Dont like recommend

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marketing

Don't like me. Recommend me.

Word-of-mouth may be the most valuable form of promotion, but not all shout-outs are created equal.

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There are two tactics in the modern marketing playbook that are so ubiquitous most consumers barely notice them. The first is the application of the Net Promoter Score (NPS), Fred Reichheld’s invaluable measure of loyalty through one simple question:

On a scale of zero to 10, how likely would you be to recommend this brand to a friend?

Many companies measure NPS continuously, sending a quick survey to customers within 24 hours of a purchase or significant brand interaction. The reason: the score is a reliable bellwether of customer satisfaction. Satisfied customers recommend brands to others. NPS safeguards your customer relationship and creates an opportunity for invaluable word-of-mouth (WOM) marketing.

The second ubiquitous tactic is closely related to NPS. It’s the invitation for customers to write a review about their experience. Recognizing that WOM is the most powerful promotional device, businesses big and small are encouraging their customers to blurb their brands on social media, to write reviews on Amazon, and to Yelp freely.

A growing body of research supports the validity of these tactics. Earlier this year, Kent Grayson and Mathew Isaac published a compelling study in the Journal of Consumer Research that demonstrated how reviews activate a consumer’s persuasion knowledge in a beneficial way. The research was surprising because activating persuasion knowledge is usually viewed as a detriment to closing a sale. When we are aware of a seller’s persuasive motives, our skepticism usually rises. Grayson and Isaac found the opposite to be true in the context of reviews. While a carefully placed review within a sales pitch made prospects more aware of the seller’s attempt to persuade them, the tactic was generally viewed positively—as an objective benefit that bolstered the persuasiveness of the pitch overall.

The digital giants of electronic commerce have known this for years. That’s why reviews are such a prominent component of the Amazon and Netflix user experiences. Reviews lead to better conversion. Period. End of sentence.

Yet not all reviews are created equal. A new study published this month in the Journal of Marketing Research revealed that words really do matter, and that the specific language used by a reviewer has a big impact on the review’s influence. For example, a review that says, “I recommend this movie” is a lot more effective than a review that says, “I really enjoyed this movie.” While the degree of difference between these two examples may seem trivial, the data suggests that the former endorsement is the one that closes a sale, while the other might merely increase sentiment.

But the study didn’t stop there. It revealed another surprising dimension, one that is actually a little depressing. Novice reviewers are much more likely than their more experienced counterparts to use words like “recommend.” As experience goes up, the tendency to explicitly recommend goes down, and vice versa. Think about that for a moment. The people who are best qualified to guide you are the ones who may be using the least influential language. And the people who are less experienced than you may be the ones most influencing your decision.

There’s an old adage in the wine industry: Buy with apples, sell with cheese. Apples reveal a wine’s imperfections and cheese softens them. I close this edition of The Findings Report with an analogous rule of thumb. As a consumer, be wary of strong recommendations. As a marketer, encourage them.

As a consumer, your spidey sense should elevate when you read a review with a glowing “recommendation.” That’s your cue to investigate the qualifications of the source. As a marketer, stop asking customers to merely “Like us on Facebook.” Instead, start asking them to “recommend” your product or service on their favorite social channel. Be specific.

Actually, the two opposing motives need not exist in conflict. Marketers can steer customers to strong reviews while also providing helpful context. After all, it does no service to the marketer if prominently placed recommendations by novices lead a customer to a bad experience. Several e-commerce businesses now cluster summaries of reviews together, using machine learning that matches customer tastes and experience with relevant reviews. They feature reviews under headings such as “people who love Cabernet also liked…” to guide customers to the best fit. Many are also adding semantic cues to help customers assess the knowledge and credibility of the reviewer based on criteria such as how long they’ve been a customer, number of reviews written, etc.

I strongly recommend that you put these ideas into practice, and not just because I like them. Now, parse that advice for what it’s worth.

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