How the Ladder of Inference Protects You Against Decision-Making Pitfalls
In business, as in life, you’re not given a clear roadmap to your goals. Instead, your ability to make impactful strategic decisions often determines your success.
That’s why understanding and improving the way you make decisions is a crucial skill for any business owner. After all, you can’t improve a process if you don’t know what it is.
One way to visualize how we make decisions is the ladder of inference. It’s a mental model you can use to understand why you jump to conclusions and how prior beliefs and subconscious bias affects actions.
WHAT IS THE LADDER OF INFERENCE?
In 1970, psychologist Chris Argyris developed the ladder of inference, which models the brain’s process when people jump to conclusions. While it only takes a split second to draw a conclusion based on our experience, Argryis’ model identifies seven steps that take you from observation to action.
Here’s a closer look at the individual rungs of the ladder:
- Experience and observation: You observe objective facts and circumstances in the real world.
- Filtering and selection: You select, or choose to pay attention to, some facts based on your past experience and existing beliefs.
- Interpretation and reasoning: Giving personal meaning to the facts you have selected.
- Analysis and assumptions: You make assumptions based on the personal meaning given to the selected facts.
- Drawing conclusions: You come to one or more conclusions based on your assumptions and prior beliefs.
- Adopting beliefs: You adopt beliefs about the world based on your conclusions.
- Taking action: You take actions based on your beliefs.
The ladder of inference tends to get a bad reputation since it explains why we jump to conclusions.
However, you can use it to better understand your reasoning process to improve it. To do so, you have to start by understanding its inherent limitations and pitfalls.
LADDER OF INFERENCE LIMITATIONS
The bottom of the ladder begins with objective reality and increases in subjectivity as we move up to beliefs and actions.
In other words, the farther you get along the ladder, the more likely you are to develop beliefs based on your existing assumptions rather than using all of the available data.
Let’s see what that looks like in action.
Consider an example where you have a colleague named Sarah who has rescheduled a meeting with you two times now. She is working on important projects with two departments and meets with six people each week.
- Experience and observation: Here, you have all the facts, including the meeting rescheduling and your employee’s existing workload.
- Filtering and selection: You pay attention to the fact that Sarah has rescheduled her meeting with you twice.
- Interpretation and reasoning: You interpret the situation and conclude that Sarah is only rescheduling her meetings with you.
- Analysis and assumptions: Based on your prior experience, you assume people reschedule meetings that aren’t important or if they want to avoid someone.
- Drawing conclusions: Given your assumptions, you conclude Sarah has been trying to avoid meeting with you.
- Adopting beliefs: Since Sarah is actively avoiding you, you now believe Sarah does not like you as a person. She doesn’t think you contribute as much value at work as she does.
- Taking action: You avoid working with Sarah.
Jumping to Conclusions
In the above example, essential facts fall to the wayside in the second stage. You focus on your missed meetings without considering Sarah’s workload and other responsibilities.
By jumping to conclusions instead of working step-by-step, you miss out on other possible explanations for Sarah’s actions:
- Sarah may be rescheduling meetings with other people during the week.
- Sarah has not had time to prepare for your meeting, and she values your time, so she’s rescheduled to another day to ensure she’s prepared.
- Sarah is under pressure to complete another project that has a tight deadline.
Moving through the ladder without questioning your assumptions often leads to conclusions based on your existing beliefs instead of the facts at hand.
If you want to improve the quality of your decisions, observe how your thoughts progress through each stage and ask yourself, “Am I ignoring any important facts?” or “Are there other ways to interpret this situation?”
If left unchecked, prior beliefs and inherent biases can cloud your judgment and lead you to miss alternative paths of action.
For instance, in stage four of the example, you assume people only cancel unimportant meetings or when they want to avoid someone, leading you to believe Sarah doesn’t value your work or just doesn’t like you.
Yet, there are several other reasons why someone may cancel a meeting, but they don’t enter your thinking process.
Many of our biases are not known to us. Instead, they affect our beliefs and actions without our knowledge. One example includes confirmation bias, which is the tendency to select data or interpret facts in a way that supports your existing beliefs.
The good news is when you take time to understand your subconscious biases, your awareness of them can improve your decision-making.
If you want to clarify your mental processes, you need to accept and learn about your unconscious beliefs and use that awareness when making decisions.
Specifically, pay attention to your existing beliefs, assumptions about other people, thought patterns, and decision-making motives.
THE LADDER OF INFERENCE IN BUSINESS
In his book, The Fifth Discipline: The Art and Practice of the Learning Organization, author Peter Senge shows how to apply Argyris’ ladder of inference model to improve group decision making and business processes.
Senge argues that a systems-thinking approach helps business owners invest resources more wisely and avoid costly assumptions. To get the most out of the ladder of inference, successful companies combine it with data-driven and customer-informed decision-making.
