Tag Archives: mac

Is the golden age of Indie software over?

The concept of shareware appeared in the 1980s. Developers would use relatively primitive tools to create their software, then promote it via fanzines, user groups and bulletin boards to a niche audience of shareware fans. If you wanted to try the software, you would have to get hold of a floppy disk with it on. And, if you wanted to buy a licence, you would generally have to post a physical cheque to the developer. This was being an Indie developer in hard mode. A few people made a lot of money, but most vendors made modest returns on their efforts.

I started selling my first software product in 2005. This was a good time to start up as an independent software vendor. High quality compilers, IDEs, debuggers, version control systems and web servers were widely available and mostly free. The market for software was growing, as more and more people purchased PCs and Macs. Payment processors were starting to streamline online payments. But the real revolution was being able to distribute your software worldwide via an increasingly ubiquitous Internet. And getting noticed by potential customers, while never easy, was generally achievable through writing content for search engines to find, paid online ads (such as Google Adwords pay per click), download sites or even ads in physical magazines. With a lot of hard work and a bit of luck, it was quite possible to make a decent living.

Things have continued evolving at a rapid pace over the 20 years I have been selling software. Development tools have continued to improve. Mobile and web-based software has become mainstream. App stores have appeared. Outsourcing became a thing. Subscription payment models are increasingly common. Mostly these changes haven’t affected my business too much. But recently things have begun to feel noticeably harder.

LLMs have made a major impact. While I don’t worry that LLMs will do a better job than my seating planner software, data wrangling software or visual planning software any time soon (my main competitor remains Excel), everyone is noticing that their web traffic is falling. People increasingly read LLM summaries rather than clicking on search engine links or the accompanying ads. Maybe the LLM will include a link to the website that they ripped off the content from, but probably they won’t. So writing content in the hope of traffic from search engines is becoming less and less of a viable strategy to get noticed.

Other promotional channels are getting squeezed as well. Online ads are increasingly expensive and rife with click fraud. This makes it hard to get any chance of a return, unless lifetime customer value is hundreds of dollars. Google Adwords is a case in point. In the early days, I could get lots of targeted clicks at an affordable price. But Google have done everything they can to raise bid prices and generally enshittify Adwords, so they can grab more and more of the value in every transaction. I now get barely any clicks at bid prices I am prepared to pay.

One of the few useful promotional channels left is YouTube. But it is very time-consuming to produce videos and the amount of competition is huge. I fully expect generative AI to erode its value over time, as AI slop floods the channel.

Typically promotional channels start off great for vendors and become less great over time (the law of shitty clickthrus). But then new promotional channels appear and the dance starts again. But there just doesn’t seem to be much in the way of viable new channels appearing for Indie vendors like myself. My experiment with advertising on Reddit did not go well.

LLMs potentially also make software easier to write, which is a double-edged sword. It might help you code features faster, but it also lowers the barrier, so that more people can compete. Even if your new competition is bug riddled garbage, ‘vibe coded’ by someone who doesn’t know what they are doing, it still makes it harder for your product to get noticed.

The general cost of living crisis hasn’t helped either. The super-rich are making out like bandits, but everyone else has less disposable income. And that is only going to get worse when the current AI funding circle-jerk implodes.

Each of the different software platforms also have their own issues.

  • Downloadable software has fallen out of fashion and the market is shrinking as increasingly people expect software to be web-based. People are also wary about downloading software onto their computers, in case it contains malware.
  • Web-based software is more of a service than a product and is expected to be available 24×7. Expect to get lots of very unhappy emails if your server falls over. And woe betide you if your customer data is hacked. Disappearing off somewhere for a few days without an Internet connection is not really viable, unless you have employees.
  • Mobile-based software is expected to be free or, at best, very cheap. So requires huge scale to make any decent return. And that is tough when there are some 2 million apps in the iPhone app store. You are also at the mercy of app store owners, who really don’t have your best interest at heart.

The new wave of AI tools must be creating new opportunities, but it seems these opportunities are mostly there for big companies, not for Indie developers. And it is very risky to build your product as a thin layer on top of someone else’s platform. Ask people who built tools and services on top of Twitter.

