In the first 100 days of starting IN10T, I have learned more than at any other point in my 20+ year career. Throughout daily conversations with customers and industry leaders, there are common themes related to building a business. I admire other thought leaders who share their ups and downs along the way, so I plan to follow that example.
We have no idea what our company will look like in the future, so sharing our lessons regarding what’s working and where we need to improve, allows us time to receive feedback and reflect on where we are today. Here are some lessons from the first 100 days.
Starting a business is hard
I have heard this a thousand times but living it is a whole new phenomenon; especially when bootstrapping the businesses. There is a big difference between working at a startup and running a business with direct P/L responsibility. Over the last 20 years, I have worked in and around different startups. The experience was invaluable, and it helped to prepare for this step, but receiving a paycheck and having to create value is not the same.
Our company, IN10T, was recently working on a project around the timing of customer communication. One part of the project was to look at when businesses talk with their customer’s. Below is a sample data set we ran for a specific group in Agriculture. (Sample included 100 Twitter Users with expressed interest in #AgTech)
Suggested timing for highest Customer Engagement
What is IN10T
In10t is a digital agriculture company founded in 2016 focused on solving farmer adoption challenges in agriculture. We create custom digital and data science solutions for our customers both in agriculture and those investing in agriculture.
Why start IN10T?
The market including farmers, investors, agribusinesses, and agronomists all kept sharing the same themes and pain points:
- We don’t need more data products, we need answers to what is working
- We need less systems and more integration between current applications
- Help us understand product and system performance benefits
- We have data, lots of data, what are the business applications for it?
- We have our own hypothesis, help us prove/disprove it
- How do we begin our data analytics strategy? Or more simply put, how do we make data work for the entire ag supply chain.
- Our situation is unique.