Skip to main content

Stop Bringing Backlogs: Start Telling Money Stories To Execs

Over the last few years, I’ve seen the same pattern again and again. The agile movement helped me a lot. It pushed me to focus on real outcomes for users, and on changes that are clear and valuable for buyers. Product management brings the whole picture and structures the discovery phase. From a product marketing point of view, it became easier to speak about pains and benefits in simple terms. But with executives, something was still missing. 

At first, I spoke about process. Then I tried to speak about “value”. Every time it was too long, too fuzzy, and it didn’t really land. I did not have a short, clear way to explain why a product decision mattered in a language that made sense for executives. 

That’s why Rich Mironov’s talk “Crafting business cases that win” at Productized conference really connected with me. He starts from a very clear point: most executives do not care about our backlogs, frameworks, or internal product practices. They care about revenue this quarter, a few big deals, and strong financial results. If product people stay in the language of process, we sound like we are giving excuses, not helping the company succeed.

 Rich suggests a different way: tell “money stories” instead of feature stories. For every important idea, we should be able to say, in one or two sentences, how much money it could bring or save: more revenue, new business, or lower costs. The numbers can be rough, but the story must end with a currency amount.

He gives simple patterns we can reuse:

  • Upsell: number of basic customers × price difference × share who might upgrade → extra revenue per year.

  • New market: number of possible customers × revenue per customer × expected win rate → new yearly revenue.

  • Support savings: number of tickets × cost per ticket × share we remove → money saved each year.

The goal is not perfect prediction. The goal is to “count the digits”. Is this idea worth a few thousand, a few hundred thousand, or a few million per year? That is enough to see which ideas deserve serious work, and which ones we should drop before asking engineering to estimate them. 

His final takeaways are the ones that stay with me: Talk about money and outcomes with executives, not about process. A rough order‑of‑magnitude is usually good enough. Always give “or” choices: if we add this new request, which exact project do we stop or delay to make space? For product people who struggle to make roadmaps clear for executives, this way of thinking gives a simple bridge between user‑focused work and board‑room discussions. Thanks to Rich Mironov and PRODUCTIZED

Comments

Popular posts from this blog

Lancement de ProductTank Lyon

Mise à jour 05/05/2023 : Le COVID aura tué ma motivation d’essayer de relancer ce meetup. Peut-être que d’autres le feront.  ---- Tout d’abord, bonne année 2020. Je me suis investi ces dernières années dans les communautés/événements CARA Lyon, MiXiTConf, LyonDataScience et CaféDevOps sur Lyon, France. Ces activités m’ont permis de comprendre les experts de ces domaines, d’apprendre quelques notions fondamentales à travers leurs exposés et d’améliorer mes capacités d’échange avec eux. Product Manager depuis plus de 5 ans, je désire améliorer mes réflexes et compétences dans mon domaine. Le faire à travers des rencontres/meetup est ce que je préfère et j’aimerais retrouver la stimulation des communautés dans cette discipline. En cette année 2020, quelques Product Manager lyonnais, lançons, le meetup ProductTankLyon à Lyon, France. Le réseau ProductTank compte plus de 150 meetups dans le monde et profite des conférences, blog et podcast MindTheProduct. Ins...

Learning about Data Science?

This is the end of a beautiful summer, and also one of the warmer recorded in France. I’m continuing my journey in the product management world and today I’m living in the product marketing one too. I will blog about this later. During this first half of this year, I read several articles on big data and started to understand how important the data science discipline is. Being able to define a direction/goal to search, collecting the proper data, then using a collection of techniques to extract something others can’t see - it sounds like magic. Also, when I listened to the Udacity Linear Disgression podcast episode “Hunting the Higgs”, I understood people with these skills can be better at solving a problem than the domain experts themselves. Katie Malone explained that in a competition to solve a particle physics problem, the best results came from machine learning people. Then I read the article about Zenefit on the vision mobile website : “Zenefits is an insurance compan...

My Learning Journey in the World of AI Agents: A Comprehensive Overview

I'm currently learning about AI agents and thought it would be helpful to share my findings. Below, I've grouped AI agent technologies into 13 main categories. Each category has a brief explanation to make things clearer. After the categories, you'll find a list of the technologies with links to their websites for further reading. In progress, will be updated. 1. Design & Architecture Frameworks These are the foundational patterns and models that define how AI agents are structured and behave. They include various architectural approaches like reactive and proactive designs, cognitive models like BDI (Belief-Desire-Intention), and frameworks that specify how agents interact with their environments. The newer ReAct Pattern and chain-of-thought training frameworks help agents reason and act more effectively. 2. Development & Building Tools These tools facilitate the creation of AI agents, from comprehensive frameworks like LangChain and Microsoft AutoGen to vi...