify potential customers, and how to reach them. Once we have an idea of our target demographic, and what responses they are giving to our products, we can create a model to find new customers who would also fall within the same consideration set.
Through Bright Data, we have the ability to know rather than guess about our customers. We have the data that we can ingest to actually know the customer rather than making assumptions.
Aside from consumer research and brand production, we are also using Bright Data for pricing comparisons as well as to predict supply chain disruptions.
We have clients from all over the world. So, let’s say if there is a wildfire in Australia, by accessing public web data, we can side-step or better prepare for these supply chain disruptions in order to still deliver our perishable goods to consumers.
Our global Rolodex of clients is also buying and selling internationally, which means that our products are being sold for different prices all around the world – competing against thousands of other farmers who are selling similar commoditized products as well as against the supply chain itself.
Therefore, to understand what the best price is to set our products at, we use Bright Data to collect public web data in real-time. Doing so ensures that all our prices are up to date and most attractive for consumers.
We plan to continue using Bright Data to paint the full picture of our market as well as the beef cattle industry as a whole – from product to consumer – and we intend to further use strategies we formulate leveraging public web data and the insights gathered.