It feels that it is getting harder for small software vendors, like myself, to make a living. Of course, this could be just the ramblings of a 50-something-year-old, looking back through his rose-tinted varifocals. What do you think? Has it got harder?

If you want to show indie software vendors some love, check out all the great indie software for Mac and Windows (including my own Easy Data Transform and Hyper Plan) on sale at Winterfest.

Updating application icons for macOS 26 Tahoe and Liquid Glass

The application icon for my data wrangling software looks like this on a Mac up to macOS 15.x:

However, Apple has once again nuked everything from orbit. Now it looks like this in macOS 26 Tahoe when the application is inactive or if you choose any Icon & widget style apart from Default:

Thanks Apple. It is such a joy to develop for Mac.

With some help from a designer and hours of going around in circles, I have finally managed to fix things to support the new ‘liquid glass’ look. This is how it should look in the next release, depending on the setting in Appearance>Icon & widget style:

Default
Dark
Clear/Light
Clear/Dark
Tinted

I’m not convinced it is an improvement in terms of usability. But, at least my app icon doesn’t look like shit.

What you need to know

The new macOS 26 icon format is .icon. It is a folder full of various resources and is totally different to the old .icns format.

The .icon file can be created by Apple Icon Composer. I used a freelancer on Fiverr who did a good job of converting my existing vector artwork and was very cheap. The .icon file should contain a maximum of 4 groups (which seem to be like layers) other it won’t compile to a resource properly.

Note that macOS hide the extension of .icon folders by default, which was a source of some confusion.

The .icon file then has to be processed into an Assets.car file using actool. For example:

xcrun actool application.icon --compile ./icons/macosx --output-format human-readable-text --notices --warnings --errors --output-partial-info-plist temp.plist --app-icon Icon --include-all-app-icons --enable-on-demand-resources NO --development-region en --target-device mac --minimum-deployment-target 26.0 --platform macosx

You will need to change the bold parts above, as appropriate.

I had to update my Mac laptop to macOS 26, Xcode 26 and the macOS 26 SDK for the above to work.

You can check the Assets.car file using assetutil to create a .json file listing the contents:

xcrun --sdk macosx assetutil --info ./icons/macosx/Assets.car > ./icons/macosx/temp.json 

You will need to change the bold parts above, as appropriate.

Then you need to reference the new icon in your existing application .plist file. For example, add at the same level as CFBundleGetInfoString:

<key>CFBundleIconName</key>
<string>application</string>

You will need to change the bold part above, as appropriate. I believe the string value if based on the file stem of the original .icon file. But I’m not 100% sure about that. Look at the .json file produced by assetutil for clues. Mine contained this:

The temp.plist file generated by actool is apparently supposed to give you a .plist file that refers to the icon resource. It didn’t and was completely useless.

Then place both Assets.car and your old .icns file in the Resource folder of your application (before you sign it). That way it should look ok on both macOS 26 and earlier OSes.

If you develop using XCode, it will probably do some of the above for you. I develop in C++/Qt using Qt Creator, so I had to do it all manually.

I was able to generate the Assets.car on macOS 26 and then incorporate it into the build on my macOS 12 development machine.

I hope the above saves someone a few hours. Now I need to repeat the process for PerfectTablePlan.

You might also find this post useful (where I got some of the information):

https://www.hendrik-erz.de/post/supporting-liquid-glass-icons-in-apps-without-xcode

What I learned spending $851 on Reddit Ads

I am always on the lookout for cost and time effective ways that I can market my software products. Previously, I have had quite a lot of success with Google Adwords Pay Per Click ads. However, the law of shitty clickthroughs means that advertising platforms generally get less and less profitable (for the advertisers) over time. And Google Adwords is a case study of that law in action. As Reddit is a less mature advertising platform, I thought it might still offer opportunities for a decent return. So I decided to experiment with advertising my data munging software, Easy Data Transform, on Reddit.

[By the way, I understand that nobody goes to Reddit because they want to see ads. But commercial products need to market themselves to survive, and Reddit probably wouldn’t exist without ads. Yay capitalism.]

