Revenue leaders who rely on past experience, best practices, and biases to make decisions are more likely to fail than those who truly embrace data-driven decision making.

We’re in an era where success is no longer replicable, and every decision you make matters more than ever. Yet, many revenue leaders still fail to acknowledge that hinging performance on biases like experience, best practices, and opinions invites unnecessary risk. In a global economy where competition is fierce in nearly every industry, you can’t afford to make broad sweeping decisions based on what’s worked in the past. Every decision has to align with the nuances of your industry, your business model, and, most importantly, your potential customers.

It’s not just about the decisions you make, it’s about how you make decisions. Your job as a revenue leader is to help set the direction for the company’s growth, but many leaders focus more on being authoritative and decisive rather than being intentional. With statements like “fail fast” being thrown around, the quick decision-maker is often portrayed as a hero, regardless of whether the logic behind the decision was solid.

The risk of biased decision-making

Many biases lead us to make misguided decisions, but the one I find most interesting in this context is the action bias. The Decision Lab describes the action bias as “our tendency to respond with action as a default, automatic reaction, even without solid rationale to support it.” Rushed decisions that lack logic can stifle curiosity and discussion, break trust, and impact your ability to learn from decisions.

The way you present your decisions can make things even riskier. Lumen Learning says studies have shown that when people say they are 100 percent sure about a decision, they’re usually right between 70 and 80 percent of the time. This is a result of the confidence bias, which is when people are overly optimistic about how right they are. In other words, we can rarely be certain about an outcome unless we test.

A revenue leader’s role is not just to be decisive; it’s to set the tone for decision-making so you and your team can uncover new pathways to better performance, overcome obstacles, and grow the business. The first step is understanding and acknowledging your biases and observing where they arise.

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Where to check your bias

If you want to be a data-driven leader, here’s where to start checking your bias:

Your go-to-market strategy: The risk of letting your bias guide decisions is not a new concept and often starts at your business’s foundation. According to CB Insights, 64% of startups fail due to either a lack of market need, flawed business models, or mistimed products. The “I know my business best” mindset is one of the most dangerous assumptions out there because it’s not about what you think; it’s about whether the approach you take reaches and works for your customers.

Your performance expectations and benchmarks: The list of things that can impact conversion rates is bigger than ever, which means there is no one-size-fits-all recipe for growth. As early as 2021, Unbounce saw the signs of this in its Conversion Benchmark Report with average conversion rates varying by up to 9.4% between industries. Sustainable growth requires understanding which factors impact your conversion rate and what’s realistic for your industry and business model.

Your growth levers: Business models are becoming increasingly complex, especially in industries like SaaS and ecommerce. For example, this Harvard Business Review article attributes this to the “rebirth” of subscription SaaS and the need to shift from “scalable” growth to “sustainable” growth. Be wary of using a “playbook” style approach because growth levers should never be solely informed by what’s worked in the past. They should be adjusted constantly based on what’s driving growth right now.

Your approach to trying new things: Let’s capture this one in an example. Many marketing leaders are pushing AI on their teams, often because they assume that increasing output with AI will improve performance. This is an example of a broad assumption that needs to be validated within your unique context. In a recent study by Neil Patel Digital, human-written content outranked AI-generated content 94% of the time. This statistic shouldn’t entirely discourage you from using AI for content creation, but it should signal caution and the need to test it.

Understanding where biases arise is a critical first step, but the real work is overcoming them. Embracing and fostering a culture of experimentation is the best antidote to biased decision-making.

Foster a culture of experimentation 

Data, experience, and best practices represent moments in time. You need to know what has worked to help inform decisions, but you also need to understand what’s working right now (and why). Experimentation is the best way to mitigate biased decision-making, but it requires effort, consistency, and intention.

Here are a few specific ways you can foster a culture of experimentation:

• Make A/B testing a part of your workflow: One of the biggest mistakes teams make is only leveraging experimentation reactively to save a low-performing campaign or settle an argument. It’s difficult to test (and reap the benefits of A/B testing) if you don’t do it often, which means it needs to be built seamlessly into your workflow.
• Reiterate that A/B testing isn’t a “pass” or “fail” exercise: When you run experiments, you’ll either uncover something impactful or something inconclusive. You shouldn’t be discouraged by inconclusive results because they reveal the things that don’t significantly impact conversion, so you can focus on what matters.
• Invite curiosity while mitigating risk: Big, bold, creative ideas can often feel risky. The reality is they’re only risky if you don’t test them. There might be more risk in not trying something new if you find that your more creative idea performs twice as well as a more familiar concept. Your team also becomes more likely to embrace A/B testing if they see the rewards.

Testing is critical to driving better performance, but it shouldn’t be overly complex. If it feels onerous, you likely don’t have the right mindset, approach, or tool.

Now that you know what it takes to build a culture of experimentation, let’s talk about how it will make you a better revenue leader.

A better approach to decision-making 

Revenue leaders measure and motivate their teams using various forms of OKRs and KPIs. That said, if your approach isn’t methodical and measurable, you’ll never really understand why you succeed or fail.

If you approach decisions with an experimental mindset, you’ll:

• De-risk decision making: If you’re testing an idea you’re uncertain about, it’s better to know that going in. You can then adjust how you invest in that idea to account for any associated risk. For example, A/B testing with a smaller percentage of your audience or investing a smaller portion of your budget upfront.
• Be better positioned to optimize: If you approach a decision with an experimental mindset, you’ll need to learn how to design for measurability. Designing for measurability ensures that the insights you get are specific enough to optimize your campaign on the fly and determine what factors most significantly impact conversion.
• Become a higher-performing leader: When you’re more methodical about the things you choose to test, you’ll become more knowledgeable about how to best engage and interact with your customers. By understanding the nuances behind what impacts performance and why, you’ll become better positioned to make strategic decisions and demonstrate your function’s impact on the business.

The next time you feel the urge to force a decision, remember to slow down and consider the type of leader you want to be. Check your bias, encourage your teams to experiment, and remember why A/B testing is so rewarding.

Revenue leaders need to see experimentation as business-critical because success is no longer about doing more; it’s about achieving better results with less. The best thing you can do for your business is put your biases and ego aside and let your data do more of the talking.

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Also catch; Episode 178 Of The SalesStar Podcast: RevOps and Revenue Generation Best Practices with Derrick Herbst, Director-Business Transformation at Conga