Howdy! 👋 It’s been a while, but we are finally able to share with you some more insights on pricing as an art. Most companies, knowingly or not, have a pricing system already in place. But how exactly does one evaluate its effectiveness and how to improve it?
What even is pricing effectiveness?
The biggest problem with pricing is that it falls into multiple categories. In traditional business and product development, the money you get from customers will ideally cover your costs and give some profit. Obviously, there are exceptions from this rule: VC or external investments, non-profits, charity work, etc. For most out there, though, the costs+profits = prices simplification will usually be the way to go. Effectiveness could be measured by earning as much as possible, right?
The problem is: pricing in itself cannot influence your costs. These might be critically low or absurdly high. Your prices reflect this, but might not be the cause or place to optimize in such cases.
More complexity comes by looking at pricing as a marketing medium. By slightly adjusting the number you can increase or decrease the number of conversions from your campaigns. Sometimes brands start with very high prices to position themselves as premium brands, or to have room for massive discounts. Some of you probably play games on PC or consoles. Let”s be honest: do you buy AAA titles on launch day all the time, or wait for Black Friday and other events to fill your collection? Available data clearly shows that that most users of Steam treat sale prices as the “standard’, and the non-discounted ones as way too high to consider.
In marketing, pricing effectiveness can be viewed as maximum positive impact on marketing metrics and conversions.
Last, but not least when it comes to pricing: sales. Let’s assume the following scenario:
Product A: $50/mo, sales team closes 10 deals a month
Product B: $25/mo, sales closes 20 deals a month
When looking at a single month only, you seemingly get the very same result. Life is not so easy, though. If these are subscription services, which ones are more likely to yield more profits with churn involved? Will the lower price encourage more users to renew (think Spotify), or will the churn make fewer paying users more profitable, like in mobile gaming? What are the costs of supporting these users, will they not eat profits too much for Product B?
For sales pricing usually is effective when it enables as much sales as possible – though this is very rarely the big picture.
When evaluating your current pricing you need to make absolutely sure that all 3 areas (business, marketing, sales) and their respective metrics are taken into account. Otherwise you might fix a small problem and introduce a myriad of others in the process.
Step 1: Define top priority
Similarly to an API, there are 3 main types of crucial objectives in pricing:
GET – usually getting new customers
BOOST – increasing ARPU and profits
KEEP – retention, reduced churn
Choose 1 of these as your main goal. Keep the others in mind, but you need a clear objective to start. Then start calling your pricing evaluation and changes as an experiment. You want to check if your changes helped your main objective, then how much it helped and at the very end: what was the impact on other areas?
If you don’t know where to begin or need a tip: I highly recommend starting with BOOST. Monetization of current and past customers is easier to achieve and usually the best place to start with pricing experiments. That’s because you can actually TALK to your customers, know their preferences (or maybe even budgets) and have more data points to work with.
At this stage, collect all your prices. Most likely they are focused on standard business practices, aka costs & profits. What if you are wrong?
Step 2: Radically change your thinking
Have you ever heard of Sequoia Capital? It’s one of the leading Venture Capital firms in the world, so famous it was even portrayed in the show Silicon Valley. One of the key rules they have when it comes to their porftolio companies reads as follows:
Focus on perceived value: the gap between your prices & how much value customer thinks it delivers
Such an approach is called WTP: Willingness to Pay. In short, it leaves aside competitors’ prices & traditional cost-profit analyses to focus on maximizing impact and the customers.
This could not be more true for software companies, especially software houses and agencies. most likely you have encountered scenarios when a customer asked you for a change or addition that would be a laughable cost in terms of time & material. A few clicks, a line of CSS, a single image replacement. In the technical sense such changes are basically plankton. For the customer, however, this 1 tiny detail might’ve been millions of profits gained due to slightly better conversions.
A few years ago I was asked to optimize the user journey flow on a e-commerce website. Data clearly showed that customers had some difficulty with locating the cart button. The 1 and only change that was made to address this was…a new cart icon, with green background and white text (the rest of the menu was black text on white background). Conversions immediately jumped up by 17%.
How would you quote for such a process? Making the change took literally 20 minutes. However, for the customer this was worth wayyy more than 1/5th of a standard hourly rate.
The answer is: data. You need to understand and quantify the value delivered, so you can extract it in price.
Step 3: Gather, calculate and offer based on data
There are many types of data you can use for pricing experiments and discovery of the value you actually deliver to the customer. Take a look at what processes your product/service creates or improves. Can it be further divided? Then, put metrics on said processes. Let’s take a look at an example.
The customer asks you to create a CRM automation module. You could go with a standard “well, it’s around 160 dev hours, times $80 an hour, more or less $12800” .
Alternatively, you can ask the customer some questions. How big is the team using the system? How big will it be in the next few months? How much data is typed in daily by each person? How long does it take them? What are their salaries?
It might turn out that currently 10 people are typing in data manually for 4 hours every single day. Due to lack of effectiveness, the customer planned on increasing their headcount to 15 soon. The average salary of a salesperson is $3000.
By automating their work you save the customer costs in 2 areas:
- Current employees: half of their work, so also salary, is basically wasted on data input. That’s $15000
- 5 new hires won’t be needed, so another $15000 saved
Overall, the value for the customer is around $30K. That’s much, much more than $12.8K you’d get from looking just at the development hours at face value.
Now answer 100% honestly: in this scenario, do you think that the customer would object to paying $15K to save $30K? Personally: I think they’d sign the deal immediately just as good as with the lower price. The only difference being 15% more profits for you for the same amount of work.
Side note: you might be thinking “but what if my competitor does the simpler analysis, quotes $13K and gets the deal because the customer wants to save even more”? This might happen, but is quite unlikely due to 1 reason: you will calculate the benefits and show them to your customer. The competitor will likely tell them “we will build you a CRM module for $13K”, which does not communicate nor underline the value well.
We strive to make this series somewhat interactive. At the end of each article we will ask you questions. When put together, they will form a basic but sufficient pricing audit – one that will give you a great idea if you feel confident about your current prices.
- Are you quoting based purely on costs, or also the value?
- If you were to calculate the value on your most recent offer, did you overprice or underprice?
Tip: write down your answers as further reference will be useful!
Stay tuned for the next installment 👋