How to Announce: Crafting the Right Message for a Usage-Based Pricing Launch
This is the second in a three-part blog series on launching usage-based pricing. Check back in the coming weeks for additional installments.
Once you are ready to announce your usage-based pricing, there is still a lot of planning to do. You’ll need two separate launch plans. One for the new customers, and the other for the existing customer base.
Launching to new customers
Launching usage-based pricing to new customers is the easier task of the two, because it’s much like any other product launch. You want to plan messaging, have a message testing plan in place, track outcomes, and then course correct.
Begin with a set of hypotheses and a system to build predictions.
The hypotheses are what you expect will happen. Even if you don’t know what will happen, you still need hypotheses so that you know what to look for. In order for this to even work, you also need to have a culture where it is OK to be wrong so long as you have quick learning and response. The hypotheses help with the learning.
What are the typical hypotheses you might want to test?
- Usage. What usage patterns are you assuming? Make sure these are explicit in a shared document. You had to make assumptions as you designed the pricing, now make sure you track what is really happening.
- Value. How will usage impact the potential value to customer ? You should have a model that estimates the value your solution brings to your customers and how this correlates with usage. If it does not correlate with usage, you chose the wrong usage-based pricing metric.
- Value messages. What messages will resonate with the market? Different users will respond to different value messages. Are there differences between:
- the messages that drive purchase? and;
- the message receptivity that predicts usage?
Value messages should map to the value drivers that determine how much value your solution provides to users and to buyers. And in fact, there are three kinds of value driver: economic, emotional, community.
Economic value drivers
These kinds of value drivers are the impact your solution has on your customer’s business. In B2B they are generally the most important. Your solution has the potential to impact revenues, operating costs, operating capital, capital investment, risk, or optionality (give your customer more freedom of action).
The value drivers that are most important will differ from customer to customer and depend on business conditions. Currently, the uptrend in interest rates has made operating capital an important value driver for systems that can impact it (like billing systems).
Emotional value drivers
People will generally buy based on an emotional connection. So it stands to reason that emotional value drivers are crucial in both consumer applications, as well as B2B. This is especially true for new solutions that are perhaps not well understood.
Emotional value drivers range up Maslow’s hierarchy of human needs. So the higher your solution appeals to the top of the hierarchy, the greater your pricing power.
Community value drivers
These are the ways that your solution creates value (or destroys value) for people who are not your users or buyers. Environmental, Social, and Governance (ESG) covers part of this. Unsurprisingly, these types of value drivers have now become emerging concerns. Many people use the United Nations Sustainable Development Goals as a way to frame community value drivers.
Community value drivers should not be ignored in marketing, and even more so when choosing a usage-based metric. Can you design a usage-based pricing metric that guides your users towards choices that are in the general interest?
For more on value drivers, see Core Concepts: Value Driver.
Segment your customers and A/B test messages
So now you have hypotheses about which messages will have an impact and as a result, you have mapped messages to segments. You may even have gotten this right. But the world is constantly changing, requiring you to continuously test and adapt. A/B testing is a good way to test value messages. Design an A/B testing plan as part of your launch plan so that you can adapt quickly.
With usage-based pricing, you are going to want to go deep into the data.
Ask yourself, “Are there differences in usage patterns between buyers that responded to different value messages?” If you find such differences, focus messaging on the segments that show the most usage.
Down the road, you are likely to use some form of AI to predict future usage from current usage. Having well-structured data around value messages, response, and usage will help you do this.
Migrating existing customers
The first question you will ask yourself is “Do I want to migrate my current users?” The answer should generally be “YES.” Product-led growth companies want to keep things simple (or as Einstein is said to have said “as simple as possible but no simpler”).
Having customers on different pricing models is not simple. It makes all of your customer success optimization, predictive analytics, and overall revenue generation more complicated. For example, those customers on the legacy pricing model are like the sail you are dragging behind your boat.
However, you’ll want to avoid trying to migrate all of your current customers at once. You will need to decide whether the customer is in need of a direct touch with support from the customer success or account management team (curated). Or perhaps, you can automate the conversion process. If you can automate the conversion process, the easier it is for you If you have more than a few hundred customers, you will need to.
Before you make any decisions on how to migrate your current customers, conduct a simple segmentation exercise. Put your customers into four buckets using the following two axes:
- Low vs. High Value to Customer
- Low vs. High Customer Lifetime Value (LTV or the revenues you expect over the life of a customer)
For customer lifetime value, you may need to go deeper, as there are two reasons why LTV could be low.
Lifetime Value (LTV) = Gross Contribution * [Retention Rate / (1 + Discount Rate – Retention Rate)]
So a low lifetime customer value could reflect low prices, high churn, or both. Hopefully this was taken into consideration when you designed your new pricing model, the one that layers in usage-based pricing. It also has the power to influence the order in which you migrate customers to the new pricing model.
These two segmentations can guide you through the migration process, telling you where to automate and where to use a curated process.
There is one more decision to make, and that is who to migrate first. There are three basic approaches here. In most cases, there will be some customers who will see the subscription price decline, and others who will see it increase. Some advocate going after the customers who will see prices decline first. Others want to go after the customers that will drive revenue growth and leave any decisions regarding price cuts for the future. Before making this decision however, you want to remember the three principles of fair pricing:
- It correlates with value (which is why usage based pricing is gaining momentum)
- It’s consistent (similar buyers get similar prices)
- It’s transparent (as to how prices are set if not to the prices)
Given this, the best approach is a blended one that optimizes learning. Do not leave the customers that will see price decreases to the end. This violates the principle of consistency and slows learning.
Go back to customers who churned and to lost opportunities
As part of the migration work, consider going back to customers who have churned. A new pricing model, especially one that includes usage-based pricing, can often be used to bring people back. Hopefully the CRM has data on customers who have churned and why they have churned. If the data is accurate, a subset of these can be brought back.
You can apply the same approach from segmentation for the current customer base with the churned customers. There is great satisfaction in bringing a lost customer back to the fold.
The same thinking applies to lost opportunities in the CRM. There will probably be less data here, and no usage data, but it is still worth going back to the lost opportunities to see if you can win them now.
What incentives should be offered?
In many cases, you should include some form of incentive to current customers to move to the new pricing model.
One common approach is discounts. You’ll need to be careful here, as discounts can undermine value and establish lower reference value. An alternative to discounts is to put in a cap on payments for a fixed period of time (generally one quarter or one year). This gives both parties time to see the implications of the usage-based pricing.
Whether you take a discount approach or a cap approach, make sure that it is time-bound. It is reasonable to give people time to understand and adapt, but not on a “forever” plan.
Another crucial part is to only provide new functionality to customers on the new pricing model. This is one of the best ways to encourage migration, especially if you have a product roadmap where a lot of new value is going to be added. The best practice is to organize around value paths (the series of actions a user takes to get something of value). The usage metrics should map to these value paths. In this way, the value-to-price connection is reinforced for everyone.
Launching a usage-based pricing plan is all about value, value, value
Usage-based pricing can change the relationship you have with your customers. With fixed subscriptions you have a grace period–say two-thirds the length of the subscription–to demonstrate value to your customers. The stakes are higher with usage-based pricing. You need to be focused on delivering and documenting value from day one.
In our third post, we are going to get into the weeds and look at modeling the impact of usage-based pricing on the current customer base.