Setup

The basic process to get started with Reddit Ads is:

  • Sign up for a Reddit Ads account.
  • Enter your details and credit card number.
  • Create a campaign.
  • Create one or more ad groups for your campaign. Choose a bid for each ad group, which countries you want it shown in and who you want it shown to.
  • Create one or more ads for each group.
  • Add the Reddit tracking pixel to every page of your website.
  • Set up conversion goals.

All pretty standard stuff for anyone who has used Google Adwords. The twist with Reddit is that you can advertise to communities (sub-Reddits), rather than based on search keywords. For example, Easy Data Transform is a much better tool for most data wrangling tasks than Excel, so I can bid to show ads targeted at Excel users in communities such as: reddit.com/r/excel/ and reddit.com/r/ExcelTips/.

Like Adwords, there are various ways to bid. I don’t want the advertising platform to set the bid prices for me (because I’m not insane), so I opted for fixed price bids of between $0.20 and $0.40 per click. Some of the ad groups suggested much higher bids than that. For example, the suggested bid for my Excel ad group is $0.79 to $4.79 per click!

However, Easy Data Transform is only a one time payment of $99. Paying more than $0.40 per click is unlikely to be profitable for me, especially when you factor in support costs. So that is the maximum I was prepared to bid. Also, the suggested bids are just the ad platform trying to push up the bid price. Something that anyone who has used Google Adwords will be all too familiar with. I was still able to get clicks, bidding significantly less than the recommended minimum.

I also set a daily maximum for each ad group, just in case I had messed up and added a zero in a bid somewhere.

I created multiple ads for each ad group, with a range of different text and images specific to the communities targeted. Here are some of the ones I ran in the Excel ad group:

Clicking ‘Learn more’ takes you to https://www.easydatatransform.com/.

I didn’t try to use edgy images or memes, because that isn’t really my style. There is an option to turn comments on below ads. As Reddit users are generally not well-disposed to ads, I didn’t try turning this on.

Based on hard-won experience with Google Adwords, I only set my ads to run in wealthy countries. I also restricted my ads to people on desktop devices as Easy Data Transform only runs on the desktop.

When Easy Data Transform is installed, it opens a page on my website with some instructions. So I used this to set up the Reddit conversion tracking to count the number of times a click ended up with a successful install of either the Windows or Mac version of Easy Data Transform.

I monitored the performance of the ads and disabled those that has poor click through or conversion rates and made variants of the more successful ones. Darwinian evolution for ads. I ended up creating 70 ads across 15 ad groups, targeting 50 communities.

I wasted an hour trying to get Reddit to recognize that I had installed their tracking pixel. But, overall, I found the Reddit Ads relatively simple to setup and monitor. Especially compared to the byzantine monstrosity that Google Adwords has become.

Reddit advertises a deal where you can get $500 of free ads.

But the link was broken when I clicked on it. Someone else I spoke to said they had tried to find out more, but gave up when they found out you had to have a phone call with a sales person at Reddit.

Results

I ran my experiment from 08-Jul-2025 to 31-Jul-2025. These are the stats, according to reddit.

Spend$851.04
Impressions490,478
Clicks3,585
Windows installs177
Mac installs63
Total installs240
Click Through Rate0.73%
Cost Per Click$0.24
Click to install conversion rate6.59%
Cost Per Install $3.55

I generally reckon that somewhere around 10% of people who install are going on to buy. So $3.55 per install would mean around $35.50 cost per sale, which is reasonable for a $99 sale. So that all looks quite encouraging.

But, comparing the Reddit number to the numbers I get from Google Analytics and my web logs, I think the Reddit numbers are dubious. At best. In a week when Reddit says it sent me 1174 clicks, Google Analytics says I received 590 referrals from Reddit and my web log says I received 639 referrals from Reddit. Some of the difference may be due to comparing sessions with clicks, time zones etc. But it looks fishy.

The discrepancy is even greater if you look at conversions. The total installs per week reported by Google Analytics and my web logs didn’t go up anything like you would expect from looking at the Reddit conversion numbers. If you dig a bit further, you find that Reddit uses ‘modeled conversions‘ to:

“Gain a more complete view of your ads performance with modeled conversions, which leverages machine learning to bridge attribution gaps caused by signal loss.”

