Pricing Optimization for the Bellevue Urban Garden

Introduction

The Bellevue Urban Garden (TBUG) is a non-profit, volunteer-run community farm spanning about 2.7 to 3 acres in Bellevue, Washington's Lake Hills Greenbelt, dedicated to growing fresh, healthy produce. The organization believes nutritious food is the root of strong kids, families, communities, wellness, and meaningful connections, emphasizing that what we eat is inseparable from the land and sustainable conditions in which it grows. TBUG promotes wellness, joy, and sustainable living through urban farming, nurturing both plants and people and engages locals by offering affordable, fresh vegetables while fostering community involvement through activities like planting, harvesting, weeding, and building.

Healthy and sustainable growing practices are vital, as they preserve soil health, minimize environmental harm, and provide long-term access to chemical-free, nutrient-rich food. TBUG advances these practices by operating as a vibrant hub for local produce and education, empowering community members to embrace eco-friendly methods.

Running a community vegetable patch involves more than just planting and harvesting; you also need to decide how much to charge for your produce. Even as a non‑profit, finding the right price is crucial. If the price is too high, customers may be turned away; if it’s too low, the garden might not cover its costs. This paper attempts to introduce pricing strategies from basic pricing strategies to more complex optimization strategies.

Basics of Pricing

Cover the Costs: For any product (say, a bundle of Swiss chard), first we need to figure out how much it costs to grow. This includes seeds, water, fertilizer, and a share of tools or labor. A basic rule is that your price should at least cover the cost per unit. For example, if it costs $1 to grow a bunch of Swiss chard, charging $1 just breaks even. Charging $1.25 would cover costs and provide a small surplus to reinvest in the garden. Non-profits don’t aim for big profits, but they still need a buffer for sustainability.

Consider the Mission: A commercial farm might price to maximize profit. Our non-profit vegetable patch might have a different goal, like covering costs while keeping vegetables affordable. Maybe we want as many people as possible to enjoy fresh produce, while ensuring the garden’s expenses are paid. In pricing terms, every business can have a goal – maximizing profit, growing volume, etc. The “optimal” price depends on what we are trying to achieve at TBUG which is balancing revenue with community service.

Before we delve into pricing strategies, it is important to understand the basic interaction of pricing and demand. Demand means how many people want to buy at a given price. Generally, when prices go up, demand goes down, and when prices go down, demand goes up. If we charge $5 for a pumpkin, maybe only a few people will buy it. At $2, many more will be bought. This relationship is often called the demand curve. Finding the right price is about balancing these two forces: we want a price that enough people are willing to pay but also brings in enough money.

For instance, imagine:

• At $5 per pound of squash, you sell 10 pounds (income = $50).

• At $2 per pound of squash, you sell 40 pounds (income = $80).

Even though $2 is cheaper, it yields more total revenue here because so many more people buy. But if you go too low:

• At $0.50 per pound, you might sell 100 pounds (income = $50 again, and possibly not even cover costs).

This simple example shows there is possibly an optimal point – e.g., $2 per pound – that maximizes revenue while keeping customers happy. Pricing optimization, at its heart, is about finding such sweet spots.

But let’s start by reviewing some simple pricing strategies. Companies typically use a few basic strategies to set prices. Here are some that even a non-profit community garden business can consider:

· Cost-Plus Pricing: This is the simplest method – calculate your cost per item and add a markup (a bit extra). For example, if a bag of lettuce costs $1 to grow, a 50% markup would be priced at $1.50. This ensures costs are covered and gives a cushion. It’s straightforward but doesn’t directly consider what customers are willing to pay. Many companies rely on simple cost-plus pricing to determine price, but this can lead to missed opportunities if customer demand or competitor prices aren’t considered.

· Value-Based Pricing: This looks at what customers are prepared to pay. If our organic, fresh carrots are highly valued by the community (say people would gladly pay $2 for that $1 cost bunch), we might price closer to that value. We must gauge customer perception – maybe through feedback or seeing prices at the local farmers’ market. Value-based pricing sets the number based on the product’s perceived worth to the buyer

For a non-profit, we might even use a “suggested donation” approach (e.g. “Suggested price: $2 per bunch, pay what you can”). This way, people who can pay more might subsidize those who can’t, while still signaling a fair value.

• Competitive Pricing: Check what others charge for similar produce. If the supermarket sells pumpkins at $3 each, we might price ours around $3 or a bit lower to attract customers. This strategy uses the market rate as a benchmark. It’s easy for customers to compare. However, remember our veggies might be organic or locally grown, which is added value – some customers might pay a little extra for that. Competitive pricing doesn’t account for our unique situation (it ignores our actual costs or community mission), so use it as a reference, not a strict rule.

• Penetration Pricing: This means setting a very low price initially to attract customers. For example, a new community garden might sell tomatoes very cheap in the first season to get people interested. Low prices can quickly build a customer base. But this isn’t always sustainable – if we raise prices later, people might feel upset, and if we keep prices too low, we won’t cover costs. It’s a risky strategy because it can start a price war (others drop their prices too) and make people expect cheap produce forever.

