Periodically every business needs to take a fresh look at pricing their products or services. There are several basic questions that need to be asked, and then a great deal of work to analyze the current state of pricing data and then to forecast the impact of any change in pricing. First, you need to ask yourself, “Why would I want to change my pricing model?” There are many reasons a manager might consider a change: margins that are too slim, a lack of profitability, or a even speculation that customers will accept higher prices with no impact on conversion rates or volume. Second, a business owner or manager may determine that they are not receiving a fair share of the value chain. Many businesses are reliant on their partners in a chain to create and deliver their offerings to the customer, but sometimes the economics of the chain may be weighted unfairly towards another partner in the chain, and the business may wish to adjust that balance through new pricing. Finally, and most importantly, is your current pricing leaving money on the table? For many businesses, this is a complex question and, hopefully, this post may help provide some tools help answer that question.
Pricing analysis and modeling is a complex exercise and will be different for every company that attempts it. However, there are common elements and steps that every business would have to accomplish in order to answer it. I’ll try to keep the details broad enough that any manager can apply these concepts to their own model, but provide enough specifics to help you learn to perform the analysis and develop your own recommendations for your new pricing model. Here are five tips that should help:
1. Gather ye data. How many times do I write a similar sentence with one of my posts on data analysis and modeling? The point is that without high-quality historical data on your business you can not possibly perform the analysis required for this process. So you’ll want to collect into one spreadsheet as much of your historical sales data as possible and over as long a period of time as you can. You’ll want customer records, as well as daily, weekly, monthly, and annual sales data. You’ll also want to available any data on individual products or services to analyze unit contribution and margins for each of your offerings.
2. Formulate your questions. Once you have your data organized, you’ll need to figure out exactly what questions you are trying to answer. Do you want to know average sales per customer? Sales during a specific calendar period? Gross margins on any given product? Unit margins on each service you offer? Every business will develop their own unique set of questions, but this is the heart of any analysis. A simple approach is to let the data itself guide you; when you import the raw data into a spreadsheet, every column will be named with a unique label: month, time, amount, customer, transaction number, etc., etc., etc. Perusing these columns will help you to define the questions you want answered: “What is an average customer spending with my business annually?” Or, “What are our busiest times of day?” Or even, “How has average per customer sales trended over the past 3 years?”
3. Interrogate those numbers. So now you have compiled the raw data and you have devised the questions you want answered. Here’s where the rubber meets the road and where your work really begins. By setting up pivot tables and using formulas, an experienced spreadsheet cowboy can drill down through vast mountains of numbers. As you define the questions, you will have to build the data tables and formulas to extract the numbers and answer each question. For instance a simple pivot table can be constructed to answer the question about average sales per customer over time – simply define the rows of the table to display your customers and the columns to display average sales for that customer per day or week or month. If you’re looking for busy times of day, you would set the rows of your pivot table to display the hours of the day and the columns to show average sales for that hour, or perhaps the number of units sold on average during that hour.
4. Play the “What-ifs.” Once you have asked the questions and have a meaningful understanding of your current offerings, you can begin looking at your alternatives and their impact. For instance, if you are considering raising the per-unit price on one of your products, you’ll want to look at a number of variables. Take a look at how many units of that product an average customer purchased last year and at what price; if you are raising the price, ask yourself if the new price may lead to a lower per-customer volume and how that would impact your profits. Pretty simple to get this answer: create a “sales volume” cell in your spreadsheet that is changeable and see what happens when sales volume drops to 90%, 80%, 70%; multiply the new sales volume by the per-unit margins and you can quickly determine the impact on bottom-line profitability. Create a list of the ‘changeable’ assumptions that will impact your overall numbers and iterate: plug in number after number and watch what happens!
5. Forecast, predict, expect, estimate, calculate, reckon. Whatever you want to call it, this is your projection of what would happen if you were to implement one of your what-ifs. I like to set this up in two columns: Historical and Projected. Those columns can include everything from net earnings to unit metrics to per-customer sales. As you adjust the values in the changeable cells, you will se a side-by-side comparison and learn to quickly project how those changes would affect your business and your customers.
Here are a few posts on related topics:
Photo: 3-D Iterated Function System fractal made by constructors in XenoDream (“Abacus”) by Virginia McCabe
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