Category Archives: Analytics

Live Tweeting

I have been having some fun on Twitter this last week (you can tell as I have also gained 20 followers this week) and have even got involved with a couple of Live chats.

The first was a Thursday night (well, 10pm GMT) #DataTalk discussion which I found out about via Klout – which is another thing I have been playing about with too (more on that later).  The topic was “What is a data scientist?”

Here is my tweet which retweets a Storify of the chat.

It was good fun, though as I was participating on my phone tucked up in bed (it was late ok), the refresh rate wasn’t very speedy and I seemed to be always a few tweets behind.  I found some new people to follow and have boosted my followers too, great stuff.  #DataTalk live tweets happen every other Thursday (I have set up a recurring appointment in my calendar, sad I know) – the next one is on Trends in Big Data and Challenges on Thursday May 7th; no questions up yet, but they do tend to list them in advance which is extremely helpful. I will definitely be participating in the future (bedtime permitting); hopefully more people will get involved in the future but wow, the hour just flew by!

Then on Friday 12pm-1pm I participated in #FRTweets – tweeting about fundraising; this time the topic was careers advice. A colleague of mine often participates in FRTweets so, after my Thursday night success thought I would give it a go.  One person had very similar views to me and I ended up following her and we got into a bit of banter after the London Marathon about cycling V running gear, which was pretty funny.

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Data Scientist MOOC

Some of you intrepid readers (again, hello mum) may remember I started a MOOC on writing a while ago.  Sadly, work went absolutely mental and I didn’t manage to progress beyond week 3 of 8.  I fully intend to pick it up again in the near future (ah, the joys and benefits of MOOCs!) and complete it.

However; I stumbled across a thread in LinkedIn which pointed me towards a different MOOC site, namely Coursera – this seems to be more US based and is a bit more computer/data-centric.

Anyway, I decided to start out with (because it says 4 weeks and only 1-4 hours a week, so I should be able to manage that) a course called the Data Scientist’s Toolbox, the first of 9 courses in their Data Science specialization. The course will also introduce me to some web-based software called GitHub (I’m sorry, I can’t help but snigger every time I read/write that). Other courses go on to look at R which should be useful; I have currently only used SPSS.

The course starts Monday 2nd February.

Careers Article in March PC Pro

A brief, shameless plug; look who is in this month’s PC Pro magazine!  Every month PC Pro profiles a particular career in IT and this month was the turn of the data analyst:

The article! March 2015 PC Pro issue 245

The article! March 2015 PC Pro issue 245

CASE Regular Giving Conference

In early December I was fortunate enough to be able to attend the CASE annual conference on Regular Giving.  I have been to a CASE conference before – the Spring Institute which was earlier (umm, in spring) this year.

So, on Tuesday night myself and a colleague got a slow train to Manchester to the Marriott Victoria and Albert hotel, arriving about 8pm on Tuesday – too late for dinner, but early enough to catch some of the speakers and attendees in the bar.

My suite – view from the door

The first surprise of the conference was when we turned up to check in – due to the number of rooms and what-not, my colleague and I were going to be in suites.  Wow.  Sadly, as is typical with CASE things, the schedule is so packed you barely get any free time at all (and what free time I did get in my room was spent searching my tremendous room for something I had just put down and forgotten where in the enormity of it all) – but I made sure I turned on my living room TV once and sat down on the sofa so I could say that I had done it.  I doubt I will ever be in a suite again!

suite - bed and column

Bedroom – I even had my own column

The CASE Regular Giving conference starts with an opening plenary on the Wednesday morning (after registration) at just after 10 with the last session finishing at 6pm – it’s a pretty full day, Thursday kicks off at 8:15 with breakfast roundtable discussions and then finishes at 5pm.  Sessions are a mix of plenaries that everyone attends, or workshops where the programme splits in two – fundamentals and innovations (I think this is similar to the CASE Development Services conference too).  There were also two showcase sessions (one each day) with a choice of two, allowing some of the suppliers/sponsors to give a 45 minute talk.  It was great to take a colleague along because it meant we could attend the full programme between us.  There had been a pre-conference on the Tuesday with two streams – one about crowdfunding and one about setting up a Regular Giving programme, but we did not attend.

I will follow-up in a later blog post with what I learned and what sessions I attended (as I did at Spring Institute), but I had been really looking forward to seeing Adrian Salmon from Leeds (his blog is here and twitter feed here) he was doing a number of sessions and I also wanted to chat to him in general.  Sam Davies who was at SI was also going to be there, delivering one of the last elective sessions.  Sadly Bob Burdenski was unable to be there; his plane was cancelled due to the bad weather in America – a real shame.
In fact, there ended up being a big group of delegates from my Spring Institute – it was great to catch up with everyone and see how people had grown in experience, confidence and knowledge. Extremely heartening for the future.

Open rates over time

I have been thinking not just about the overall open rate of an email, but about when emails get opened.  How long after we have sent an email can we assume most people who are going to read it, will have read it?  here are two examples of two very different emails that I have sent in the last few months.

In example A emails were sent to 129,226 people at around 9:30 in the morning.  This email went to alumni and had an overall open rate of 37%

Email A

In example B emails were sent to 1,395 individuals with a particular interest marked on their record (the email was a fundraising solicitation, they were not necessarily donors to this area though, or indeed to anything).  This email had an overall open rate of 51%.

Email B

There were no geographical limiters on either email, so recipients were from all over the world (but mostly based in the UK).

The results are extremely interesting, I stopped measuring in both instances 3.5 weeks after the initial send as opens at this period, became increasingly rare.:

  • In both email A and email B, about half  (A=46%, B=51%) of all those who opened the email, opened the email that same morning
  • In both emails, around 85% (A=84%, B=86%) of all those who opened the email had opened it on the same day as it was sent.
  • Over 90% of all those who opened the email, opened it in the first two days of sending (A=91%, B=93%)
  • A week after sending the email almost everybody who was going to open it had done so (A=98% B=97%)

Both emails were sent around 9:30am and both were sent on a Thursday – this was coincidental (fitting send in around work-loads, other tasks, database upgrades) and not deliberate, but it is interesting, particularly when you consider the high percentage overall opens in the first 2.5 hours compared to the 12 hours that come later that same day from 12 noon to 12 midnight.