See first, think later, then test.
But always see first. Otherwise you will only see what you were expecting.
— Wonko the sane, in ‘So long, and thanks for all the fish’
Commenting – AVC, Disqus, privacy, and WordPress
This is what greeted me when I tried to comment on Fred Wilson’s post today.
Fred Wilson wrote about signing up to Pocket, and requested suggestions for becoming a power user. Naturally, I wanted to comment with a plug for my Chrome extension for Pocket. I also wanted to offer my 2c on why I found Instapaper better than Pocket 1.
I didn’t. Continue reading Commenting – AVC, Disqus, privacy, and WordPress
Availability bias and the remote work advantage
Removal of the easiest to observe input metric – face time – reduces the availability bias in remote work organisations, and helps them focus on the more productive outcome-based metrics.
This switch to emphasis on outcomes can be helpful for individual productivity, but is truly transformative when the whole organisation goes remote-first.
Behavioural biases confuse performance appraisal in office-based organisation culture
The time spent in office looking productive is a key factor in performance appraisals across teams and organisations. Even when time in office is not a formal factor, it unconsciously creeps in and affects rating scores on other factors.
This focus on input factors and ‘visible productivity’ (time spent, sales calls made, lines of code written1, bugs closed) is a result of the availability heuristic and substitution bias in action.
The outcomes of an individual/team’s work are delayed and often diffused – hard to credit exactly. However, the inputs are visible and trivially measurable. In pursuit of productivity metrics, the manager/organisation substitute the hard to measure outcomes with the easily available input factors (time spent in office, calls made, lines of code) etc.
Continue reading Availability bias and the remote work advantage
Should we use multi-year, moving-average of income to calculate tax & benefit payments?
Income tax rates are based on current/last year’s income. This makes them easy to calculate and implement.
This immediacy of taxes also makes them painful, and makes the tax slab thresholds as artificial barriers to income mobility. An example of this is when we get a raise which pushes us from near the top end of one tax rate bracket, to the bottom end of a higher tax rate bracket. This frequently means that even though the employer is paying us more after the raise, we are actually taking home less money due to a higher tax rate.
Government benefits work similarly. For example, the unemployment benefit / social support payments cut off (or reduce dramatically) when we start working. However, after accounting for taxes and loss of benefits, the take home income from pay is often lower than the unemployment benefits.
Money is the final metric
This headline reminds me of the old principle that I emphasise:
Money is the final metric
If our metrics don’t directly correlate to, or convert into money1 in the near term, then they are not the correct metrics.
Too many metrics, in my experience, are designed for being:
- easy to measure (or easily available),
- easy to improve, and
- comfortable to explain
What they are not designed for: being strongly correlated with current or future supply of money.
For growth, revenue (total, unit, net unit) is the best metric.
Views of our videos2 can be good metrics if they convert directly into money:
– product sales show a direct correlation, or
– advertisers accept them as proxy for ad views, or
– investors require them as a valuation input for the next raise
Views of our videos are a bad metric when they aren’t directly impacting revenue. If sales aren’t growing in proportion with the views, then counting views is of no relevance to the health of the business.
The availability bias trip-wire
The availability heuristic operates on the notion that if something can be recalled, it must be important, or at least more important than alternative solutions which are not as readily recalled.
Most online advertising platforms understand this well, and use it to hook their customers (advertisers). If they show us good3, clean and easily accessible metrics of their choice, we will give those metrics more weight than they deserve.
The views count is right there – in the analytics dashboard. While finding the correct metric that actually correlates with sales, and then tracking it, can be hard.
This also ties in the second part of the hook. The view count number is also easily movable. Spend some money on advertisements, the view count will go up. Voila! View counts – a metric that is easy to measure, and easy to improve!
Those money-correlated metrics, they are even harder to improve than they are to discover and track. Oh look, the views went up again!
We all have heard of ‘Our viewer/user/visitor numbers were amazing, but the money ran out before…’
This is why the money ran out. Because we chose the easy metric, over the metric that really matters – money.
TIL: Moving average with missing periods in Google Sheets
The short answer:
=AVERAGEIFS(A1:A99, B1:B99,"<="&B2, B1:B99, ">"&B2 - 30)
Values to be averaged are in A1:A99, corresponding dates are in B1:B99.
What the formula does: average the values in the range – Include a value in calculating average for the current date if:
- The date for that value is same as or before the date in the current row, and
- The date for that value is greator than the date X days before the date in the current row (X is 30 in the formula for a 30-day moving average)
The long one:
I have a spreadsheet with my daily weight log. It has occasional missing days – when I didn’t log my weight.
Yesterday, I wanted to chart this data, and wanted to add a moving average to it. Google sheets’ in-built moving average trend line refused to work – either due to the missing data, or due to the number of entries. So I added a column to the sheet with the calculated (trailing) moving average weight.
I’ve never before had to calculate moving average over a non-consecutive data set. So, in case I forget, I’m noting it down here for later…
Continue reading TIL: Moving average with missing periods in Google Sheets
About those exorbitant hospital parking fees
I had an appointment at the hospital today, and was thinking about the rates at the hospital car park. The parking area at big NHS hospital in my town has the highest parking rates around. They are probably more than double the rate at any other paid parking zone in the town.
At a first look, they seem extortionist. At most places, high parking rates are a nudge for users to either take an alternate means of transport, or to curtail their visits. At a hospital, however, few people visit by choice. Also, the visitors are more likely to use a car – comfort for the ill and all that. By charging these, probably ill, visitors these extraordinarily high rates, the hospital/NHS/council are just heartlessly milking the already suffering.
Unjust!
On a second thought, however, there is a valid reason behind these high rates – consumption tax. They are not just parking rates, they are an indirect tax on the heaviest NHS users.
Continue reading About those exorbitant hospital parking fees
Twitter’s product ideas funnel
Since its founding, Twitter has made a religion of listening to users. After all, they came up with some of the company’s best ideas — including the hashtag, reply and retweet. After the flow of good ideas from users stopped, Twitter was hard-pressed to come up with its own.
Bloomberg: Why Twitter Can’t Pull the Trigger on New Products
The first part of that quote is a fact – users came up with hashtag, reply, and retweet, and Twitter (the company) adopted them.
However…
Premier league table – the trend continues
Follow-up to the last week’s post.
All the trends from the previous week continue…
- Manchester City (blue) continue to run away with the title, West Brom (slate) continue a lonely run at the bottom.
- The 2nd and 5th placed teams are now just 4 points apart (red) – Manchester United lost, while Chelsea, Tottenham, and Liverpool all won.
- Arsenal (slate) continue to be in the middle of nowhere – 7 points behind 5th, 9 points ahead of 7th.
- The mid-table / relegation pack (light grey) got even tighter – only 11 points between the 7th and 19th placed teams.
Premier league table – some trends this season
That chart from the BBC got me interested. Looking at the Premier league table distributed by points makes it lot more interesting than distribution by ranks.
So, I downloaded the Premier League data for current season from Football Data, and created some graphs1.
Weeks 1-8: Mixed bag, except for Crystal Palace
7 straight defeats! Crystal palace really had a crap start to the season!
Continue reading Premier league table – some trends this season