Leveraging Data-Driven Decisions
According to Forbes, switching to a data-driven mindset can help companies increase customer acquisitions by 35% and get to market 40% faster.
Using data to drive decisions helps business leaders consider the big picture and reduce the influence of assumptions. In other words, you gather data from more sources to help you avoid jumping to conclusions.
Collecting Customer Perspectives
One of the most critical data sources for businesses is the customer perspective. Investing in marketing campaigns and product launches without customer input can lead to costly flops like Cheetos Lip Balm or clear Pepsi.
Data-driven companies use various tools to solicit customer opinions, including marketing surveys, customer service logs, and live chat.
EXAMPLES OF DATA-DRIVEN DECISIONS
A report by McKinsey & Co. found that executives spend roughly 40% of their time making decisions. Whether it’s a marketing campaign, product launch, or the look of your website, you have a lot of choices in front of you.
Here are some examples of how you can leverage data to make better decisions and avoid faulty thought processes in your business.
In the world of online businesses and ecommerce stores, website design can make or break your business. Leaders often want to know how to increase conversions and get users to perform actions like subscribing to an email list or purchasing products.
In this example, data-driven businesses need to gather information about the user experience instead of making random changes.
To do so, you can use tools like:
- Heat map software like Hotjar or Content Square to track customer interactions
- A/B testing for different website designs
- Usability testing to observe where customers look, click, and interact with your page
These tools help you see how customers interact with your website so you can make adjustments to eliminate confusion and make your pages easier to use and more engaging.
For best results, avoid making assumptions about how your customers will react. Remember, you know your brand and products inside and out, but your customers don’t. A website that makes sense to you could confuse first-time visitors and even put off interested buyers.
Product Research and Development
Understanding your customer’s wants and needs is the most integral part of developing products and features. Too many products and services fail because companies don’t follow the proper procedure for the ladder of inference and design something that doesn’t match their customer’s needs.
Successful data-driven companies invest in research and development strategies that help them understand customer needs and preferences before launching a new product.
Here are some of the top tools you can use to create a data-driven product strategy:
- Test groups
- Beta testing
- Soft launches
By observing customers and getting feedback early in the process, you can collect data that helps you understand their pain points or look for friction in existing products.
Then, you get to base your analysis and assumptions on factual data as you work to find a solution for the problem.
When developing new products and features, avoid funding large projects before having data that proves customer needs. Otherwise, you may end up wasting valuable resources on biased assumptions that don’t accurately reflect your audience.
Customer retention is a top priority in the SaaS industry, where customers pay recurring subscription fees. If you want to keep customers, you can’t afford poor customer service.
According to Zendesk, 50% of customers will switch brands after one bad experience, and 80% will change after several poor experiences.
To improve your customer service quality, start by understanding existing users’ problems and pain points.
The following customer service tools provide valuable data that helps you understand what’s working and what needs improvement in your customer service processes:
- Support ticket systems
- Live chat transcripts
- Customer service surveys
- Call center recordings
Making changes to your product or customer service solutions based on assumptions rather than data puts you at risk of investing valuable resources on a non-existent problem.
You can use data from surveys and support transcripts to understand which issues apply to your overall customer base and have the most significant impact on retention.
Paid advertising is a beneficial marketing tactic for companies in competitive landscapes or brands looking to increase recognition.
But how do you ensure the money you spend actually brings in new customers? Let the data tell you.
Analyze the performance of each campaign before trying something new. That way, you can make changes based on what has worked in the past.
Here are the metrics you should be tracking on your marketing dashboards to understand the effectiveness of your paid campaigns:
- Total number of clicks
- Cost per click (CPC)
- Cost per acquisition (CPA)
- Total ad budget
- Social media engagement and impressions
- Incoming referral traffic
Get the most out of your investment by starting with a small budget for testing. Run a few campaigns to discover what works and scale your efforts based on real user engagement.
You want to avoid running massive marketing campaigns without any data from your target audience. Doing so puts you at risk of losing a significant investment and not seeing good returns.
FINAL THOUGHTS: USING THE LADDER OF INFERENCE TO IMPROVE BUSINESS DECISIONS
The examples provided highlight the importance of gathering as much objective information as possible before working your way up the ladder of inference.
If you ignore biases and only use subjective data, the ladder of inference can lead you to a cycle of hasty, biased decisions. But if you pay attention to each step and focus on collecting objective data, the process improves your ability to see the big picture and prioritize accordingly.
Switching to a data-driven mindset means investing more time at the beginning of your decision-making process to prevent jumping to conclusions or letting bias affect your actions.
When you employ thoughtful, data-driven strategies, you improve your chances of success and avoid wasting resources.
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