I Sucked At Saying No To Stakeholders Until I Discovered This

Throughout my time as a developer and a project manager, I’ve found myself experimenting with a number of different strategies for defending my team’s decisions and convincing clients to go along with our vision.
When I began this journey, harnessing a reliable method for this seemed a little like black magic. I’m an ambivert and this task seemed better suited for an A-type, gift-of-gab person. With time I’ve realized it just takes time and careful consideration.

The approach I now use is producing positive results. To save others the trouble, I thought I’d share the insights and discoveries from my experiments.

Clients invariably want all of their expectations met. They’re not only making a sizable financial investment for the proof of concept, but stakeholders’ names are also on the line. The attachment for many stakeholders is personal — they see the delivered project as a measure of their performance.

When a client’s preferences aren’t met, doubt starts to rear its ugly head throughout your project. And in the context of a high-profile project, this doubt can be the first step towards the demise of an otherwise healthy relationship.

Notice I used the word “preferences”, not “expectations”. Why? Because preferences are subjective, and though users are individually subjective, numbers and user data are not.

So let’s go ahead and explore a few different levels of how to say “no” to your client and get them on side with your team’s vision.

Probably the most straightforward way to say no is to… well, just say no. I know many PMs who see themselves as one side of an adversarial process — the defender of their team and of their organization. Their strategy is literally to play hardball: say no, and hold your ground.

My experience is that the result generally isn’t positive — either it’s a won battle in a project-long war between client and PM, or it’s a bullying situation where the PM wears a client down into submission, leaving behind resentment and that sense of disappointment.

I’ve tried this strategy, and honestly, it’s not for me. So I tried a different approach.

Something that did have a better effect for me was a more dogmatic approach: using trends and the opinions of vocal thought leaders as “proof” that my team’s position was sound. Rather than simply shut down clients’ ideas, we would invest the time to share some of the writings out there advocating for the approach we were recommending.

This got us closer to evidence-driven decision-making, but our “proof” didn’t have the adoption yet to measure success. Likewise, we hadn’t had the time to really evaluate the new method ourselves. And as much as it helped me be persuasive, it didn’t help build our client’s understanding of their specific needs and challenges. In fact, it often kept them on the outside.

But it did something else: it built trust.

By opening the discussion to our clients, reviewing team progress with them, and providing context on the team’s performance, we opened the door to building our client’s confidence in the team as well as a shared understanding of the problem set.

Okay, so getting to data-based decision-making in our projects wasn’t turnkey. For anyone in the process of making this transition, here’s what I would recommend:

Yes, that’s a loaded statement, but it can start with just the desire for each team to have some kind of methodical approach and evidence-based rationale for doing things.

Your projects won’t have a big data engine with machine learning right off the bat. Use an MVP mentality to build a data framework that is meaningful and viable.

Secondary sources such as research papers, reports, and open data can augment areas where you don’t have data of your own.

You’ll also need to build a culture of data within your client or project sponsor. Use every opportunity to tilt their mindset towards data-driven decisions rather than subjective preferences.

There’s no silver bullet. What works well for one project may not work at all for other projects. Keep a playbook of your process and continue to test and iterate.

I’d be remiss not to mention that having all the data doesn’t mean it’s going to be smooth sailing from there. Whereas you may be able to use data to figure out what a client really needs versus what they say they want, if the data doesn’t reflect well on them or goes against their original vision, you can run up against resistance, inadvertently build animosity, or your efforts might just get ignored.

The thing to consider with data and its delivery is the human component. From a psychological standpoint, sharing data and driving that conversation still requires tact, sensitivity, and professionalism.

Part of the art is packaging the information in such a way that it will be heard and considered. Some elements that factor into that include:

Context: plan when and where to have the conversation. Some data is better-prefaced one-to-one before being discussed in front of your team or your client’s team.

Word choice: practice using words that frame the data neutrally, and avoid using words that can be construed as abrasive or confrontational. Here are some examples of what I mean:

Things to avoid

Things to use

Tone: make a strong recommendation, but try to provide options that keep your influential stakeholders and sponsors in the driver’s seat. Just make sure they’re aware of the consequences of their decisions!

For me, including the client as part of the decision-making team and sharing our process for deciding their highest-value requirements has increased their feeling of control and ownership of the project. Sharing the idea of intelligence-based design has also helped build my clients’ confidence in my team, knowing that they’re working methodically rather than just on a whim.

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