Australian ISP Market Share, March 2012

Back in 2010 I had a go at estimating the relative market shares of Internet Service Providers (ISPs) in Australia. A reader has asked if I have more recent data, so I thought it was high time I revisited these estimates.

I’ve applied the same method as last time: Roy Morgan make ISP customer profiles available for purchase. At the bottom of each report’s synopsis, you’ll see that a sample size has been included (for example, the Internode customer profile is based on a sample of 404 customers).  Now, combining these sample sizes from the ISPs’ profiles I think could potentially provide a good basis for estimating overall market share.

My results are presented in Table 1 below. Market share is ALL business, government and home subscribers with ANY kind of internet access including dialup, DSL, cable, fibre, satellite and wireless (fixed & mobile).

The Roy Morgan samples behind these estimates were taken between April 2011 and March 2012 — except where marked with an asterix. Marked samples were taken between April 2010 and March 2012 (i.e. over two years instead of one), so please regard estimates based on these data with extra caution. Market share in these cases could actually be much smaller than calculated here.

Several ISPs listed individually in Table 1 are actually subsidiaries  of larger groups, particularly the iiNet group. As a group, iiNet now has a combined market share of 13.2%. Please refer to the notes under the table for more details.

Table 1: Estimated Australian ISP market share, March 2012

Internet Service Provider Roy Morgan
sample size (no.)
Estimated relative
market share (%)
3 Internet 243 1.8%
AAPT 255 1.9%
Adam 185 1.4%
Chariot 135 * 1.0% *
Dodo 348 2.6%
Exetel 119 0.9%
iiNet 660 4.9%
Internode 404 3.0%
iPrimus 237 1.8%
Netspace 164 1.2%
Optusnet 2,367 17.6%
TADAust Connect 144 * 1.1% *
Telstra Bigpond 6,607 49.2%
TPG 749 5.6%
Unwired 111 * 0.8% *
Virgin 163 1.2%
Vodafone 252 1.9%
Westnet 297 2.2%
TOTAL 13,440 100.0%

* Treat these data/estimates with extra caution. Time period for these samples are two years, April 2010 – March 2012. All other time periods are one year, April 2011 – March 2012.

Important notes:

  1. AAPT, Netspace, Westnet, and Internode owned by iiNet. Estimated market share for whole iiNet group = 13.2%.
  2. Adam Internet serves South Australia and Northern Territory only. Also, Adam are in the process of being acquired by Telstra (at time of writing).
  3. Chariot is owned by TPG.

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Australian ISP Market Share, 2009-2010

The market research firm, Roy Morgan, has released its latest ISP satisfaction data, with an overwhelmingly positive result recorded for Internode and iiNet.

According to the latest Roy Morgan Internet Satisfaction data, Internode (93.4%) is still the top performer for customer satisfaction while iiNet (89.9%) appears to be closing the gap from 5.6% points in the 6 months to April 2010 to 3.5% points in the 6 months to May 2010.

Scrolling further down the Roy Morgan press release page, you’ll find individual ISP customer profiles available for purchase.  At the bottom of each report’s synopsis, you’ll see that a sample size has been included [for example, the Internode customer profile is based on a sample of 305 customers].  Now, combining these sample sizes from the ISPs’ profiles I think could potentially provide a good basis for estimating market share.

My results/estimates are presented in the table below.  Market share is ALL business, government and home subscribers with ANY kind of internet access including dialup, DSL, cable, fibre, satellite and wireless [fixed & mobile].  The Roy Morgan samples were taken between April 2009 and May 2010.

