It turns out that this is also very much a cultural issue with the expectation being that people who are not morning people are somehow slackers. I didn’t think about it in those terms, probably because I tend to function better in the mornings (at least, once I’ve taken my meds).
On a related note, it also turns out that so-called “coffee naps” are great ways to recharge during the day. I tend to nap for around 20 minutes and will do that if I have an opportunity because it works well for me.
Apparently, a cup of coffee right before a 20 minute nap could be just the thing you need to recharge and return to a much more productive state. According to Vox:
It’s counterintuitive, but scientists agree that drinking coffee before napping will give you a stronger boost of energy than either coffee or napping alone. To understand a coffee nap, you have to understand how caffeine affects you. After it’s absorbed through your small intestine and passes into your bloodstream, it crosses into your brain. There, it fits into receptors that are normally filled by a similarly shaped molecule called adenosine. Adenosine is a byproduct of brain activity, and when it accumulates at high enough levels, it plugs into these receptors and makes you feel tired. But with the caffeine blocking the receptors, it’s unable to do so. Here’s the trick of the coffee nap: sleeping naturally clears adenosine from the brain. So if you nap for those 20 minutes, you’ll reduce your levels of adenosine just in time for the caffeine to kick in. The caffeine will have less adenosine to compete with, and will thereby be even more effective in making you alert.
So, if you’re ever accused of being lazy or slacking off because you’re not a morning person or because you just want to have a quick nap and recharge, there is a body of science backing you up!
Many people are almost fanatical about their “perfect diet”, so much so that you’d think diets are new religions and we’ll one day see wars fought over disputes about carbs. Jabs at some of these diets aside, it turns out there is no perfect diet for humans.
Professor Eran Segal gave a talk at TEDxRuppin in July about research he and his team have been conducting at the Weizman Institute here in Israel into this notion of good diets and bad diets. As he pointed out, dieticians tend to work on the basis of standardised dietary information and recommendations to advise their patients.
It turns out that you can’t apply a standard model to everyone because our microbiomes are so different. What works for one person, puts another person at risk. So, perhaps, diets like Banting are good for some people because their bodies are compatible with the diet and terrible for others because they aren’t.
On a side note, when I think about Israeli hi-tech I’m proud of, this is a great example of how Israeli innovators are doing work that could change the world for the better.
As a diabetic, this kind of research is really interesting to me in a very personal way. I am still learning which foods tend to aggravate my diabetes and which help me manage it better. I can just imagine how beneficial a completely personalised dietary recommendation would be based on my unique biology.
The case against sugar: Science is not definitive and will likely never be. So read the evidence, judge for yourself https://t.co/66pe64JbB8
Instapaper has become so much more than a reading app to me. It has become a fantastic research tool too. As good as it is, it could be better (or, at least, it could do one thing better). Bear with me, I’ll explain.
As you may know, Instapaper introduced highlighting and commenting (or Notes) some time back. If you are using the “free” service you are limited to just 5 notes a month but if you become a Premium user for a mere $29.99 for a year (or $2.99 for a month), you have all the awesomeness that is Instapaper available to you.
My work involves a fair amount of writing. I typically write around 3,000 to 4,000 words a week in the form of blog posts for my employer’s blog. I also write guest posts for industry websites now and then and edit blog posts that my colleagues have written. My writing tool is Byword and I am a big fan. Reaching the point where I write those articles usually means doing a fair amount of research. That involves finding useful materials, saving them to Instapaper where possible and later going through my saved items in Instapaper to review them more closely.
At that point I use the highlighting and commenting tools a lot to pick out phrases and ideas that I want to incorporate into my articles. I created IFTTT recipes (here and here) that take highlights and comments and add them to running Evernote notes for each article.
The results are nicely laid out Evernote notes with all my highlighted texts and comments. It’s a useful way to aggregate all those highlights and comments in a central reference that I can go back to when it is time to write my article.
Instapaper has a Notes tab which has a list of all your highlights and comments but I haven’t used that. I’ve been using Evernote for years so it seemed like a good idea to just send my highlights and comments there rather than use the Instapaper option.
At the end of my research phase I had a dozen or two Evernote notes with dozens of items in each which I thought would be useful in my article. What hit me is how relatively unproductive this workflow is where I have a lot of material to review after the initial research. Making all those notes and highlights practically useful requires me to go through my Evernote notes and manually extract all of those items into some sort of outline of my article. My favourite outliner is OmniOutliner but any OPML-based outliner would work just about as well.
Usually I don’t use an outliner too because my articles aren’t generally as complex as this ad blocking piece. In this case, an outliner became essential. I was working on my outline and I realised that as terrific Instapaper is as a research tool, being able to automatically export all those highlights and notes into an outline that I could manipulate afterwards would be far more effective than flat Evernote notes.
The benefit of an outliner is that I can drag lines around and re-order the outline pretty easily. I could possibly even create an initial draft of the article in the outliner and finish it off in Byword or another word processor. It would really depend on how I structured my outline and how much of the article I’d want to write in it. In this case, I still did my writing in Byword but I split my screen and placed my outline on one side of my screen as a reference and wrote in the Byword window.
An alternative to this option is to just use Scrivener which is an excellent writing app. I started my article in Scrivener because it has an outlier function and the capacity to collect research materials in the app itself but I switched back to my Byword/OmniOutliner combo option – I just felt this strong need to stick with plain text in a simpler writing window.
