In the coming weeks, we’ll be publishing interviews with revenue leaders – people who are having an impact on this space – and getting their insights on the changes they are seeing and what drives them.
As part of this ongoing series of interviews, we took some time to chat with Ammanuel Selameab, a pricing optimization expert and founder of Accrue, the #1 pricing optimization tool for revenue teams.
Below are excerpts from that discussion:
Ammanuel, thank you so much for taking the time to chat today. Could you tell us a bit about your background and your current venture, Accrue?
My entire professional background has been in pricing. During my time as a Pricing Analyst, I had an “Aha moment” – I realized that all companies are guessing when it comes to pricing. It was clear to me that there wasn’t a good option to help companies add more rigor and process to making pricing decisions.
That’s why I decided to found Accrue in September of this year.
Accrue is the default enterprise solution for pricing. Pricing in the last 8 years is one of the most neglected and poorly researched aspects of a company’s growth plan.
But it also has the most outsized impact on revenue and LTV. It’s the most powerful tool and the most mishandled tool.
Our software ingests data to identify pricing opportunities to exploit and runs experiments and iterates on opportunities that don’t work. Finding opportunities to increment revenue and testing the ones that have some credence, all in an automated fashion.
What does "Pricing Optimization" mean to you?
I actually don't love using that phrase, I prefer maximizing revenue.
There is a great need for businesses in our space to identify what is the optimal pricing and monetization that will maximize revenue in the long term.
If you charge too much, you are going to lose a certain percentage; If you charge too little, you’ll win but not get enough revenue. There’s more to maximizing revenue than going up and down on price.
Pricing decisions can be extremely complex.
B2B software products tend to have many different pricing packages, how do you determine the mix of features, charges, and pricing models in those packages? Those are all monetization questions that aren’t strictly related to price.
Monetization goes beyond price.
Can you tell us a bit about "Pricing Experimentation"? What does it mean to you and why is it important?
When you are trying to figure out what the right price should be, there are basically two broad approaches:
- Stated Preference Analysis: In this model, you go directly to your target and ask the price they are willing to pay. There are various techniques to accomplish this, whether it is a standard discussion or something like Van Westendorp's Price Sensitivity Meter, the goal is to find the price that someone would be willing to pay.
- Derived (or revealed) Preference Analysis: In this model, you actually observing what people pay. While classical elasticity is backward-looking, experimentation of this nature is more of an active exercise - an experiment.
In my estimation, any data you can bring to bear in making decisions is good. However, the most reliable strategy is to make real offers and see what people do - Observational, experimentational data.
The main challenge with surveys is that what people say and what they do is different. When you are asking a hypothetical, it does not always reflect what would happen in actuality.
What someone says they will pay and what they actually will pay can be two very different things, which is why I founded Accrue as a tool to support these kinds of pricing decisions through experimentation.
There are several frameworks that companies can implement for pricing experimentation, but the gold standard is the A/B test model.
In the context of pricing experimentation, an A/B test would include two similar groups of customers, present the test group with the thing you want to analyze (higher price, different packages, etc.) and the control group your standard offering.
If you don’t leverage A/B testing in your pricing experimentation, you run the risk of not knowing if the changes you see are a result of a pricing change, or was it some external factor.
There are too many confounding variables that can hurt meaningful inference. Test and control, if done effectively, is the only thing that can prove the cause of differences in cohorts.
While we performed these tests manually in my previous roles, Accrue was built to do this automatically.
What are some new and innovative pricing models you have seen and what trends are you seeing more generally in pricing?
I'm actually not seeing much innovation in this space at the moment; most pricing models tend to fall into predictable subscription models.
In terms of trends, I do think we will see the continued popularity and growth of the Pay-as-you-go and usage-based models. We are seeing a lot of companies migrating to this model as it is more transparent to customers and is better aligned with the value that customers are paying for.
Mailchimp is a great example of this as their pricing is based around data packages, a usage-based model; you are paying relative to the size of customer data with modules that assist you across different functions.
A lot of the stagnation that I see with pricing is really a function of treating it as an afterthought, often driven by anecdotes and not science. It often becomes a "me too" strategy in which you look at competitors and mimick their pricing.
It doesn’t matter what "Pricing Pro" tells you, running an experiment will trump any other tactic that one could devise. No one can know, prima face, what type of changes will have a positive impact.
I find myself, more often than not, evangelizing pricing and its outsized influence on revenue. Most people look at increasing TAM for revenue but don't realize that pricing is an even more powerful lever. It is neglected. Companies are always looking at top of the funnel and conversion rate optimization strategies instead.
But pricing has a much greater upside. If you wanted to double revenue, you could double the price or you could sell twice as many units, which one is harder?
I’m biased, but the biggest thing I try to communicate is the importance of thinking strategically about how you are selling stuff; it' so critical.
It's important to get people to put pricing front and center, something that permeates organizations, and something people have an intuition about.
There needs to be a greater awareness of its impact.