How we used financial modelling to build the ultimate pricing strategy for a startup

How you price your product is one of the most impactful decisions you’ll make as a business owner. It’s a balancing act between how much your customers are willing to pay and how much income your business needs to survive – let alone keep growing.

Would you base your business’ pricing decisions on a vibe? Heaps of business owners tell us they use industry experience or competitor research to set prices – but every business is different, and this individuality needs to be reflected in your pricing strategy.

If you’ve never needed a financial model, you might assume that commissioning one is a super complex process. In reality, it can be streamlined into a few steps.

Keep reading to learn how we built a flexible financial model tailored to the needs of our client, a software company who wanted to understand how to price a new product – and we’ve even added some tips for anyone thinking about commissioning a model themselves.

Financial modelling is one of the most accurate ways to make predictions about your business’ future. Check out our article on financial modelling basics for a quick explainer on what it’s all about, or check out how financial modelling helped a startup secure grant funding if you want to explore more ways that financial modelling can help grow your business.

The client:

• A small software as a service (SaaS) company
• Seed stage
• Team of under 10 people
• Both B2C and B2B customers
• Looking to service customers in multiple countries

The task

The software company was looking for a five-year financial forecast so they could start seeking further venture capital funding, and this raised questions about the pricing structure of their product. The business owners were conscious that the product needed to be an attractive price to potential customers while still supporting business growth, and they were having trouble striking this balance.  

Our job was to create a flexible model that explored the effect of different pricing scenarios in the short to medium-term, while also forecasting the effects of that pricing over the next five years.

Planning the model

After agreeing to the project, we met with the business owners to work out exactly what they wanted to learn from the model. We started by asking general questions about the level of detail they were looking for, as this would impact the complexity of the model and how long it would take to build. We knew they wanted to visualise the outcomes of pricing scenarios over a five-year period, but they also wanted this data broken down to analyse predictions in the short to medium-term, too. This information helped us decide how we would display the data generated by the model.

We also needed to understand exactly how they were planning to use the model; would it be data that they used for a single business decision, or were they hoping to use it continuously over some time, and if so, how long? The client told us they wanted a model that could be updated as the business grew, meaning we needed to make it easy to change input values.

We also used this meeting to gather data that we would use as assumptions in the model. The information we asked for included:

Revenue structure

Revenue structures are highly product-specific; for example, a freemium pricing model works well for Spotify, but wouldn’t work for more niche products like the Adobe Suite, which uses a pay-per-seat subscription model. That’s why we recommend that business owners take the time to understand their product’s positioning and consider a revenue structure before they commission a financial model.

When we met with the business owners, they’d already done extensive market research and decided to implement a three-tiered subscription model. This was a great start; helping a business to decide on a revenue structure is a big piece of work on its own, and can drastically draw out the timeline of building a model.

Number of users

Not only did we need to know how many people were using the product, but we also needed to know the distribution of users across each subscription tier. These numbers were important because they indicated the accuracy of the assumptions we would be making – a larger number of users meant more reliable data and a more accurate model.

Growth rate

Our assumptions on future growth rates were informed by data on how fast the product’s user base had grown. When we compared the growth rate of each subscription tier to the overall number of users, we were also able to see how frequently people upgraded or downgraded their subscriptions.

Acquisition channels

This refers to the places where users were finding and purchasing the product. For example, was the business mainly reaching people through its social media channels, or were they relying on paid ads to drive conversions? It’s normal to have a few irons in the fire when marketing a product, and data on the performance of different acquisition channels helped to further refine our assumptions on user growth rates.

Churn rates

Churn is how frequently users cancel their subscriptions. The company was able to give us churn data for each of their current subscription tiers, which also gave us insight into how much customers were willing to pay for particular product features.

Direct costs

These are the costs directly associated with generating revenue. In this case, the company paid fees for transactions made using their product, and incurred costs on the tools and labour that made those transactions possible. Working out exact direct costs can take a significant amount of time and research; instead, they can be represented as a percentage of revenue when being built into a model.

Fixed costs

These are the costs incurred from just running the client's business. They include all the boring stuff like rent and utilities, which can usually just be represented as a percentage of revenue in the model without needing to get super granular. However, if you do know your exact fixed costs, this will only add to the model's accuracy, so it doesn't hurt.

This list of assumptions is pretty typical of financial modelling for software companies, but your business might operate on a completely different set of metrics. As a rule of thumb, the more information you give to your accountant, the more they’ll have to work with when creating your model. Having your books in order will always make the process of providing this kind of information easier, so it’s a good idea you reach out to an accountant (👋) before you need a model built.

Building the model

After our initial conversation with the business owners, we advised it would take us about two weeks to finish the model. This time frame was based on the model’s complexity and when the client could get us all the information we needed.

It’s worth noting here that once we’ve started building a model, the process of adding more assumptions can get pretty complex. We always try to build models with as much wriggle room for more information as possible, but when you’re working with complex formulas drastic changes can sometimes mean going back to the drawing board.

We’ve worked on projects before where clients have requested last-minute additions, and it’s meant we’ve needed to completely overhaul the model. This can push out costs and timelines pretty drastically, so we always stress this during the planning phase.

The result

When the model was finished, we presented it to the business owners in Google Sheets and spent some time showing them how to use it. We created a page where assumption values could be edited quickly and easily and a dashboard-style page that displayed results. We also gave the business owners some recommendations on their subscription tier pricing based on what we’d found when testing the model.

The client was thrilled with the model’s ease of use, and it’s given them the confidence to test various pricing strategies on their own. Now they’re using a copy of the original model for their operations, allowing them to experiment with their product’s subscription tier pricing in real-time.

By telling us during the planning phase that the model needed to grow with the business, we were able to include ways that the client could easily add assumptions in the future. This forward thinking saved the business a huge amount of time and money, as we built in the ability to make changes like adding a subscription tier without needing to contact us.

The model was also detailed enough to provide a comprehensive five-year forecast of the business’ performance. They were able to approach venture capitalists with this data and have made significant progress toward securing funding. They told us that while the process of seeking funding has been stressful, the ability to show the value of their business using our model has helped grow their confidence when meeting with investors.

Need expert help with the pricing strategy for your product?

The Project Alfred accountants are experts in financial modelling, cash flow projections and scenario planning, so no matter how complicated the model you’re looking for is, we’ve got you covered.

Want to find out more about how we can help your business apply for the funding you need to achieve your goals?

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