Chapter 2 - The Ebb & Flow of Cash
Previously: We introduced Maria, a florist with 40 years of experience, and her granddaughter, Elena, who has followed in her grandmother’s footsteps by opening her own florist shop in the next town. While they share a passion for flowers, their approaches to running their businesses and their views on technology are streets apart. That’s where the story lies. We continue.
As it has been for the past 40 years, Maria’s biggest expense is the flower stock. Every Friday, she places her orders with a handful of trusted suppliers with whom she’s built a relationship over the years.
While experience and seasonal trends inform her purchase decisions, she has recently taken an important step to make better use of the data she has on hand. (On the recommendation of Elena, of course.) She has diligently entered all her historical sales and order numbers into an Excel spreadsheet. Entering 40 years' worth of numbers was a huge undertaking, but she now uses this spreadsheet as a valuable tool to verify her orders before placing them.
Her spreadsheet is great for informing her decisions, but it’s not failsafe. At times, she’s been caught with too much stock, tying up her cash. And, of course, Valentine's Day is always an issue with too little stock and very high prices. But she does her best to read the trends and make informed choices.
Given these weekly orders, good cash flow management is essential for her. She uses her spreadsheet and her bank balances to give her an idea of her cash flow requirements. It’s not an exact science, but her experience stands her in good stead.
Being a businesswoman, Maria understands how quickly things can turn south. That’s why she’s diligent about putting aside some funds for a rainy day every month. In fact, she has set up a separate bank account to keep those funds safely tucked away for when she needs them.
Granddaughter Elena is sitting in her own shop contemplating the same issues. Like her grandmother, flower stock is Elena's biggest expense. She understands that there are three variables in this equation: availability, price, and demand. She also knows that the right data will help her make better decisions.
That’s why she’s started digging into the numbers.
📊 For demand:
- She reviewed her own historical sales data and client feedback.
- She compiled a list of local events and the number of weddings booked in her area.
- She gathered local economic indicators, property sales, and retail sales data from the Chamber of Commerce.
📈 For supply:
- She found industry reports showing global supply changes.
- She reviewed consumer behavior trends.
- She looked at long-term weather forecasts in her suppliers’ regions.
📉 For pricing:
- She reviewed the online sites of her local competitors to see their offerings and prices.
- She examined pricing trends in the industry.
- She looked at factors that could influence supply chain costs and forecasts about that.
With her research done, she wanted to extract meaningful insights. She fed all her data into ChatGPT to find the patterns that would inform her about
a) when to buy what flowers,
b) when to expect a surge in demand, and c
) how to adjust her offers.
It even showed her when she should offer luxury items and when she should focus on more value offerings.
She converted this information into a personal GPT to give her a six-month buying plan. She could update it with the latest information and run it monthly to double-check her buying plan and ensure she doesn't overstock by too wide a margin.
She also used AI to find a few new smaller suppliers who were willing to make good deals to get their stock moving.
She then turned to ChatGPT again to help her create a cash flow forecast for the next six months. She and her AI assistant chatted back and forth until Elena got what she needed.
She now had the numbers at her fingertips and knew how much she had to keep on hand to handle demand surges.
Following her grandma's advice, she also put money away for a rainy day. However, she didn’t put it into a savings account. She moved her savings straight into a brokerage account where she started building up a portfolio of interest- and dividend-earning assets. That included investing a portion of it into Bitcoin. She wasn’t just providing a cushion for her business. She was actively building her wealth.
It wasn’t too long before that proverbial rainy day arrived though, for both Elena and Maria.
Read all about it in Chapter 3, available soon.
[Story Image Credits: A Booysen & ArtFlow]
*This might be a fictional tale, but the described outcomes are certainly achievable. However, reaching these results requires hours of experimentation and sifting through AI tools. That’s why our clients choose Personal Training in AI to fast-track their learning and achieve measurable business results faster.
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