Ce descoperi când analizezi 40 de milioane de mail-uri

O companie pe nume Boomerang a analizat în bulk cam cinci milioane de mail-uri și au descoperit ceea ce știam dar cel puțin putem să cuantificăm acum:

De aici, aici și aici (site-ul oficial)

• Email-ul într-adevar mănâncă tot timpul, mai precis cam 2 ore pe zi răspunzând la 12 mail-uri serioase care necesită multă muncă 

• E bine să trimiți mail-uri dimineața înainte de ora șapte 

• E bine să scrii ca la clasa a 3-a sau „explain it to me like I’m five”

„Emails written at a third-grade reading level were optimal.” Short words and short sentences win, getting a 53% response rate. This compares to a similar 46% for kindergarten level, and 45% for high-school level, but most depressing is that emails written at college level get the worst response—just 39%.

• E bine să pui întrebări – dar nu prea multe (2-3 ajung) – și să fii subiectiv

„We found that emails that asked 1-3 questions are 50% more likely to get a response than emails asking no questions. But a bombardment of questions won’t help you either – an email with 3 questions is 20% more likely to get a response than an email with 8 or more! The more opinionated the content of the email, the higher the response rate climbed. One caveat – we have no idea if those subjective emails generated positive responses or declarations of war, so caveat writer!”

• Cele mai eficiente mail-uri sunt scurte (125 cuvinte) și neutre

Keep it short—125 words is the optimum length—but don’t worry too much if you need to go over that, because response rates don’t really drop until you reach 2,000 words, by which time your recipient is too busy sleeping to hit the reply button. Keep your emails easy to read and easy to reply to, and don’t scare anybody off by being too enthusiastic (or too negative)

„Hey, I’d definitely like to get together next week. Do you want to get pizza?”

0.35 positive sentiment  is the most effective—just a little positive, but not too pushy. For contrast, here’s the perfect negative subject line: „I had an awful experience at your store today. The clerk was very rude. Please do something to make it right.” (Negative 0.35 sentiment)

E bine să nu fii un spammer care vrea să facă schimb de linkuri sau să-și promoveze ceva social media event bullshit se pare 🙂 :

De asemenea interesant despre cum se mai analizează online sentiment

„Most word processors don’t support sentiment analysis yet, and online tools report wildly varying results for the same content.

The reason is that most sentiment analyzers work by using machine learning techniques to train algorithms  which words are positive or negative, based on whatever training data their authors could track down. The training data matters a lot – one common source is movie reviews, since they come with a numerical score that can be used as a proxy for how positive the review is likely to be. This data is all noisy, and because machine learning generates incredibly complicated algorithms, it’s hard to calibrate after the fact.”