Uh huh. Sounds suspiciously like ‘making shit up’.

And then there are the sales. Or lack of. I don’t have detailed tracking of exactly where every sale comes from. But I estimate that my $851 outlay on ads resulted in between $0 and $400 in additional sales. Which is not good, given that I don’t have VC money to burn. Especially when you factor in the time taken to run this experiment.

The top 5 countries for spend were:

  1. Italy
  2. Spain
  3. France
  4. Germany
  5. Singapore

The US only accounted for 0.28% of impressions, 13 clicks and $3.81 in spend. Presumably because the US market is more competitive, and I wasn’t bidding enough to get my ads shown.

You can look at various breakdowns by country, community, device etc. This is helpful. But some of the breakdowns make no sense. For example, it says that 41% of the click throughs from people reading Mac-related communities were from Windows PCs. That sounds very unlikely!

But the worst is still to come. Feast your eyes on this Google Analytics data from my website:

Average engaged time per active user (seconds)Engaged sessions per active user
Google / organic330.75
Successfulsoftware.net / referral310.74
Youtube.com / referral270.86
Chatgpt.com / referral240.69
Google / CPC160.65
Reddit / referral80.25

8 seconds! That is the mean, not the median. Yikes. And 75% of the sessions didn’t result in any meaningful engagement. This makes me wonder if the majority of the Reddit clicks are accidental.

I had intended to spend $1000 on this experiment, but the results were sufficiently horrible that I stopped before then.

If I had spent a lot of time tweaking the ad images and text, landing pages, communities and countries, then I could probably have improved things a bit. But I doubt I could ever get a worthwhile return on my time and money.

If the lifetime value of a sale is a lot more than $99 for you, or your product is a good fit for Reddit, then Reddit Ads might be worth trying. But be sure not to take any Reddit numbers at face value.

20 years working on the same software product

I released version 1 of my table seating planning software, PerfectTablePlan, in February 2005. 20 years ago this month. It was a different world. A world of Windows, shareware and CDs. A lot has changed since then, but PerfectTablePlan is now at version 7 and still going strong.

PerfectTablePlan v1

PerfectTablePlan v7

I have released several other products since then, and done some training and consulting, but PerfectTablePlan remains my most successful product. It’s success is due to a lot of hard work, and a certain amount of dumb luck.

I was getting married and I volunteered to do the seating plan for our wedding reception. It sounded like a relatively straightforward optimization problem, as we only had 60 guests and no family feuds to worry about. But it was surprisingly difficult to get right. I looked around for some software to help me. There were a couple of software packages, but I wasn’t impressed. I could do better myself! So I wrote a (very rough) first version, which I used for our wedding.

Things weren’t going great at my day job, at a small software startup. Maybe I could commercialize my table planner? I was a bit wary, as my potential competitors all seemed rather moribund and I didn’t think I would be able to make a living off it. But I thought I could do everything worth doing in 6-12 months and then start on the next product. Wrong on both counts!

Web-based software was still in its infancy in 2005. So I decided to write it as desktop software using C++ and cross-platform framework Qt, which I had plenty of experience in. Initially, I just released a Windows version. But I later added a Mac version as well. Qt has had its commercial ups and downs in the last 20 years, but it has grown with me and is now very robust, comprehensive and well documented. I think I made a good choice.

I financed PerfectTablePlan out of my own savings and it has been profitable every year since version 1 was launched. I could have taken on employees and grown the business, but I preferred to keep it as a lifestyle business. My wife does the accounts and proof reading and I do nearly everything else, with a bit of help from my accountant, web designers and a few other contractors. I don’t regret that decision. 20 years without meetings, ties or alarm clocks. My son was born 18 months after PerfectTablePlan was launched and it has been great to have the flexibility to be fully present as a Dad.

CDs, remember them? I sent out around 5,000 CDs (with some help from my father), before I stopped shipping CDs in 2016.

During the lifetime of PerfectTablePlan it became clear that things were increasingly moving to the web. But I couldn’t face rewriting PerfectTablePlan from scratch for the web. Javascript. Ugh. Also PerfectTablePlan is quite compute intensive, using a genetic algorithm to generate an automated seating plan and I felt it was better running this on the customer’s local computers than my server. And some of my customers consider their seating plans to be confidential and don’t want to store them on third party servers. So I decided to stick with desktop. But, if I was starting PerfectTablePlan from scratch now, I might make a different decision.