Because my outline was more of a secondary outline after I finished my initial research, I still had to go back to Evernote to find individual quotes and arguments and combine material from both sources into my article. If I had been able to automatically send highlights and comments straight into an outliner, it would have placed all my reference materials into one outline from the start and made it a lot easier to structure that data for reference when I started writing.
So, my wish list for 2016 (I’m putting this out into the ether in case it is possible to make this a reality) is for some option to automatically export highlights and comments into coherent outlines just as I can create a similar workflow for Evernote. One possible solution is to create an integration with Dave Winer’sFargo.io outliner. It should be something simple and create an OPML file that you can manipulate later to create the basis of an article or similar document.
As marketers we think that we are “engaging” with our “target market” when we run “campaigns” on various social media platforms. We point to various indicators of our successes which range from social signals like retweets, Likes, +1s and comments of various sorts along with some sort of secondary success metric. The industry has come a long way since the early days of just picking a bouquet of social media services and pushing messages, hoping for the best … or have they? Why do brands fail to engage meaningfully even when they think they do?
The power of the social Web is nicely encapsulated by this old nugget from The Cluetrain Manifesto. You have probably come across it somewhere along the way even if you haven’t read the book:
Markets are conversations
That isn’t just some platitude (or maybe it is), it underpins social marketing, sales and advertising. At least it should. What seems to be happening is that marketers are slipping back into old habits. They are just pushing marketing messages across the wires to “target markets” or “target demographics” thinking their campaigns are more engaging and effective simply because they are using a social network to get their message across.
Brands like to talk about how well they know what their consumers want. But the truth is, they’re barely scratching the surface. That’s the big takeaway from a wide-ranging new study by IBM and Econsultancy, which found that brands are just not delivering the level of customer experience that they think they are.
The study consisted of two surveys — one for brands and the other for consumers — and found a gap with a real business impact. “The biggest takeaway was the disconnect between how marketers perceive the job they’re doing and how consumers perceive that job,” said Jay Henderson, director, product strategy at IBM Commerce.
The data is very interesting. It highlights brands’ perceptions that they provide a “really good user experience”; provide relevant content across consumers’ preferred challenges and generally understand consumers’ desires. Consumers don’t agree, and markedly so:
Consumer responses about external communications from brands, however, found that in general nobody’s impressed. Only 35 percent of respondents said their favorite companies sent “usually relevant” emails or messages. And for companies that weren’t necessarily already a favorite, that number dropped even more — only 21 percent said those messages are “usually relevant.”
IBM’s Jay Henderson told DigiDay that the problem lies not in insufficient data but too much data. Brands don’t seem to be using this data effectively enough to derive accurate insights about consumers and then engage with them meaningfully. I wonder if the problem isn’t an exaggerated emphasis on data itself.
On one hand, data enables marketers to develop very precise and relevant marketing campaigns but the point of social marketing isn’t just to add Facebook, Twitter and SnapChat to a list of pipes to use to shove messages down consumers’ collective throats. Instead, these platforms offer brands an opportunity to reach out to consumers and actually have conversations with them. Those conversations can be collective or they can be on an individual level.
The key word here is “conversations”. Brands shouldn’t be talking at “target markets”, they should be conversing with consumers, people. Social networks give us the ability to identify our customers and talk to them as individuals. That sounds time consuming but it need not be.
DigiDay published another article which I read this morning, titled “How programmatic creative can revive your inner Draper“. It is a sponsored article but the point of the article is very interesting. The idea is to use programmatic tools to deliver highly personalized and relevant content to consumers based on their preferences and other relevant data points:
With programmatic creative, copy lines and art are loaded automatically, no assembly required. And thanks to HTML5, those ads can be fed into any inventory size available, without diminishing the quality of your media, particularly on mobile.
Thanks to a wide range of technologies, creative departments can use display video HTML5 ads to tell big, bold brand stories across platforms and, using ad sequencing, across time. Using data-driven creative, agencies can give their display campaigns a genuine narrative arc and show the right story to the right people, using data signals to set their parameters.Does your story happen over the course of a day? Use time-based signals to show “chapters” morning, noon and night? Is it a point-of-view play, with different family members coming at the same problem from different perspectives? Use data rules to reach them properly.
It is an intriguing way to be more personal in your approach to your consumers without falling into the trap of seeing consumers simply as entries in a SQL database to be “targeted” even if that is what most consumers may eventually become from an operational perspective. Simply abstracting the “target market” de-humanizes the people we are reaching out to and disconnects us from them. The result is that we lose the ability to really engage with them, connect to them and, consequentially, they don’t connect with our brands.
I had an issue I needed to resolve with my Internet and mobile provider, 012 Smile. I reached them through their Facebook page and, instead of just sending me a message in Hebrew (which isn’t unreasonable), I received phone calls from one of their call center agents in English because it was clearly the best language to use for me (my Hebrew is still pretty basic). 012 Smile uses Facebook really effectively to keep in touch with customers.
I received several calls to follow up and give me updates and it was a terrific experience for me as a customer; not because it was a particularly complex issue or because 012 Smile did something special for me it doesn’t do for other customers. What made a difference to me was that the call center agent took the time to phone me and talk to me in English.
Injecting a little humanity into the process could be what brands need to adjust their perceptions of just how effective their work is and help them adapt their strategies to more human conversations (at least, human-sounding conversations) powered by large data-sets and programmatic targeting tools.