• Discounts and Promotions: Even as a non-profit, we can use sales tactics. For instance, at peak harvest time when we have more zucchini than we can handle, we might do a “buy one, get one free” or an end-of-day discount to clear out inventory. This is like a clearance sale – better to sell at a lower price than to have veggies go to waste. Big stores do this at end of season for products that didn’t sell.

We can do it whenever we have a surplus. It creates a win-win: customers love a deal, and we recoup some costs instead of losing produce to spoilage. Each of these strategies starts from a basic idea—cover the costs, understand the customer, or respond to competition. They are not mutually exclusive. We might use cost-plus as a baseline but still adjust if, say, our price is way higher than the grocery store (competitive factor) or if customers say it’s a bargain (value factor).

But can we optimize pricing such that we maximize revenue while meeting our community goals? This is about using information to fine-tune your pricing. In simple terms, price optimization is the practice of finding that “just right” price using data and analysis. Rather than just guessing or sticking with one strategy forever, we can continuously refine the price based on real feedback and data. According to pricing experts, price optimization involves looking at factors like customer behavior, operating costs, and market conditions to find the best price point. It’s a more scientific approach to pricing.

But why should we consider optimization? Because it can greatly improve outcomes. By finding the sweet spot, we can maximize our revenue or whatever goal we might set for TBUG, for example, revenue to reinvest in the garden, or number of families fed. For us, that could mean more funds for community programs or expansion of the garden. Another benefit is that data-driven pricing takes some guesswork out of the process. Many businesses historically spent very little time on pricing and essentially guessed at prices, but modern techniques let even a small operation use feedback and simple data (like weekly sales numbers) to make better decisions. In short, we make decisions based on evidence—selling veggies become part science, part art.

Key ideas in price optimization include:

• Price Elasticity: This refers to how much sales change when prices change. High price elasticity means customers buy less if prices rise, while inelastic products see little change in sales. Measuring elasticity helps set optimal prices without losing too many buyers. For example, a 10% price increase that barely affects apple sales shows low sensitivity; if sales drop sharply, demand is highly sensitive.

• Using Data to Adjust: Price optimization involves regularly reviewing sales trends, costs, and external factors. If tomatoes sell out quickly at $2, try raising the price to test demand. If cucumbers don’t sell at $1, lower the price or bundle them. Track sales and adjust as needed—use evidence rather than guesswork.

• Customer Segments and Fairness: Pricing can vary for different groups. Non-profits might offer discounts to those in need or bulk rates for restaurants. Flexible pricing models like “pay what you can” help serve different community needs. Businesses analyze buyer patterns to set prices for various segments, but simple flexibility works well for smaller operations.

Price Elasticity: Price elasticity is defined as the percentage change in sales with a 1% change in the price. The demand function equation has some assumptions and one of the assumptions is that sales are only impacted by price but that is generally never the case in the real world because there are generally multiple factors impacting sales like promotions, holidays, events, etc.

Price Optimization Approach: So, we need to find base sales we need to compute the sales component wherein we can remove the impact of all these additional events. Now we need figure out how we can utilize these elasticity values to determine the optimized prices for each item. Also, we should consider limit on the minimum and maximum price change that should be considered while optimizing prices.

First, we know that Optimized Revenue = Total units sold * (Optimized Price) – equation (1)

We need to optimize the price so that we can maximize revenue. But the total units sold will also change with a change in price. Let’s re-write the above equation and we can call the total units sold at an optimized price as optimized units.

Optimized Revenue = Optimized units * (Optimized Price) – equation (2)

Earlier, we discussed Elasticity as:

Elasticity = %change in the units sold/ %change in the price

Therefore, using the concept of elasticity, Optimized units = Base units + change in units at an optimized price – equation (3)

Expanding equation (3) now, Optimized units = (Base units + (Base units price elasticity (% change in the optimized price vs regular price) – equation (4)

Or, switching to Optimized Revenue now,

Optimized Revenue = (Base units + (Base units price elasticity (% change in the optimized price vs regular price) * (Optimized Price) – equation (5)

Optimized Revenue = (Base units + (Base units price elasticity [(Optimized Price -Current Price)/ Current Price] * (Optimized Price) – equation (6)

So, the key parameters in the optimization equation (6) are the following:

· Base units, which is, Average unit sales at the current price.

· Price elasticity = computed value for the item’s price elasticity

· Current price = Latest selling price

So, the optimization algorithm to calculate the price that maximizes revenue has the following important components needed for optimization: -

1. The objective function that needs to be minimized/maximized: - We have already defined the objective function which is maximizing the optimized revenue as defined in equation (6).

2. Bounds: As defined by us we need the optimized price to not change by more than 20%. So, the Lower Bound = Current Price (1–0.2) & Upper Bound = Current Price (1+0.2)

3. Optimization Algorithm: We could use Scipy.optimize library from Python to implement the optimization.

Conclusion:

Pricing optimization might sound technical, but at heart it’s about using information to set a fair, effective price. We started with the basics – covering costs and understanding how price affects buyer interest. Then we explored simple strategies and how paying attention to data (even just simple sales info) can lead to better pricing decisions. For a small non-profit vegetable patch, these principles ensure we charge a price that sustains the garden and serves the community. By steadily moving from basic common sense to data-informed tweaks, we take the guesswork out of pricing and help our project thrive. The right price helps more people enjoy our vegetables while keeping our mission growing.