Table 1: Estimated Australian ISP market share, 2009-2010

Internet Service Provider Roy Morgan sample
(no.)
Est. market share
2009-2010 (%)
3 Internet 322 2.6%
AAPT 342 2.8%
Adam 134 1.1%
Chariot 134 1.1%
Dodo 321 2.6%
Exetel 206 1.7%
iiNet 509 4.2%
Internode 305 2.5%
iPrimus 284 2.3%
Netspace 166 1.4%
Optus 2,099 17.3%
Primus-AOL 153 1.3%
TADAust 119 1.0%
Telstra 5,710 46.9%
TPG 539 4.4%
Unwired 175 1.4%
Virgin 158 1.3%
Vodafone 103 0.8%
Westnet 384 3.2%
TOTAL 12,163* 100.0%
  • AAPT, Netspace and Westnet are owned by iiNet
  • Chariot is owned by TPG
  • Adam offers residental internet access in South Australia & Northern Territory only

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Can I get a Little MORE support around here?

In November last year, I blogged about the phone queue reporting and graphing page beta-released by my ISP, Internode.  The aim was to use the data presented on that page, with some basic queuing theory (Little’s Law), to determine the size of their helpdesk.  I theorised that a rough estimate for how many Internode support staff are on duty at any particular point in time could be given by:

Calls in Queue x 12.5 / Wait Time

Looking at the hourly averages, I concluded that, on the Saturday of my analysis, Internode helpdesk had 8 or so people on hand to assist with customers’ technical problems.  I have been informed that my estimate for that period was surprisingly accurate.

The graphs and hourly averages data were taken offline for a little bit, but they’ve recently been reinstated.  I thought it would be timely and interesting to have another look and see what’s changed over the intervening months.  Last Saturday evening I went through and analysed the hourly averages covering the time period from 8pm Friday (17 July 2009) to 8pm Saturday (18 July 2009).  Note that Internode’s residential technical support helpdesk is staffed from 7am to midnight, 7 days a week.  I then applied the same methodology from Can I get a Little support around here to estimate the number of support staff on duty (last column).

Table 1: Internode helpdesk phone queue – hourly averages

Time period Avg. wait time
(mins:secs)
Avg. calls queued
(no.)
Support staff on duty
(est.)
Friday, 8pm-9pm 00:21 0.1 4
9pm-10pm 00:22 0.1 3
10pm-11pm 00:21 0.0 not enough data
11pm-midnight 00:22 0.0 not enough data
Saturday, 7am-8am 00:22 0.1 3
8am-9am 00:30 0.2 5
9am-10am 00:22 0.2 7
10am-11am 00:26 0.3 9
11am-noon 00:22 0.2 7
noon-1pm 00:22 0.2 7
1pm-2pm 00:23 0.2 7
2pm-3pm 00:23 0.2 7
3pm-4pm 00:58 0.7 9
4pm-5pm 00:22 0.2 7
5pm-6pm 00:23 0.2 7
6pm-7pm 00:22 0.1 3
7pm-8pm 00:21 0.1 4

Looking through my small window of analysis, it appears that Internode have largely resolved any problems they were experiencing late last year/early this year in terms of extraordinarily long wait times.  Time spent in the phone queue has collapsed from around 10 minutes to less than 30 seconds.  However, this dramatic improvement doesn’t appear to be due to any significant increase in staff numbers.

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Estimating Adam Internet’s Broadband Customer Numbers

Executive Summary

I estimate that Adam Internet is somewhere between 27% and 30% the size of Internode, which equates to between 42,249 and 46,934 broadband customers.  This estimate does not include other services such as 3G, VoIP and dialup customers.

Introduction

Adam Internet” is an Internet Service Provider (ISP) based in my home town of Adelaide, Australia.  They are tightly focussed in terms of the markets that they operate – only providing residential broadband services (apart from 3G) to South Australia and the Northern Territory.  I’ve never used them, but by all accounts they are a very reputable company, with high levels of customer satisfaction and a strong parochial following in the Whirlpool forums.  Adam Internet is also the eternal, bitter, blood-rivals of Internode.  Well, that might be a bit of an exaggeration.  I wanted to inject a bit of drama.

Nothing like a bit of drama.

