What is inductive reasoning?

Inductive reasoning is a process of creating general conclusions based on specific information. People implement inductive reasoning when they rely on past experiences to make conclusions about current situations.

For example, Bob meets five black dogs who bark at trees. He concludes that all black dogs bark at trees.

In research and marketing, inductive reasoning is more complex. It involves gathering data, identifying patterns, and drawing educated conclusions. This method can be highly effective for analyzing target audience behavior and building a successful sales strategy.

Let's take a closer look at how inductive reasoning works for organizations.

Inductive reasoning is a process that has three stages:

• Observation: Noting that in one particular situation, one fact is true

• Pattern: Discovering more situations when this fact is true

• Conclusion: Concluding that a certain fact is true in specific situations

If you look at the above example, you can see that:

• Observation: Bob sees one black dog that barks at a tree.

• Pattern: Bob meets five black dogs that bark at trees.

• Conclusion: Bob believes that all black dogs bark at trees.

People use inductive reasoning to understand our world in everyday life. In many cases, their conclusions are accurate. However, without gathering sufficient data, it's easy to make mistakes.

The main downsides of inductive reasoning are:

• The conclusions you draw are only as good as the evidence and knowledge you gather.

• New evidence and knowledge could prove your conclusion wrong.

For example, Bob has seen too few black dogs to conclude that their entire population has a vendetta against trees. To ensure accurate results, he would need to conduct in-depth research and observe hundreds of dogs who bark at trees.

He would need to ensure that trees were the only reason black dogs bark when he meets them. Bob would also need to check if dogs with fur colors other than black also bark at trees.

That's where professional inductive reasoning comes in.

Marketers implement inductive reasoning all the time. They need it to make conclusions about the target audience, its pain points, and its behavior. For example:

Jim is a 45-year-old customer who buys new wireless headphones every year. By itself, this information seems random. However, you can record it as an indication of customer behavior.

Next, you notice that Todd, Ben, and Sarah are also 45-year-old customers who buy new wireless headphones yearly. This is already a pattern of behavior.

Finally, you can conclude that 45 years-old customers are likely to buy new wireless headphones every year. 

While this doesn't mean that all 45-year-old customers do this, it could be valuable data when coupled with other information about your audience's behavior and demographics. Based on this information, you can structure your sales and marketing tactics.

Several types of inductive reasoning exist. Let's take a closer look at five types we can apply to customer research, sales, and marketing.

Generalization

Generalization is the most common type of inductive reasoning that marketers and researchers use. Similar to the above examples, you make general conclusions based on recurring patterns. 

To develop a hypothesis, you must observe several instances of something happening and find common qualities.

Example

This type of inductive reasoning is useful in surveys and polls. You can analyze trends and behaviors in a specific group of customers to draw conclusions about your entire target audience.

However, more than one poll or survey may be required to develop an effective marketing or sales strategy. You may need to run several surveys for different audience segments to make sure you aren't making erroneous conclusions.

Researchers (just like marketers) need to survey different audience segments when conducting scientific studies.

For example, studying how running affects someone’s health may show different results for younger and older audiences. Younger people are likely to note better health and faster weight loss, while older people could have concerns about knee pain.

Causal reasoning

Unlike generalization, causal reasoning doesn't require patterns to make conclusions. It depends on the cause-effect relationship.

Example

When you bring flowers home, your family members start sneezing. You conclude that flowers cause allergies.

While helpful, causal reasoning comes with several downsides. You may come to the wrong conclusions due to the lack of information. For example, your family could be sneezing because they all caught the flu, and the flowers aren't responsible.

In marketing, you can take advantage of causal reasoning while A/B testing a new product design or digital ad content. When you have sufficient information, you can see what works and what doesn't.

Sign reasoning

Sign reasoning doesn't require a cause-and-effect relationship to make a conclusion. You draw a conclusion based on specific events occurring together.

Example

When December comes, it gets cold. By itself, December doesn't cause temperatures to drop. However, when this month comes, cold weather is highly likely.

Winter comes because the earth rotates and tilts. However, looking at the outdoor thermometer is much easier than analyzing the earth's rotation patterns. That's called using signs for inductive reasoning.

When studying customer behavior, sign reasoning can be helpful in identifying product use patterns.

When summer comes, children eat more ice cream because it's hotter outside, and they have more free time. You don't need to gather all this information to build a marketing strategy. It’s sufficient to connect summer and higher ice cream demand.

Analogical reasoning

Analogical inductive reasoning involves comparing two entities, situations, or groups. If they have numerous similar qualities, it's possible to conclude that other similarities are possible.

Example

Dogs have hair, warm blood, four-chambered hearts, and complex brains like humans. So most likely, dogs are mammals who feed their young with milk.

The toughest part about analogical reasoning is ensuring that the two objects you are comparing are similar enough to make new inferences.

When studying customer behavior, analogical reasoning can be highly useful since you don't need to compare DNA or conduct complex studies. You can compare demographics and pain points. If they are similar, you can figure out buying behavior.

Statistical reasoning

Statistical reasoning is drawing conclusions based on statistical data.

Example

Last year, 10% of your customers positively responded to upselling opportunities. You can count on 10% of your target audience responding well to upselling.

This information can help you plan your budget and revenue. As you convert more customers, you can expect more upselling opportunities.

Just like the other inductive reasoning types, statistical reasoning has downsides. Theories based on just one population segment aren't always accurate, especially if it's small.

Inductive reasoning is the opposite of deductive reasoning. With deductive reasoning, you use general information to make conclusions about a specific situation.

• All dogs bark at trees

• Jasper is a dog

• Jasper barks at trees

Deductive reasoning can be just as valuable for marketing as inductive reasoning. For example, after studying your customers, you found that 30-year-olds are pressed for time and don't spend more than five minutes choosing a product on your website.

You can convert new customers by offering a transparent comparison (e.g., infographic) of your products and the competition's offers that potential customers can evaluate within five minutes.  

This type of reasoning is excellent for creating successful marketing strategies. Meanwhile, inductive reasoning can help understand customer behavior and adjust your offers and services.

The conclusions you reach by implementing deductive reasoning are correct if the assertions are true. That's because you base your conclusions on premises. However, a conclusion based on inductive reasoning has to go beyond the initial information. Accuracy isn't always satisfactory.

Comparison with examples

Deductive reasoning:

• All white cats purr

• Dolly is a white cat

• Dolly purrs (100% true)

Inductive reasoning:

• Dolly is a white cat

• Dolly purrs

• Luna is a white cat

• Luna purrs

• All white cats purr (may not be true)

When it comes to customer behavior, the more information you can gather, the more likely you will achieve top results with inductive and deductive reasoning.

Unlike scientific research, marketing and customer behavior exploration doesn't need extreme precision and decade-long studies. You can easily implement different types of inductive reasoning by using the information you collect about your existing customers.

With inductive reasoning, you can use your customer data to draw conclusions about your target audience. This can help maximize conversions. Meanwhile, you can use the information you gather to improve existing offers and increase conversion rates.