Plenty of strange and wonderful things have happened over the last 20 years, including:

  • PerfectTablePlan has been used by some very famous organizations for some very famous events (which we mostly don’t have permission to mention). It has seated royalty, celebrities and heads of state.
  • PerfectTablePlan was used as part of a demonstration of the (controversial) first commercial quantum computer by D-Wave.
  • A mock-up of PerfectTablePlan, including icons I did myself, was used without our permission by Sony in their ‘Big day’ TV comedy series. I threated them with legal action. Years later, I am still awaiting a reply.
  • I got to grapple with some interesting problems, including the mathematics of large combinatorial problems and elliptical tables. Some customers have seated 4,000 guests and 4000! (4000x3999x3998 .. x 1) is a mind-bogglingly huge number.
  • A well known wedding magazine ran a promotion with a valid licence key clearly visible in a photograph of a PerfectTablePlan CD. I worked through the night to release a new version of PerfectTablePlan that didn’t work with this key.
  • I found out that CDs are edible.
  • I sponsored the building of a kindergarten in Nepal.
  • I once had to stay up late, in a state of some inebriation, to fix an issue so that a world famous event wasn’t a disaster (no I can’t tell you the event).

The lowest point was the pandemic, when sales pretty much dropped to zero.

Competitors and operating systems have come and gone and the ecosystem for software has changed a lot, but PerfectTablePlan is still here and still paying the bills. It is about 145,000 lines of C++. Some of the code is a bit ugly and not how I would write it now. But the product is very solid, with very few bugs. The website and user documentation are also substantial pieces of work. The PDF version of the documentation is nearly 500 pages.

I now divide my time between PerfectTablePlan and my 2 other products: data wrangling software Easy Data Transform and visual planner Hyper Plan. Having multiple products keeps things varied and avoids having all my eggs in one basket. In May 2024 I released PerfectTablePlan v7 with a load of improvements and new features. And I have plenty of ideas for future improvements. I fully expect to keep working on PerfectTablePlan until I retire (I’m 59 now).

Inputting and outputting to Excel XLSX/XLS using the LibXL library

I needed a way to input from and output to Excel .xlsx and .xls file in my data wrangling software, Easy Data Transform. I had previously used Qt’s ActiveQt classes to talk to Excel via ActiveX, in my seating planner software, PerfectTablePlan. But this came with distinct limitations:

  • Excel must be installed on the customer computer.
  • ActiveQt only works on Windows.

I wanted to be able to read and write Excel file on Windows and Mac from my C++/Qt application, whether Excel is installed or not. I would rather commit suicide with a cheese grater, than try to write my own code to parse whatever horrific format Excel is written in. So I looked around for a library.

I ended up buying a licence for LibXL, from XLWare, back in 2019. It has been working great ever since. I now also use it in PerfectTablePlan v7.

Things to like:

  • Available as a library for Windows, Mac, Linux and iOS (I have only used it for Windows and Mac, so far).
  • Accessible from lots of languages, including: C, C++, .Net, Delphi, PHP, Python, PowerBASIC and Fortran.
  • Example code is available in C++, C, C# and Delphi.
  • Good support.
  • Regular updates.
  • Reasonable pricing.
  • No per-user fees.

The API is a little low-level for my taste, but I guess that is inevitable when you support C as well as C++. Reading and writing is slow compared to reading and writing the same data to/from a CSV file. But, no doubt, that is due to the limitations of the Excel file format.

I don’t have any affiliation with LibXL beyond being a paying customer, and I haven’t been asked to write this. I just wanted to give a shout-out to the developer, Dmytro, for his sterling work. Long may it continue.

Easy Data Transform v2

I released Easy Data Transform v2 today. After no fewer than 80 (!) v1 production releases since 2019, this is the first paid upgrade.

Major improvements include:

  • Schema versioning, so you can automatically handle changes to the column structure of an input (e.g. additional or missing columns).
  • A new Verify transform so you can check a dataset has the expected values.