Anyway, as a privately owned company, Adam Internet is not required to make public how many customers it has.  Indeed, when it comes to operational data, they play their cards very close to their chest indeed.  That’s fine.  It’s not really any of our business.  However, when the company last chose to talk about subscriber numbers, it was “about 75,000 customers“.  It’s not clear whether this is just residential broadband customers, or all customers including VoIP, dialup, and other services.

The Challenge

I thought it would be fun to look at some ratios available in the public domain and then, keeping with the Adam-Internet-vs.-Internode theme of this post, use Internode as a base line to estimate just the number of Adam Internet broadband customers.

The Data

Adam Internet and Internode both attract a very loyal following among the ne’er-do-wells of the Whirlpool forums.  With that in mind, the first metric I looked at was the ratio between respondents to the last three annual Whirlpool surveys.  Assuming that there’s a correlation between Whirlpool survey respondents and number of customers this may be a useful measure of relative size.  The results are summarised in the table below:

Whirlpool survey year Adam Internet respondents Internode respondents Respondent Ratio
2006 707 3,156 0.224
2007 1,003 2,981 0.336
2008 673 2,649 0.254
Average 794 2,929 0.271

Source: Total respondents taken from the question, “Would you recommend your ISP to other people?”

Results from the last three Whirlpool surveys suggests that Adam Internet is 27.1% the size of Internode.

The second metric I used was Google Trends.  Assuming that there’s a correlation between Google searches and customer numbers, Google Trends may be a useful indicator of a company’s market share.  I compared average traffic of “Internode” to “Adam Internet” (without the quotes) from Australia over the last 30 days.  At the time of writing, Adam Internet’s Google traffic was 27% of Internode’s, a remarkably close match to the average Whirlpool survey ratio above.

adam-vs-internode-google-trends

Results from the Google Trends comparison suggests that Adam Internet is 27% the size of Internode.

Finally, I turned to Wikipedia.  At the time of writing, Internode had 300 staff to Adam Internet‘s 100 staff.  Assuming that both companies adhere to roughly the same staff-to-customers ratio, Wikipedia suggests that Adam Internet is about 30% of the size of Internode.

The Results

Of course I’m more than likely totally wrong in all my assumptions.  And probably the entire approach is wrong.  However, the three sources of Whirlpool, Google and Wikipedia all seem to suggest that Adam Internet is somewhere between 27% and 30% the size of Internode.  If wrong then at least it’s consistently wrong.

But if I’m confident that the percentage market share is in the ballpark, how would this translate to actual numbers of broadband customers?

Back in September last year I had a crack at modelling Internode’s growth rate.  The formula that I came up with then was:

Internode broadband customers = 463.047 * loge(0.626 * loge[year])

At the time of writing (22 May 2009), “year” is 9.389.  Plugging that value into the formula suggests that Internode currently have 156,447 broadband customers.  Admittedly a lot has changed at Internode since September 2008.  They’ve released many new products onto the market including (in no particular order) Chumby, Tivo, ADSL “TwoPlus” and even (rather belatedly) a 3G product of their own.  Not to mention the obvious problem that my model was a big dodge right from the start.  But the last media release I read from Internode quoted “more than 150,000 (broadband) customers” nationally.  I guess if Internode had more than 160,000 broadband customers they’d say so?  So I think it’s fair to say the number is between 150-160k.  Perhaps Internode are just being modest.  Or, God forbid, perhaps my model is proving to be not only correct but remarkably resilient!

Conclusion

If Adam Internet is somewhere between 27% and 30% the size of Internode as indicated, and if Internode have 156,447 broadband customers as modelled, it follows that Adam Internet have between 42,249 and 46,934 broadband customers of their own.  If correct then the difference between that figure and the publicly quoted number of 75,000 customers could be made up of 3G, VoIP, dialup and other Internet services.

This analysis is just a bit of fun.  Don’t take it too seriously.  Having said that, constructive feedback is always welcome.  Do you think I’m in the ball park or so off base it’s not even funny?  Would a senior Adam Internode rep like to comment?

Does one in four equal fifty percent?

