Pepsi Challenge & New Coke

Why data can be correct yet misleading.

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In 1980s Coke started losing market share to Pepsi. In supermarkets Pepsi’s share surged passed Coke’s by 2%. That’s $1 BN in sales.

The ‘Pepsi Challenge’ was considered to be the primary reason for this turn of events.

2/
Pepsi went to malls around the country and invited people for blind taste test between Coke and Pepsi. The results were surprising.

People picked Pepsi over Coke by a significant margin.

This became the basis of the Pepsi Challenge ad campaign.

3/
Coke had twice as many vending machine, dominated fountain, had more shelf space, spent more on advertising — the consensus was there was no reason why Coke should be losing share.

Except taste.

4/
Pepsi was the sweeter product & this was the reason for the consumer preference in taste tests.

But consumers don’t sip cola. They gulp it in huge quantities. The preference in “testing environment” is not same as a better product.

Pepsi now knew this. Coke didn’t

5/
Coke began thinking the unthinkable — changing the formulation. Coke developed a sweeter receipt which showed significant promise in similar sip tests.

Coke management (Don Keough, no less) was justifiable elated.

6/
Coke ignore the focused group data where most groups came to the conclusion that they do not want any tampering with an iconic brand with which they had positive affiliations for decades.

Quantitative data won over qualitative insights.

7/
New Coke was a disaster. The consumer backlash was unprecedented.

Coke did one thing right in this whole episode. They had a contingency plan. They had a marketing plan ready to bring back the old Coke. It was a resounding success.

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Lessons:

1. Correctly define the problem: Coke’s problem was lazy advertising, losing young consumers to Pepsi’s “cooler” ads.

2. How you ask a question matter: Sip test is no proxy for real world usage

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3. Over-reliance on data: “Numbers represent a surface facade of a complex process.

Beneath lies a chain of issues that impact the these numbers-problem definition, research design, how questions are asked, and even the analytic skills of those who interpret them.”

10/10
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