Currently there are 48 different verification checks you can make:

  • At least 1 non-empty value
  • Contains
  • Don’t allow listed values
  • Ends with
  • Integer except listed special value(s)
  • Is local file
  • Is local folder
  • Is lower case
  • Is sentence case
  • Is title case
  • Is upper case
  • Is valid EAN13
  • Is valid email
  • Is valid telephone number
  • Is valid UPC-A
  • Match column name
  • Matches regular expression
  • Maximum characters
  • Maximum number of columns
  • Maximum number of rows
  • Maximum value
  • Minimum characters
  • Minimum number of columns
  • Minimum number of rows
  • Minimum value
  • No blank values
  • No carriage returns
  • No currency
  • No digits
  • No double spaces
  • No duplicate column names
  • No duplicate values
  • No empty rows
  • No empty values
  • No gaps in values
  • No leading or trailing whitespace
  • No line feeds
  • No non-ASCII
  • No non-printable
  • No punctuation
  • No symbols
  • No Tab characters
  • No whitespace
  • Numeric except listed special value(s)
  • Only allow listed values
  • Require listed values
  • Starts with
  • Valid date in format

You can see any fails visually, with colour coding by severity:

  • Side-by-side comparison of dataset headers:
  • Side-by-side comparison of dataset data values:
  • Lots of extra matching options for the Lookup transform:

Allowing you to do exotic lookups such as:

Plus lots of other changes.

In v1 there were issues related to how column-related changes cascaded through the system. This was the hardest thing to get right, and it took a fairly big redesign to fix all the issues. As a bonus, you can now disconnect and reconnect nodes, and it remembers all the column-based options (within certain limits). These changes make Easy Data Transform feel much more robust to use, as you can now make lots of changes without worrying too much about breaking things further downstream.

Easy Data Transform now supports:

  • 9 input formats (including various CSV variants, Excel, XML and JSON)
  • 66 different data transforms (such as Join, Filter, Pivot, Sample and Lookup)
  • 11 output formats (including various CSV variants, Excel, XML and JSON)
  • 56 text encodings

This allows you to snap together a sequence of nodes like Lego, to very quickly transform or analyse your data. Unlike a code-based approach (such as R or Python) or a command line tool, it is extremely visual, with pretty-much instant feedback every time you make a change. Plus, no pesky syntax to remember.

data wrangling

Eating my own dogfood, using Easy Data Transform to create an email marketing campaign from various disparate data sources (mailing lists, licence key databases etc).

Easy Data Transform is all written in C++ with memory compression and reference counting, so it is fast and memory efficient and can handle multi-million row datasets with no problem.

While many of my competitors are transitioning to the web, Easy Data Transform remains a local tool for Windows and Mac. This has several major advantages:

  • Your sensitive data stays on your computer.
  • Less latency.
  • I don’t have to pay your compute and bandwidth costs, which means I can charge an affordable one-time fee for a perpetual licence.

I think privacy is only going to become ever more of a concern as rampaging AIs try to scrape every single piece of data they can find.

Usage-based fees for online data tools are no small matter. For a range of usage fee horror stories, such as enabling debug logging in a large production ETL pipeline resulting in $100k of extra costs in a week, see this Reddit post. Some of my customers have processed more than a billion rows in Easy Data Transform. Not bad for $99!

It has been a lot of hard work, but I am please with how far Easy Data Transform has come. I think Easy Data Transform is now a comprehensive, fast and robust tool for file-based data wrangling. If you have some data to wrangle, give it a try! It is only $99+tax ($40+tax if you are upgrading from v1) and there is a fully functional, 7 day free trial here:

Download Easy Data Transform v2

I am very grateful to my customers, who have been a big help in providing feedback. This has improved the product no end. Many heads are better than one!

The next big step is going to be adding the ability to talk directly to databases, REST APIs and other data sources. I also hope at some point to add the ability to visualize data using graphs and charts. Watch this space!

Experiences promoting niche software

This is a guest post from fellow software developer, Simon Kravis.

