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📝 Intro

The first review that we read about a product has a strong impact on our perceptions of it.

Add to this that most of us read only a handful of reviews before deciding whether to buy or move on, and the first few reviews become crucial.

Some companies with large troves of data (e.g. Amazon, Google) already know which types of reviews they should show first.

Now, thanks to this research, we know too.

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Carefully choose which review you show first to boost sales

Impacted metrics: Customer acquisition
Channels: Reviews | Website/App | Marketing communications
For: B2C
Tip type: New research (May 2021)

📈 Recommendation

Decide carefully which reviews to show first on your product page (manually or through an algorithm), don’t simply show the most recent ones first.

Try to show a first review that’s positive and provides useful information (e.g. additional details about the product, who the product is ideal for).

People will be much more likely to buy.

🎓 Effects

• The first review shown on a product page has a strong impact on how likely people are to buy. 

• An analysis of reviews and sales on a major UK online retailer found that:

  • When the first review shown was a five-star review (vs a one-star review), purchase probability increased by between 0.88% and 2.72%, from an average conversion of 3.25% to 3.88% (depending on the data analysis model used)

  • The same review in position two only increased purchase probability by 0.50%

  • Reviews were least effective when in positions three or four (our of five shown on the first page)

  • These effects were independent of the average product’s rating

• An individual review displayed at the top of other reviews mattered the most when it:

  • Had a high rating but the average rating of the product was low

  • Was for a low-priced product

  • Provided extra information that was missing on the product page (e.g. the duvet cover has buttons that are easy to open and close)

  • Explained who the product is for, and who it isn’t for (e.g. great quality headphones but fall out often if you use them for running)

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🧠 Why it works

• We generally read fewer than 10 reviews for a product before forming an opinion, usually much fewer.

• So we are particularly influenced by the first reviews that we read, typically those that are most clearly shown on the product’s page.

✋ Limitations

• This analysis is based on a website that shows reviews in the order of most recent first. It’s likely that people quickly get used to the format used on a website they visit. So if a website always shows five-star reviews first, the effect may be weaker.

• The research is based on the analysis of a single dataset and didn’t do an in-depth text analysis of what is considered an ‘informative’ review (for example, it used review length as an indicator). This means that while conclusions about the effectiveness of the first review’s star rating are solid, those about its content are weaker.

🏢 Companies using this

• It’s common to find retailers that still display reviews by most recent (e.g. Gap, John Lewis, MediaMarkt).

• However, the trend is shifting towards using algorithms that show the most ‘relevant’ reviews first (e.g. Yelp, Airbnb), or even aggregate and extract highlights from specific reviews (e.g. Google’s review snippets).

⚡ Steps to implement

• You can develop a simple algorithm to optimize your product page’s review section, for example:

  • Avoid showing one or two-star reviews as the first review (unless the product truly has terrible ratings, in which case you should probably stop selling it)

  • Have a voting system so others can vote for reviews they find helpful, and show those higher up

  • Long reviews can be an indicator of additional ‘points’ for the algorithm since they likely contain more useful information

• Alternatively, manually select one or a few positive and informative reviews and ‘pin’ them at the top or showcase them separately on your product page.

🔍 Study type

Market observation (analysis of 380,450 online review impressions in February - March 2015 of technology and home and garden products of a major UK retailer, undisclosed). United Kingdom

📖 Research

Vana, P., & Lambrecht, A. (May 2021). The effect of individual online reviews on purchase likelihood. Marketing Science.

[Link to paper]

🏫 Affiliations

Tuck School of Business and London Business School. United States and United Kingdom

Remember: Because of the groundbreaking nature of this paper, it 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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