Pricing is not something that we do once and we're done. It's a dedicated role with a dedicated owner that continuously researches, reviews and adjusts from one quarter to the next. According to Price Intelligently, who we have learned a great deal from, and almost everything in this article could be attributed to, our quarterly cadence should look like this:
- week 1 to 4; customer / market research
- week 5 to 7; the customer communication plan and impact analysis
- week 8 to 9; implementing changes
- week 10 to 12; evaluating changes, compiling lessons learned
Despite having a dedicated owner of our pricing we do consider it to be a group effort. The pricing committee for our company is our meta-scrum. All analysis and proposed changes are vetted and thoroughly reviewed there before being released. And when released we typically use the following key performance indicators to evaluate the results:
- Monthly Customer Acquisition Cost
- Monthly Recurring Revenue with Growth (Prior Year over Current Year MRR) and we want this to exceed 20%
- Monthly Customer Churn Rate and we want this to be less than 1%
- Customer Lifetime Value
- Customer Lifetime Value to Acquisition Cost Ratio and we want this to be greater than 3 to 1
Pricing is centred around a "value metric" that your customers evaluate your pricing by. It is the primary unit of consumption that they will hire you to deliver (in jobs to be done speak) and that they will on average evaluate your pricing plans by. The value metric must align with the customers needs, it should grow with the customer as they grow, or as they choose to consume more of your product offer, and it should be easy for them to understand and compare across your pricing plans. For us, our value metric is pretty straight forward, it is our cost per display.
Ideal Customer Profiles
Which begs the question. Who is the customer? How do we know what they want from our value metric?
To figure this out we want to look at our existing customers and determine who is ideal. Who derives the most value from what we do and is willing to pay us, profitably, for the products and services that we provide.
For every customer we have and every opportunity that is in our pipeline and that is showing a high likelihood of closing we need to know the following:
- the sector (industry) they are in
- the size of their company
- what role our purchaser holds within their company
- their consumption (quantity) of our value metric (or intended consumption for opportunities)
- what features they value most, and least (Relative Preference Scores - defined next)
- their willingness to pay (Price Sensitivity Analysis - defined next)
- their lifetime value to us (or forecasted life time value for opportunities)
From the above data we segment our customers into 3 to 5 ideal customer profile groups based upon their life time value. By segment we will now know what sectors to target, the typical size of company we should pursue, and who from those companies are our purchasers.
For each aggregated customer profile we need to know what % of our customer base they comprise, what their life time value is, the customer acquisition cost, and the ratio of lifetime value to customer acquisition cost for this profile. Anything less than a 3:1 ratio is unlikely profitable to pursue unless they are a gateway to our higher tiers.
Everyone, from product development, pricing, marketing, sales, customer success and support now knows who they are working for and what they need to do to maximize returns from our highest value ideal customer profiles.
Product development knows who to build for based upon their relative preference scores. Pricing knows by profile what their price sensitivity is to maximize both market share and revenue. Marketing knows who to target and how to speak to them. Sales knows who to pursue and sell to. Customer success knows to focus on maximizing highest tier profiles by retaining them and converting lower tiers into that segment. And support takes great care to eliminate all friction for these high value groups.
As a whole, the goal of the company is to move customers up the ideal customer profiles to consistently increase the % of the customer base that is in the highest tier. Lincoln Murphy of Customer Success fame, refers to this ladder of profiles as Success Vectors.
Relative Preference Scores
To determine the features that are valued most, and those that are least valued, we need to talk to our customers. Present them with our list of hypothesized features that we believe our customers want and ask them to rank rate them as either Most Preferred or Least Preferred. Also ask them if there is anything that isn’t on our features list that should be. From this data we calculate the relative preference score for each feature by subtracting the number of times a feature is least preferred from the number of times it is most preferred and dividing that number by the number of participants in the survey. And then then plot the relative preference scores for each ideal customer profile and update it at least quarterly.
Price Sensitivity Analysis
Next we need to determine their willingness to pay by asking them how they value the job that they would hire us to do by classifying on a sliding point price scale the following price sensitivity questions;
- At what price would you consider the product to so expensive that you would not consider buying it? (Too Expensive)
- At what price would you consider the product to be priced so low that you question the quality of it? (Too Cheap)
- At what price would you consider the product is starting to be expensive, so that it is not out of the question, but you would have to give it some thought before buying it? (Expensive/High Side)
- At what price would you consider the product to be a bargain - a great buy for the money? (Cheap / Good Value)
And then plot the results for each price sensitivity questions; Too Expensive, Too Cheap, Expensive / High Side, Cheap / Good Value, on a chart that has the Price Points on the X axis and the % Of Respondents on the Y axis. Between the intersections of the 4 Price Sensitivity lines is the acceptable Price Range.
We can determine price elasticity by changing the Y axis of the Price Sensitivity Analysis chart from % Of Respondents to % Of Sales Lost and taking the area below the Too Cheap line and Too Expensive line to plot the price elasticity of our market. The lowest point of the area drawn will result in the highest market share.
However, highest market share might not equate to the highest aggregate revenue, we need to slide along the scale to determine our optimum market share versus price point as it relates to the best bottom line for our company. This can be calculated by determining the potential revenue at each price point for 100 customers by multiplying the price point by the percent of customers that would be willing to pay that amount and when plotted on our Potential Revenue Chart it clearly presents the cumulative revenue impact of our price point options for 100 customers.
To present our pricing we package it up into plans, with each plan targeting a respective ideal customer profile. Typically moving from our lowest tier value plan on the left through to our highest tier plan on the right. Each plan is packaged based upon the ideal customer profile delineations for feature preferences and price sensitivities, with the value metric and the features provided increasing as the plans move from lowest to highest tiers.
The pricing package design checklist:
- Keep it simple and clear so that potential customers can make an informed decision, full disclosure, no need to “call”
- Highlight your value metric to show exactly that matters most to the customer across plans
- Provide non-friction gateways to plans, not trials, how can they sample and continue sampling without barrier causing trial expirations
- Redirect from bargain shopping to value seeking by using comparatives between plans
- Use the number 9 in prices
- Anchor and drive the sale of lower priced items by comparing to much higher priced items
- Structure prices using a 3 part tariff; base fee, includes X value metric, anything above X costs Y
- Make sure all plans maximize their respective values while not robbing from each other
- Do not do competitor price comparisons - customers are tired of them
- Descriptive text conveys time and experiences more than price, customers tend to make decisions based upon time saved and the experience delivered more so than price
- Provide local pricing - design for local markets, use their currencies, their language, use the PPP to set local prices
- Do not discount our prices as a negotiating tactic. It lowers willingness to pay and increases churn rates thereby decreasing lifetime values. Do consider providing published discounts for nonprofits, education, etc.
- Do not A/B test the pricing page - there is rarely enough traffic to statistically validate the results and it annoys the hell out of customers if they have an inconsistent pricing experience