It’s sometimes said that software development is only 10% of what’s required to earn money from software and I can attest to that. Since 2018 I have been developing photo captioning and related software, more as a retirement diversion than a serious source of income (after a career mostly involved in writing scientific and engineering analysis software), in the hope that sales income would at least cover running costs. My best marketing tool has been writing reviews of the class of software that I produce, and the hosting site (Hub Pages) provides some useful analytics on how often these are accessed and for how long. Below is the graph for an article on tagging.

The decline since early 2022 is hard to explain – the article is periodically updated so the steady decline is not due to diminishing ‘freshness’ – which for Google is probably a file Modified date.

Here is another review article profile (Scanning Multiple Photos) showing a similar decline:

But another (Best Photo Captioning Software) has held up, though at a low level.

I offer digital photo captioning software (Caption Pro) on Windows and Mac platforms, and an iPhone captioning app (CaptionEdit), with the Windows version dating back to 2017. I also offer part of the functionality of Caption Pro on Windows for auto-cropping scans of multiple paper photos (ImageSplit). On Windows neither Caption Pro software downloads or sales seem to correlate with review accesses, despite about 1/3 of web site accesses coming from the review. However, downloads do show some correlation with Caption Pro web site sessions, as shown in the graph below.

Sales do not correlate with downloads, which perhaps explains why most advertising for niche products is not successful – it may increase downloads but this does not appear to increase sales. The observed proportion of downloads resulting in sales for ImageSplit and Caption Pro are 6% and 9% respectively. The lack of correlation between sales and downloads may be due to the small number of sales per month, which results in random fluctuation dominating the results.

The decision to enter the Apple “Walled Garden” of software was partly at the prompting of friends rather than a commercial evaluation. Apple Developer membership (costing ~US$100 per year) is required to prevent software being blocked from installation through being from an unknown publisher. Further costs were purchasing a fairly modern Mac on which to perform development, as the App Store will only accept software developed using recent versions of the Xcode development environment, which will only run on fairly recent hardware. The App Store takes a commission of 15% on sales, which is quite reasonable when compared to the difficulty of implementing e-commerce on Windows, where a PayPal account eases the problem of low-value foreign-currency transactions, but e-commerce plug-ins may stop working after years for no discernible reason. The review process for software acceptance into the App Store is generally fast, but seemly trivial issues can require resubmission. Features which have passed one review may be rejected in a later one. The review process is generally fast, but on one occasion took 4 weeks.

Caption Pro for Mac has been available (via the App Store) only since Sep 2021.It appears within the top 6 results for a search using “Caption Photos”, which is the source for most downloads. About 3.5% of downloads result in sales. This figure is much less than the Windows version of the same app, despite Mac users’ reputation for being more willing to pay for software. The iPhone app did not appear at all initially when searching for “Caption Photos” in the App Store. After 6 months it began appearing as result number 140, after it had 360 downloads. This poor ranking performance is probably because “Caption Photos” is a very popular keyword used by many apps, including those that only caption videos. It has had very few downloads and sales, despite Apple Search Ads and Apptimizer campaigns. The number of downloads increased dramatically during the Apptimizer campaign between Jan 24 and Feb 2 (as they were purchased) but the change in ranking from these downloads did not result in any sales, perhaps because no installs were purchased. The Apple search ads campaign (which resulted in the app being shown as an ad 1 in 50 times when the search phrase “Caption Photos” was used) did not greatly affect downloads or sales. A Facebook ad campaign to show a link to the app whenever “Genealogy” or “Genealogy Software” was searched for was also unsuccessful, and very expensive, as Facebook charges by impressions rather than clicks. Additional backlinks to the web site were purchased in September 2022 from Links Management in an attempt to improve the web site Google ranking, but this did not appear to have any effect on web traffic.

Mac and Windows users contacting me with problems have had a wide range of experience level – from completely naïve to former programmers. Most have been from the US, which reflects the geographic distribution of sales. There have many downloads to non-English speaking countries but very few sales.

Some results from the Mac and iPhone Apps are shown below:

On balance, developing for Apple platforms was not a good commercial decision, as the advantages of a mostly captive audience (completely captive in the case of the iPhone) do not seem to result in higher rates of downloads or sales. Competition for iPhone apps is so intense that niche products without massive advertising budgets are unlikely to succeed. The same is likely to apply to Android phone apps, which anecdotally have a less rigorous review process. My experience is that advertising and backlink purchase for any platform are not effective in increasing sales for niche software.

