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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

[Cartoon of a construction crane in front of a new building holding three containers labeled “recommendation,” “reasons and general principles,” and “evidence.” Additional text reads: 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.

Process Diagram showing that the first step towards acknoweldgement and responses is a Claim, followed by Reasons and finally followed by Evidence

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 more effectively analyze large amounts of customer data to improve sales forecasts.
Evidence
Salesforce Research surveyed over 3,100 global sales professionals, and found that high-performing sales teams are 10.5x more likely than underperformers to experience a major positive impact on forecast accuracy when using intelligent capabilities.

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. For a more entertaining example, check out the Storks Deliver Babies study in the resource section.
  • 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
  • Satisfaction
  • Trust/commitment
  • Identity
  • Consumption goals
  • Resources
  • Perceived costs/benefits
  • Valence
  • Form modality
  • Scope
  • Nature of impact
  • Customer goals
Customer
  • Cognitive
  • Attitudinal
  • Emotional
  • Physical/time
  • Identity
Firm-Based
  • Brand characteristics
  • Firm reputation
  • Firm size/diversification
  • Firm information usage and processes
  • Industry
Firm
  • Financial
  • Reputational
  • Regulatory
  • Competitive
  • Employee
  • Product
Context-Based
  • Competitive factors
  • PEST
    • Political
    • Economic/environmental
    • Social
    • Technological
Other
  • Consumer welfare
  • Economic surplus
  • Social surplus
  • Regulation
  • Cross-brand
  • Cross-customer

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


  1. Use only reputable/trustworthy sources of information.
  2. Make sure the evidence is relevant to the argument/recommendation.
    • For example, if you’re looking to implement Chatter in a hospital, make sure to provide evidence of benefits achieved at medical care facilities that have adopted it.
  3. Provide quantitative or qualitative support, such as survey results, customers examples, etc.
    • Tip 1: Include comparisons with your quantitative information to help readers assess the level of benefits.
    • Tip 2: Include some qualitative information to bring the human element to life, such as customer quotes. Audience members that don’t really like numbers may thank you.
  4. Include some type of financial return-on-investment support for recommendations involving spending.
  5. For arguments/recommendations in which the benefits are mostly competitive parity or compliance with industry standards, it can be helpful to provide evidence that illustrates the risks of not accepting the arguments/recommendations.

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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?).
  5. 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

retargeting