You’re tasked with building and launching a new mobile app. It’s important that it gets done soon and on time so you can time it with the launch of a large-scale campaign.

You contact three agencies for quotes. The price and quality are similar, and you receive these time estimates:

• Agency A: “6 months” [= 182.5 days]

• Agency B: “27 weeks” [= 189 days]

• Agency C: “195 days”

Chances are, you will probably choose Agency C, even though they say they’ll take the longest. Why?

Use smaller units to make your statement more credible

Impacted metrics: Customer acquisition
Channels: Ads | Packaging | Website | Sales team | Customer service | Marketing communications

Tip type: Existing research (October 2011)
Previous tip: When to use AI vs humans to give product recommendations (All tips here)

Recommendation

If you want your statement or estimate to be perceived as more credible, use a smaller unit (e.g. the battery lasts ‘180 minutes’ vs ‘3 hours’, we’ll deliver the project in ‘96 hours’ vs ‘4 days’). People will be more likely to choose your product or service over a similar one that is less credible.

The effect mostly applies to time estimates, but you can try testing it in other situations (e.g. ‘34 happy clients’ vs ‘dozens of happy clients’).

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Effects

• Expressing quantities in smaller units (e.g. 30 days vs 1 month) makes the statement seem more precise and credible.

• For example, people expected 46% less delay when a construction project was said to take “52 weeks” rather than “1 year”.

• The impact changes based on context and expectations. For example, we expect claimed battery life to be an exaggeration, so smaller units will make it seem less so. On the other hand, construction project timelines tend to be optimistic, so smaller units will reduce how late we expect them to be.

• The effect disappears if the receiver thinks the communicator isn’t:

  • Knowledgable enough to give that level of precision (e.g. an online rumor about when a videogame will be released)

  • Trustworthy (e.g. the company has a known history of fraud)

• For a similar effect on pricing, see this related tip about price precision.

Why it works

• Using larger units when smaller ones could be used (“a day” vs “24 hours”) is an unspoken signal we give thath the statement should be taken with a "grain of salt".

• Vice versa, we assume that if a communicator uses small units, it means they are very confident of their estimate and we believe them as long as we consider them sufficiently knowedgable and trustworthy.

Limitations

• It’s likely that at some point units become so fine-grained that the communicator loses credibility and or it becomes hard to interpret and the effect backfires (e.g. our battery life is up to “7200 seconds” [vs “2 hours”]). This wasn’t tested and it’s unclear when it happens.

• The effect may impact people with varying experience levels differently. For example, someone who has never renovated a house may have different expectations and knowledge compared to a professional developer. We don’t know if that impacts the strength of the effect.

Companies using this

• Companies and professionals seem to instinctively use follow this principle in some limited situations, when they are confident of their statement (e.g. delivery in “48 hours” [vs “2 days”], turnaround in 24 hours).

• Very few companies seem to purposely use smaller units to make their statements appear more credible.

Steps to implement

• Think of statements you make about your product (e.g. battery life, durability), estimates you provide (e.g. delivery times, when an issue will be resolved) or proposals you make to your clients (e.g. the implementation time of a new IT system) that could be made more precise with smaller units.

• Switch to using smaller units (within reason) if you are confident you can deliver on your promise (most of the time). If you are not confident you can deliver, you may be better off using large units to avoid displeasing your customers.

Study type

Online and lab experiments, United States

Source

Zhang, Y. C., & Schwarz, N. (October 2011). How and why 1 year differs from 365 days: A conversational logic analysis of inferences from the granularity of quantitative expressions. Journal of Consumer Research, 39(2), 248-259.

[Link to paper]

Affiliations

Ross School of Business, University of Michigan. United States

Remember: This research could be disproven in the future (although this is rare). It also may not be generalizable to your situation. If it’s a risky change, always test it on a small scale before rolling it out widely.

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