I am a digital marketer, strategist, speak fluent data and I’m also a front-end developer. This isn’t a common combination. It’s kind of like Black Tea and Strawberry, a rare but excellent combo that should be more availible. I’m based in Newcastle, Australia.

I started this blog in response to a deal I made with a friend. He’d buy me a beer, I’d launch a blog. 7th January 2015 I did exactly that. This blog has been around for a while now. I write occasionally.

Having worked at a few companies, led a large(ish) team and spoken to a wide range of people and at the odd event, I feel that there tends to be a lack of depth in the understanding of digital marketing. People struggle how to answer the why behind what they are doing and consequently, digital activities often tend to feel disconnected from business outcomes.

Working with data and drawing insights out of it really excites me. I believe it is the critical missing piece in many organisations. I’m the kind of guy that watched ‘The Great Hack’ film about Cambridge Analytica and was more impressed by their data-driven marketing rather than horrified by their violation of people’s privacy.

I am a passionate person. I have a lot of fun with my work and have a pretty dry sense of humour. I want to share my passion by writing informative and entertaining articles. I hope you enjoy my articles as much as I do writing them.

The man behind the blog. I am aware of how lame that is. This is a little preamble about me and why I am writing this. in short - because why not?

About Me - Josh Wayman

Hi, I'm Josh

There is no doubt about it, Generative AI is changing things. It is changing how we work, how things look, and how we approach things. We’ve got this new tool that is helping us be more efficient, express ourselves creatively and is fun!

I think it is great. I think it is great to the extent that I get made fun of in the office because I’ve made a gang of imaginary AI friends that live in our chat office and on Instagram.

However, it has made it incredibly easy to be average. I like asking Chat GPT to generate a brand for me and then having a laugh at how generic it is—but it is passable. It’ll lack the depth and insights a skilled professional would bring, but it’ll do the job for some clients. It’ll be average.

Anyone could create it now—for free. Why get a professional involved?

Generative AI, whilst impressive, can have this inherent lack of authenticity and humanness about it. I’ve seen some awful applications that are just off putting. Here are a few examples and how to avoid them:

Completely AI generated content: articles, blogs and comms

AI has made it so quick and easy to create content. Anybody can generate an article about anything within a few minutes—and they are. The internet has exploded with a torrent of this generic, artificial, border-line spam. I hate it. It makes googling for quality information so hard!

When generating entire articles you are not saying anything new, you are not delivering your unique value to your readers and you are not demonstrating your expertise. You are saying that writing something new is too hard and time consuming for you and your readers are not worth your time.

“But nobody will notice”, you tell yourself that, but people are smarter than you think. AI generated articles tend to be repetitive, verbose and very general in nature. Vague and general content is annoying at the best of times.

It also can be wrong. Hallucinations (as it is called) are a real problem with Generative AI, and unless you are proofing work on a topic on which you are already knowledgeable, you could publish false information without realising it.

You will not earn an audience from a stream of Ai generated junk. It lacks authenticity and your personality.

There are AI models that are getting better, getting more nuanced, more refined and more human-like. I might be wrong in time, but people like connecting with people which is something Generative AI can’t do (without making it up).

Putting yourself into the stories in the content is what someone connects with. Anybody can tell 3 reasons why eating too much sugar is bad, but only you can tell it in a way that you would.

People seek connection with other people. Generative AI won’t be able to do this in a genuine way.

I love to use Chat GPT to refine what I write, or expand on something I’m stuck on. I use it to augment my work rather than replace it. It takes me a lot of effort to keep my paragraphs short and snappy, Chat GPT is my personal editor. It’s created a lot of efficiency for me and improved my work.

Generative AI is best when you as a reader don’t realise it—which is very achievable. This is what you should be aiming for. You wouldn’t know where I’ve refined this article with Chat GPT.

Additionally, feeding Chat GPT or other Generative AI models your completed comms and then getting it to create other applications of it i.e. social posts (which you then review and edit) is a great way to streamline your workflow and scale your effort.

It’s too easy to create generic junk now and too many people are doing it. You are the opportunity.