Simon Kravis runs Aleka Consulting, a small software and consultancy company in Canberra, Australia specializing in information management and offering a number of software products. He has mainly developed scientific and engineering programs, starting in the era of paper tape.

Moving from altool to notarytool for Mac notarization

This is an update to my 2018 article How to notarize your software on macOS.

I have been using altool to notarize my Mac apps for some years. However Apple, being Apple, have deprecated altool in favour of the new notarytool. altool will stop working at some point in 2023. And Apple, being Apple, have made little attempt to keep consistency between the two.

I didn’t find anything online to tell me how arguments between the two tools related. Consequently I spent a while trying to guess which arguments mapped to which. I got locked out for a while for trying to wrong combination too many times. In the end I went from this:

xcrun altool -t osx -f <mydmg>.dmg --primary-bundle-id <com.company.product> --notarize-app --username <apple-account-email> --password <password>

... wait for approval email ...

xcrun altool --username <apple-account-email> --password <password> --notarization-info <RequestUUID>

To this:

xcrun notarytool submit <mydmg>.dmg --apple-id <apple-account-email> --team-id <teamid> --password <password> --verbose --wait 

On the plus side the --wait option doesn’t exit until the notarization is complete, which means you can easily do you whole build, sign and notarize process in a single script. Hoorah.

Note that you still need to run the ‘stapling’ step after notarization:

xcrun stapler staple -v <mydmg>.dmg

More details on notarytool arguments at:

https://keith.github.io/xcode-man-pages/notarytool.1.html

Winterfest 2022

Easy Data Transform and Hyper Plan Professional edition are both on sale for 25% off at Winterfest 2022. So now might be a good time to give them a try (both have free trials). There is also some other great products from other small vendors on sale, including Tinderbox, Scrivener and Devonthink. Some of the software is Mac only, but Easy Data Transform and Hyper Plan are available for both Mac and Windows (one license covers both OSs).

Easy Data Transform progress

I have been gradually improving my data wrangling tool, Easy Data Transform, putting out 70 public releases since 2019. While the product’s emphasis is on ease of use, rather than pure performance, I have been trying to make it fast as well, so it can cope with the multi-million row datasets customers like to throw at it. To see how I was doing, I did a simple benchmark of the most recent version of Easy Data Transform (v1.37.0) against several other desktop data wrangling tools. The benchmark did a read, sort, join and write of a 1 million row CSV file. I did the benchmarking on my Windows development PC and my Mac M1 laptop.

Easy Data Transform screenshot

Here is an overview of the results:

Time by task (seconds), on Windows without Power Query (smaller is better):

data wrangling/ETL benchmark Windows

I have left Excel Power Query off this graph, as it is so slow you can hardly see the other bars when it is included!

Time by task (seconds) on Mac (smaller is better):

data wrangling/ETL benchmark M1 Mac

Memory usage (MB), Windows vs Mac (smaller is better):

data wrangling/ETL benchmark memory Windows vs Mac

So Easy Data Transform is nearly as fast as it’s nearest competitor, Knime, on Windows and a fair bit faster on an M1 Mac. It is also uses a lot less memory than Knime. However we have got some way to go to catch up with the Pandas library for Python and the data.table package for R, when it comes to raw performance. Hopefully I can get nearer to their performance in time. I was forbidden from including benchmarks for Tableau Prep and Alteryx by their licensing terms, which seems unnecessarily restrictive.

Looking at just the Easy Data Transform results, it is interesting to notice that a newish Macbook Air M1 laptop is significantly faster than a desktop AMD Ryzen 7 desktop PC from a few years ago.

Windows vs Mac M1 benchmark

See the full comparison:

Comparison of data wrangling/ETL tools : R, Pandas, Knime, Power Query, Tableau Prep, Alteryx and Easy Data Transform, with benchmarks

Got some data to clean, merge, reshape or analyze? Why not download a free trial of Easy Data Transform ? No sign up required.