The Saassy Case Study on Pricing New Products
Deciding how much to charge for a new offering can be daunting, especially if you stare at a blank page. Let’s go through a real-life pricing example from Oracle Opower to take away some of those fears.
Monetizing a new solution
Oracle Opower provides energy efficiency and customer engagement solutions for utilities. The growing number of households with solar rooftops forced utilities to adapt their engagement programs to these new customers. We developed a solution line to answer the need, and went through five key steps to price the new offering.
Step 1: Is there a willingness to pay?
Before diving into analysis of how much to charge, before even putting the feature on the product roadmap, we have to be certain there’s a willingness to pay for solving the pain point.
We were confident that there was, indeed, a willingness to pay from the get go. Not only were utilities aware of the problem, they quantified it. Solar customers had complex problems and questions, which translated into multiple and long (i.e., expensive) phone calls to the call centre. To battle the issue, some utilities even set aside a budget for renewable energy programs. In addition, a couple of utilities had already paid the Opower engineering team to build a small-scale custom solution to help address this problem.
Step 2: How does the price scale?
The pricing decision started with figuring out what to charge for, i.e. how does the value we provide scale for our customers. This can be feature-based, use based, or outcome based. In our case, we had already adopted the use-based model for our platform - we were charging utilities for the number of households they were serving with our software. We therefore decided to stick with the same model to keep it simple for our buyers.
However, we did modify it slightly: we charged per a solar household, rather than a household in general, because the value of the new solution only applied to solar households.
Step 3: How do we package it?
Next, we faced a decision whether we include the new solution into an existing packaging or whether we provide it as an add-on. We decided to offer it as an add-on because:
Not all customer segments valued this product. Some utilities had a relatively strong, and growing, base of solar customers while other utilities had close to none. If added into a core package, the feature would be viewed as a “killer” by many (see an explanation of leaders, fillers and killers).
The value scaled differently for this new feature. Our core packages scaled with the number of households, whereas the value of the offering would scale with the number of solar households.
Step 4: How much do we charge?
As a well-established company, we already had a number of pricing insights and points based on real behavior of our buyers that we could leverage.
At first, we established two pricing bands: the lower and the upper price.
The lower band was determined by our cost:
The cost to build: calculated as the number of engineering hours expected times the cost per engineer per hour.
The maintenance cost: total cost it would take to run the product, such as platform or data storage fees.
The upper band was determined by the value we could provide and available utility budgets:
There were two key benefits the solution would provide:
Increase in customer satisfaction, which has a positive impact on upsell and retention. This is harder to quantify, but a study by JD Power equated a 10% increase in customer satisfaction to 0.4% increase in ROE.
Decrease in cost to serve: We had concrete data from utilities that solar customers were extremely costly to serve - they had a lot of complex enquiries, which resulted in numerous, long calls to the call centre. Our solution could drastically reduce the phone calls and/or their length, saving utilities a significant sum of money.
Several utilities had already set aside specific budgets for renewable energy projects where our solution would fall into. We had to calculate the proportion of the budget we could realistically win by analysing the proportion of different renewable energies and key product and program categories.
With the upper and lower band established, we analysed additional pricing data that would help us land on the right price:
The price of similar offerings in our platform: Our existing pricing and pricing structure anchored our buyers’ expectations. Because the new solution was an add-on, it could not be more expensive than our core packaging. Otherwise it could diminish the value of our core packaging in the buyers’ eyes, and in addition, the buyers could feel like we are over-charging them for the add-on.
The price of custom solutions: For a couple of customers, our engineering team had already built a custom solution to serve solar households. While it wasn’t fully comparable to the new product, it gave us insights into the pricing point.
This analysis has given us banded pricing points for the new solution. Two more inputs were key when picking the final price point:
Competition: the market was far from saturated, in fact, there wasn’t anything comparable in the market yet, which allowed us to charge on the higher end of the spectre.
Insights of customer-facing teams: They had an intimate knowledge of our customers and how they value our offering.
With a single price point figured out, we calculated typical contract value for the new solution and the revenue outlook.
Step 5: How do we optimize it?
Once the solution was launched, we ran a bi-weekly sessions with our sales champions that were tasked with selling this particular solution to our target list. This enabled us to gather feedback from the market, including on the monetization aspect. The price point was spot on and didn’t need optimizing at that time. However, this would have been the moment to optimize had we learnt the price wasn’t landing well.