I love making AI generate random images. I’m a crap drawer with a wild imagination. Generative AI was made for someone like me! Getting AI to visualise my crazy ideas is satisfying—from an ice-cream tiger, or a buff unicorn holding a gun or me fighting spam with a big sword in the cyber realm: an AI can generate it basically instantly. We’ve even got an 

However, most of the time the results, whilst amusing, look like an AI did it. The images are glitchy, fingers are weird, something isn’t quite right. The latest generative video from Sora by Open AI is incredible and looks almost real, but there is something not quite right about.

DALL.E 3 and Stable Diffusion are making some incredibly real and impressive images, but my LinkedIn feed is not an indicator of this.

Maybe proof your images first and count the number of fingers?

Seriously though, I’m not sure of the solution here. I understand the appeal. AI generated images are a great, cheap (

?) way of making images for content that you would otherwise have to put a lot of time and effort and money into.

I know the first step is definitely knowing the limitations and weaknesses—detail—fingers and hands in particular.

It is a good solution to the image problem, it just can look naff—which makes you think if it is a good solution? There are other tools that you can load your brand assets into and use it to create images, but that is a few too many steps for most people.

I’d personally be opting for generic unsplash photos over AI at this stage. It doesn’t feel as inauthentic as AI generated images. Some nice photos of yourself at a desk would probably be more effective if you have to have an image—think on my previous point, people connect with people.

If unsplash/stock photography is not your vibe, using AI to create stylised images that lack detail can look really nice. OpenAI do this on their site, they use a lot of colour gradients, or a painting style that uses thick brushstrokes rather than fine art. This is playing into AI's strengths.

At BEECH have wrestled with what to do for our new latest section. We’ve opted to just go with a generic image (stock). We felt it is more inline with our brand and site that way. 

Alternatively, a friend of mine uses exclusively AI images. He has developed a particular prompt describing an art style that he uses for all his images now. It's now easy for him to create images for articles that are consistent with the rest of those on his site—and they look great. He's playing to AI's strengths. 

I use Unspash on my blog, I have forever and it would look different if I change it now.  Do what works for you, be be intentional about it.

This is one I haven’t seen as often in my web travels, but it annoys me the most. A company offers chat as a way of connecting with them. Fantastic! The chat is just a bot, that isn’t smart, that just answers in vague FAQ links that you’ve already read through and has an inability to pass you onto a person.

In this instance, I would prefer not to talk to a real person. No connection required. But, I have a job that I want done and this chatbot thing is making it harder and a person is the only way forward.

Don’t do this. It isn’t helpful. Turn it off. Kill it. It just frustrates your customers/clients. I think waiting on a response to an email would be less frustrating. Or if you do give the thing the ability to look at the pages you’ve navigated to prior so it doesn’t recommend the same things every. Single. TIME.

The technology in this use case is very emergent. We’re in a transition stage from a chat bot replying with a pre-written response based on keywords, to an generative AI that could offer some semblance of understanding.

The technology is evolving rapidly and a good solution would be transformative for organisations with large customer service volumes—both for the organisation and customer.

If I could chat with a Bot (chat or voice [yep that is a thing]) that could understand and was empowered to solve my problem I would talk to them 100% of the time.

Imagine calling up a call centre and there was no wait, the agent knew the answer to all your questions, was clear in their responses and did the data entry correctly. You’d then believe in miracles right?

This is actually possible with the technology we have today. I’m just not sure if a platform exists yet that brings it all together. It wouldn’t have a free tier and you would have to take time implementing it, but it would actually enhance your customers’ experience with you.

At the moment those chat Bots suck and are not helping anyone.

I asked ChatGPT to proof this article and suggest a conclusion and I felt bad. I’ve been rather critical, but I have such big expectations and excitement for the future of AI.

When prompted well and worked with, generative AI can help create great quality stuff. And this is not limited to just text, for instance the tools rolling out in Adobe Photoshop is nuts (albeit a little rogue sometimes)!

When using generative AI be clever about it. Don't just jump in and start using generated images or copy because it is cool or efficient to. Do it because it will help you bring better value to your customers, or communicate your idea more effectively. Use it authentically.

Back to answer my original question: is AI revolutionising creativity or diluting authenticity? 

I think we're in for an explosion in creativity over the next few years. We're also going to see a lot of crap.

Is AI revolutionising creativity or diluting authenticity? | Josh Wayman

Is AI revolutionising creativity or diluting authenticity?

Segmentation is a powerful tool for fundraisers but it is something that I haven’t often seen done well and something that there is very little information out there on that isn’t locked behind a paywall or an agency.