… perhaps for sufficiently small values of “four”…

In February 2008 two of Australia’s leading Internet Service Providers, iiNet and Internode, released an ADSL2+ broadband “heatmap”.  It illustrated what a sample of 16,000 customers within the Sydney area were achieving in terms of real ADSL2+ broadband download speeds.  The heatmap can be downloaded from the Internode website here and I’ve reproduced it below.

adsl2-sydney-heatmap

The map itself commits a few GIS sins including no scale or north arrow.  And personally I find the chosen colour grading makes it difficult to differentiate between the top two speed cohorts.  These criticisms aside however, the point that iiNet and Internode wanted to make is that 50% of metropolitan dwellings are technically capable of speeds of ~12Mbps or faster right now.  And certainly the map seems to support their contention (ISP… contention… boom, tish!… *sound of crickets chirping in the background*).

It’s worth mentioning that the map is a political statement.  iiNet and Internode believe there is no justification for laying down a new and very expensive Fibre To The Node National Broadband Network.  They want to show that the existing copper infrastructure can deliver NBN-like speeds to a large swathe of the population today.  Of course the irony is that both companies are part of a consortium (known as Terria) bidding to actually build the NBN – an NBN that neither actually want.  I guess they decided it’s preferable to be Victor Frankenstein rather than one of The Creature’s innocent victims.  Not that they have much to worry about.  As at January 2009 the NBN appears no closer to being built than it was back in February 2008.

But back to the map.  I have a problem with the map.

Let’s start with this graph on Internode’s website.  It shows how fast ADSL2+ speeds can be, based on your distance from the exchange:

internode-adsl2

ADSL2+ download speeds drop off exponentially as distance from the exchange increases.  12Mbps or faster is possible at distances of up to 2.5kms.  From that point onwards bitrates rapidly skid off the rails.  Any kind of meaningful ADSL2+ “broadband” (i.e. >1.5Mbps) seems to crap out at approximately 5kms or so.

Now consider the ideal case.  Imagine if dwellings are distributed randomly and uniformly around their exchange inside a perfect circle of radius=5km.  Also assume (I admit unrealistically) that the dwellings are connected to the exchange via perfect, straight, radial lines.  Dwellings within 2.5km of the exchange can download at 12Mbps or faster.  Households located 2.5km to 5km can still get ADSL2+, albeit at lower bitrates.  Any poor saps living beyond the 5km limit cannot get ADSL2+ at all.

adsl-circle

OK, that’s a lot of assumptions.  But proportionally π2.52/π5.02=25% of the dwellings are inside the 12Mbps-or-better red zone.  A far cry from the 50% that iiNet and Internode are claiming.

Either I’ve made a hash of the whole analysis (highly likely) or something odd is going on.  One possible explanation is that households who cannot get very fast ADSL2+ speeds simply don’t bother signing up for ADSL2+ at all.  Many households outside the theoretical 12Mbps limit seem quite happy to stick with ADSL1 or some other product.

Something for me to ponder.

P.S. Here’s an excellent mashup of the heatmap and Google Maps courtesy of Whirlpool member “Switters“.

Can I get a Little support around here?

Yesterday my Internet Service Provider, Internode, released a public beta of its phone queue reporting and graphing software.  You can view it here:

http://www.internode.on.net/customer-service/

It’s somewhat hypnotic watching the results being updated every 60 seconds.  And there’s quite a few interesting statistics presented on the page, especially if you’re into queuing theory.  In fact I’d like to use queuing theory, and Little’s Law in particular, to have a guess at how many Internode Residential Technical Support Staff are on duty at a given point in time.

Little’s Law (from Wikipedia) states that: The long-term average number of customers in a stable system (N) is equal to the long-term average arrival rate (λ) multiplied by the long-term average time a customer spends in the system (T).  That is, N = λT.

Let’s turn things around a bit so that they apply to Internode’s support desk.  N in Little’s Law will become the number of support staff on duty, λ the rate at which callers come off the queue, and T the time taken to resolve technical problems.

