Timing Is (Almost) Everything

In comedy, they saying that timing is everything. In social media, if not everything, it is something that needs serious consideration.

You can find many recommendations for when to post online, but the problem is that they are generalizations. The real answers about when to post need to be specific to your audience.

In real estate, they say location matters. That is also true for social media.

A restaurant in almost any city draws its customers from the local area. If you are in Washington D.C., posting for that time zone and around the times when people are apt to be looking for dining suggestions (Are you a breakfast or dinner place?) is optimal. A restaurant in San Francisco needs other posting times.

If your business has wider national or international reach, you may need a strategy that includes multiple accounts, such as Twitter handles, for each region.

How well do you know your audience? Questions to consider: What time are people waking up? Are they accessing your resources during work hours, evenings or weekends?

There are many free and pay tools to help you find the best time to post, such as Audiense,  and using an auto-scheduler dashboard (such as Hootsuite) then allows you to schedule social media times based on when they have performed the best.

Hootsuite has recommended Best Times to Post on the big 3: Facebook, Twitter, and Instagram.

Facebook is interesting for timing. One thing you might not consider at first is that  75 percent of your Facebook post’s engagement will happen within the first five hours and 75 percent of your post’s lifetime impressions are reached after just two and a half hours. These posts do not have a long shelf life or “legs”

The “half-life” of a Tweet is said to be only 24 minutes and Tweets reach that 75 percent mark in less than three hours.

You will find online many recommendations for specific networks. For example, for The Huffington Post , the recommendations for maximum retweets is to post at 5 p.m. and 12 p.m., and the best days for business-to-business organizations is, not surprisingly, Monday through Friday, but for business-to-consumer it’s the weekends and Wednesdays.

Takeaway: Know your audience’s social media habits and customize to that profile for each network.

 

Infographic via Kissmetrics, a behavioral analytics and engagement platform
built for marketers and product teams.

Crunching the Data When Your Post Goes Viral

Singer-songwriter Marian Call wanted to write about our changing relationship to work. She sent out a quick tweet to her followers asking what their first jobs had been before she went to sleep.

What were your first 7 jobs?   Babysitting, janitorial, slinging coffee, yard work, writing radio news, voice-overs, data entry/secretarial   — Marian Call (@mariancall) August 5, 2016

Call woke up to find her tweet had gone viral and she got replies from many people including some celebs like Buzz Aldrin (Dish washer, Camp counselor, Fighter pilot, Astronaut, Commandant, Speaker, Author) and Sheryl Sandberg (1. Babysitter -twice – Office receptionist, Salesperson in clothing store, Aerobics instructor, World Bank health team, Children’s Defense Fund).

Marian did not use a hashtag but tag emerged and that made it easier to see responses. Unfortunately, different versions were used and are still active, like #firstsevenjob or #firstsevenjobs and #first7jobs. And the query and tags also appeared in other networks like Facebook.

She was interviewed on the Make Me Smart podcast  and she explained that then needed a way to to crunch the data from all the Twitter responses.  She was contacted by the social product manager at IBM who had heard her interviewed and they put the data into their Watson supercomputer and then were able to produce an infographic of the data.

infographic

The data shown is interesting and shows commonalities across the world. Of course, we can’t manipulate the data or request other queries. Call said she would have preferred a spreadsheet she could sort and search.

This little exercise points out one flaw with Twitter and many other social sites – no easy way to pull user data and draw conclusions about it. There are paid programs and people who can do those things for you, but a free, built-in way to do those two tasks is not reality.  Most of our posts will not go viral, but even gathering the data from a normal social media campaign can be difficult.

Marian’s experience did get others to try their hand at the task without a supercomputer. One example is at blog.monkeylearn.com/analyzing-first7jobs-tweets-monkeylearn-r/