I aim to publish a series of three posts about my thoughts and experience on segmentation, each increasing in complexity and sophistication. This is the first one—this one—is covering my absolute foundations. The second will be about reporting and the third will be approaching building out a complex segmentation model.

Segmentation in fundraising is the process of dividing up a supporter database based on a specified data criteria in order to more effectively communicate with them and improve campaign performance and ROI.

Each criteria should have a hypothesis as to why that criteria should work and how those people will respond–the hypothesis could be simply that these people won’t give. Hypotheses need to be tested!

Segmentation can be really complex or really simple, it is really up to the organisation/team.

The foundations of great appeal segmentation are:

Keep segments comparable between years and campaigns

Make it implementable – There is no shame in keeping it simple

Bonus: Track the supporters movements through them over time

Sending a printed letter to an entire database is expensive. A great way to save money is to just send it to less people! But a great way to raise more money is to send it to more. What to do? Oh the dilemma!

There will be people who have never responded in years to the type of campaign you are running—boom that’s a segment. It will probably cost more money to send a letter to these people than what you will raise (worth testing though!).

There will be people who always respond—boom another segment. You definitely have to send to these people.

It might be worth having a slightly different message for new people because they are new and special (probably not though, please test this)–yet another segment.

This group of people always respond to emails, it could be worth not sending them print, providing their email is opted in—oohhh a fancy segment!

Starting simple like this will help you begin to dial in the ROI of your campaign and mean you can justify sending to less people because you are sending to the theoretical right people.

Your database is not an amorphous blob, every supporter is different and would have a different data trail with your organisation. You may not have all the data in the one spot, but there is probably some data there that can help you segment your audience.

I made a $2 donation online to a charity a year ago and I’ve got every single appeal letter with all kinds of random crap in it (letter, magnet, postcard, post-it note, bookmark and more). Four appeals later, I have not made another donation and can see my donation has been totally chewed up by print goodies.

I don’t think there is a great understanding of what the segments are at this charity.

As I’ve donated within the last year I’d say I’m in the ‘new donor’ category, probably based on a created date somewhere. However I only donated $2, is it worth sending me everything? Probably not.

I’ve seen a number of charities now where the segmentation is done by a dark data wizard in a cave somewhere scary that the fundraisers approach only when absolutely necessary. If the fundraisers are clever they send in the sacrificial agency employee first with any request to see how they respond.

You don’t need to know how to weave spells of SQL in order to understand your segments, but so often they are treated this way. Segmentation is a black box and the wizard tells you it works so it does (or they tell you it is impossible).

As a fundraiser you should really understand who these groups you are communicating with are as it should change how you communicate or approach them. It is important to understand specifically who is in and who is out.

If you have a data wizard, get them to write a short description of each segment in plain english for you. If they grumble, tell them to use ChatGPT to help them–wizards love new shiny technology.

I don’t think the charity mentioned earlier understood their segmentation because if they did I don’t think they’d see the value in sending so much to someone who only donated $2.

The messaging really thanked me for the huge impact I’d made and blah blah blah. I donated $2, it cost you that much to send me this. This doesn’t make sense to me as a donor.

New donors are typically special, but have a dollar threshold as well as a date range as part of your segment.

More often than not segmentation is approached anew for each campaign or mailing. It bugs me as it’s not building on or often taking into consideration the previous years/campaigns results.

Ideally segments are refined for effectiveness each year, yet still kept comparable. Doing completely different segments each year for the sake of it is not ideal. You learn from one campaign, tweak and refine, then learn etc.

Comparisons year on year will really help you understand if your segments are working and will empower you to make smart decisions in future years/campaigns.

Once you have multiple years of data for a particular segment—response rates, average donation value, primary response channel etc—you will be able to accurately predict appeal outcomes and better understand your supporters’ giving behaviour. It’s amazing!

More insights lead to a better understanding which leads to smarter fundraising.

From my experience segmentation typically starts and ends with direct mail. The driver is effectively maximise ROI by reducing print expenditure. That is good, but you can do better—segment your whole database.

Segmenting your entire database and then looking at all the segments that responded may surprise you. The segment “These people will never ever respond” had 15 donations via email, interesting–why, let’s dig into it!

I’ve worked with a client and we segmented their database into 25ish different segments. The direct mail only went to the top 8 which were grouped into three separate packs. Here the segments were used to predict giving behaviour, the segments with the highest predicted giving got the print packs.