But first things first.  And first we must estimate λ.

Luckily Internode makes this fairly easy.  We are given total wait time and total calls in the queue at a particular point in time.  So callers are arriving on the desk at a rate of (total calls in queue)/(total wait time in queue) every minute.  This is λ.

T is a bit trickier to estimate.  How long does it typically take for support staff to resolve a customer’s technical problem over the phone?  I don’t know.  All I really have to go on is this post from Exetel’s John Linton’s personal blog.  Specifically:

Given an average of 5 minutes of initial talk time per ‘live’ call and the requirement to spend a further 7 – 8 minutes for each call in either call back or other actions…

So it would seem it takes somewhere in the vicinity of 12 or 13 minutes for helpdesk to work through a customer’s technical issue on average.  That sort of feels right based on my very limited experience when it comes to contacting customer support.  Meh, I’ll split the difference and say T=12.5.

Therefore a rough estimate for how many Internode support staff are on duty at any particular point in time could be given by ( “Calls in Queue” x 12.5 ) / “Wait Time”.  For example, as I write this, there are 3 calls in the residential technical support queue and total wait time is 5.01 minutes.  So there must be 12.5×3/5.01=7 staff on helpdesk at the moment.

Earlier this afternoon I went through and analysed the hourly averages covering the time period from 5pm yesterday to 5pm today.  Note that Internode’s residential technical support helpdesk is staffed from 7:30am to midnight, 7 days a week.

Time period Avg. wait time (mins) Avg. calls queued (no.) Est. support staff on duty
Friday, 17:00-17:59 24.39 14.04 7
18:00-18:59 16.45 8.35 6
19:00-19:59 8.36 7.25 11
20:00-20:59 5.3 3.57 8
21:00-21:59 0 0.12 not enough data
22:00-22:59 0 0 not enough data
23:00-23:59 0 0 not enough data
Saturday, 7:00-7:59 3.2 1.27 5
8:00-8:59 5.51 4.17 9
9:00-9:59 14.5 7.08 6
10:00-10:59 16.17 9.03 7
11:00-11:59 6.51 3.52 7
12:00-12:59 6.59 4.18 8
13:00-13:59 12.25 6.67 7
14:00-14:59 8.36 5 7
15:00-15:59 1.1 0.9 10
16:00-16:59 0 0.13 not enough data

Things bounce around a bit because we’re dealing with averages and estimates and other problems such as small amounts of data.  However, looking through my small window of analysis (5pm Friday to 5pm Saturday), it looks like Internode helpdesk usually has 7 or 8 staff on hand to answer customer calls.  Totally unsurprisingly, extra personnel are available to cover peak periods.

I should repeat the analysis covering a busier time when sales and accounts staff are also on duty such as the mid-week period.

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Thanks for reading.

Your shipment of FAIL has arrived

shipment_of_fail

I was reading “Steve Waddington’s Network Notes” today where he describes how two electrical components failed on him at the same time, and wonders what the chances were of it happening.

It is highly unlikely that two electrically isolated components would fail at exactly the same time. Since both are rated at an MTBF of 90,000 hours, the chance of them both failing in any given hour, after less than 10,000 hours of operation, would have to be in the region of one in one billion.

It got me wondering… what were the chances?

“MTBF” is “Mean Time Between Failures”.  It is the reciprocal of the failure rate, λ, and follows an exponential failure distribution.  This distribution is asymmetrical, so it is not true to say that the MTBF represents the point at which the probability of failure equals 50%.  However, an exponential distribution does make probability calculations relatively easy.

P(component fails at exactly 10000 hrs | MTBF=90000 hrs) = λe−λx

= 1/90000 * exp (-1/9)

= 0.00000994

or about one chance in 100,577.

That’s the chance of one component failing.  The chance of the two components failing at once is therefore one in 100,5772 or more than one in 10 billion.  No wonder Steve ruled out “just bad luck”.  I’d be very suspicious too.  It will be interesting to see if his theory of a dodgy part proves to be correct.