Use Information to Make Better Business Decisions
Learning Objectives
After completing this unit, you’ll be able to:
- Structure effective arguments and recommendations.
- Develop defensible reasons and relationships.
- Apply best practices to increase evidence effectiveness.
Structure Arguments and Recommendations for Effectiveness
Often, the information we use in organizations needs to be in the form of an argument or recommendation. One way to do this is to think in terms of claims that are supported by solid reasoning and evidence. We also need to proactively acknowledge and respond to likely counterarguments. Inspired by the work of Booth, Colomb, and Williams (2008), this scheme is depicted in the following illustration.
For example, if we want to recommend adopting artificial intelligence (AI) to improve our sales operations, then our argument might look something like this:
Recommendation/Claim |
We recommend adding AI to our CRM system to improve sales outcomes. |
---|---|
Reason |
Intelligent systems can improve sales data quality, help sales teams understand customer needs, and create highly personalized experiences. |
Evidence |
Salesforce Research surveyed over 5,500 global sales professionals, and found that 83% of sales teams with AI grew revenue last year, compared to 66% of teams without it. They cite these reasons as their competitive advantage. |
In the example above, the recommendation looks promising, although it could be stronger with more evidence from additional sources, and with more clarity on the positive impacts.
In organizational settings, it’s also common for people to use general principles to support their claims. Preferably, these general principles are based on research and years of evidence, rather than popular myth or folklore. For example:
Recommendation/Claim |
We should reduce our inventory of home improvement items. |
---|---|
General Principle |
As economic conditions soften, home building declines. |
Evidence |
Several top economists are predicting the economy could soften over the next 24 months. |
In the example above, we are left to assess whether the general principle is valid and applicable, and if the evidence is valid. It is suggested that sales of home improvement items are likely to decline as economic conditions soften, but no support for the relationship is provided, and the evidence is pretty squishy (i.e., what does “could soften” mean, anyway?). In other words, this may be a great recommendation, but further discussion is needed.
The examples above are intended to illustrate how to structure arguments and recommendations that work. Ideally, we will provide more than one reason supporting our argument/recommendation, and provide more than one piece of evidence for each reason.
Provide Defensible Reasons and General Principles
Reasons and general principles provide our audience with a cause to accept our claim, and set the stage for our evidence. However, in organizational settings, it’s not hard to find people relying on spurious reasoning, often with comical or even disastrous results. The good news is that there are some basic principles that can help us find solid reasons and general principles. Let’s look at a select few…
- Causation: In business, it’s common to find reasons and general principles based on associations (i.e., correlations). While this can be fine for some relationships, it’s better to use causal relationships. For example, a firm found the following association: people who visited their website and downloaded a whitepaper were 75% more likely to make a purchase. This sounds like a good reason for having a high-quality whitepaper available. However, downloading the whitepaper didn’t cause the people to purchase. So, using it as a reason to support initiatives to create more downloads should be questioned. It might be better to focus on what’s causing people to purchase, such as their needs and wants.
- Algorithms: We love our algorithms! They help us map our commute, predict the weather, select songs to stream, and even identify which customers to help first. In this era of big data, algorithms and AI are quickly becoming embedded in our lives. It’s truly exciting. But have you also noticed that some algorithms just aren’t that great (yet)? Perhaps our navigation system occasionally sends us the long way, or the new smart scheduling system at work is wreaking havoc. It could be the programming, the data, or a host of other factors. The point is, before we accept the output of an algorithm, it’s a good idea to review it to make sure it makes sense—especially if we’re going to use it as support in our arguments and recommendations.
- Models: In real life, there’s often more than one factor involved in an outcome. Even if you don’t use all the factors in your arguments, it helps to be knowledgeable of the other factors—especially if you expect some counterarguments to your reasoning. To find research backed factors, and how they influence outcomes, hop on Google Scholar, and search for “model for <insert your topic: innovation, customer satisfaction, user adoption, etc.>.” For example, the table below summarizes the factors examined by one study that focused on customer engagement behaviors and outcomes—that’s quite a list of factors to consider!
Antecedents |
Customer Engagement Behavior |
Consequences |
---|---|---|
Customer-Based
|
|
Customer
|
Firm-Based
|
|
Firm
|
Context-Based
|
|
Other
|
Source: van Doorn, et al (2010)
Mind Your Evidence
The evidence we need can vary greatly depending on the significance and gravity of the situation. In addition to using the dimensions and characteristics of IQ to assess our evidence, the following short-list of best practices can help:
Evidence Best Practice |
---|
|
Test Arguments Using These Questions
Once we have our argument/recommendation constructed, test it out before going live. To help with this, here are some common questions to ask:
- Are there other plausible options? For example, if a recommendation is made to adopt a new email marketing scheme to increase sales, then it would be reasonable for audience members to ask if there are other options for increasing sales that might work better.
- Does the reasoning and evidence make sense? Sometimes the reasoning and evidence look good by themselves, but they really don’t support the claim. Make sure there’s a solid connection.
- Does the reasoning and evidence apply? Sticking with the email marketing scheme example, it would be reasonable to expect someone to ask for evidence gathered in a similar organizational context.
- What are some counterexamples? Even if the evidence looks great, there are probably counterexamples that don’t fit the claim. Do these counterexamples render the claim useless, or are there reasons why the claims are still usable (i.e., can you live with the counterexamples?).
- Is everyone in agreement on the definitions? For example, what does it mean to “increase sales”? In other words, what exactly is going to increase? Revenue? Units Sold? Something else?
Resources