This way you can bring the segmentation into other channels like email and SMS too. I use it more as a tool to dial up or down frequency rather than a customised message.

I haven’t experienced much success with creating unique email sequences or dynamic content or customised emails per segment. Most often it is horribly time consuming and the juice is not worth the squeeze—but, please test it for yourself!

Make it implementable–there is no shame in keeping it simple

Your segments being implementable is the most important thing on this list. You may design the most comprehensive, brilliant model for your organisation, but if it can’t be implemented it doesn’t matter.

You may have technology limitations, old databases or database silos. However, the most limiting factor I’ve found is the skillset to translate a fundraiser’s ideas into a dataspec, then into the segments. Data wizards are rare.

There are tools out there that are quite limited, but it may be enough, something is better than nothing.

Talk to your IT/data people, try and understand what is possible and work with one another. IT/data people like to say things are impossible—some data wizards are in fact just sneaky data muggles—keep asking questions, ask why, push through the impossible, make them think, and find a solution that works for everyone.

 Track the supporters movements through them over time

How people move through segments overtime is an amazing datapoint itself. Are there patterns of movement? Is it related to tenure? Can you predict multi-year movement? Does this movement towards a net growth or decline in predicted income?

It isn’t something that I’ve seen come out of the box, but the possibilities that tracking segment movement over time yield excites me. It’ll help paint a better picture of people respond throughout the tenure of a supporter.

Not a fundamental, but something to consider if you are setting up segments now. Your future self will thank you!

My key takeaway is to utilise segments, understand what they are, make them comparable, think beyond print and make them implementable.

Fundraising segmentation 101: the basics

I didn’t think this day would come. I thought that Google Analytics 3 AKA Universal Analytics would always be a part of my digital world, I didn’t think it couldn’t be—but here we are.

I was extremely sceptical when the original sunset date was announced. I thought GA4 was nowhere near ready for mainstream and that the GA3 sunset would be pushed at least 6-12 months. I was wrong, the day is here and it has happened.

GA3 is dead and GA4 is still kind of confusing.

10 years ago(ish) was the first time I witnessed eCommerce tracking enabled in GA3 on a website—it was honestly life changing. The fact you could directly attribute income to a marketing activity (i.e. an email, a social post, a blog), and see the behaviour in between was a brand new paradigm.

The marketing I studied at university was largely conceptual, big picture and driven by opinion and anecdote. It was grounded in focus groups and self reported surveys, not data and immediately observable behaviour.

Assessing impact and performance would require marketing research, a brand index, or something else that is only accessible to those with deep pockets and then inclination care.

Google Analytics—a free tool—changed all of that. Shattered it.

Who cares if the industry is saying sending emails at 10am is the most effective when I can look at my own data and know that 8am is better.

Who cares if the big boss thinks this copy is the best when we can prove that an alternative is better. The fact some people don’t care about data was a more nuanced lesson I was yet to learn...

I remember previously being in arguments about button size and colour, or copy changes or position of stuff on a page and they would drag on, unresolved. Now we could just say we’d test it and pick what performed best (often without any kind of statistical significance mind you).

Everyone wanted the best outcome, so this was an easy option that was previously nonexistent.

Marketing, in my mind, could now become a science. And for me, it did.

Everywhere I’ve worked since I’ve strived to bring about a curious, data driven and informed culture. I’ve spruiked the value of a well implemented GA to anyone that would listen. I’ve pushed people to reflect, learn, and be better, because we now knew what was possible.

Google Analytics transformed the way I approach my work and totally changed how I think about things. No other tech platform has consumed more of my time, inspiration and passion than GA3.

I understand the benefits of GA4 and I understand why there needs to be change, but I will sorely miss GA3. 

I’ll miss the familiarity of the reports, the quirks and getting lost in drilldowns and even the moments when you accidentally click the bubble chart diagram button.

Thank you and goodbye Google Analytics 3—I'll miss you.

Thank You and Goodbye Google Analytics 3 | Josh Wayman

Thank You and Goodbye Google Analytics 3


I love cutting edge web analytics and finding ways to better track and understand people's online behaviour. As this can seem like an 'ethics optional' field at the moment I thought I would lay down a few ground rules.

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I will use Cookies and Javascript and whatever else I can find to track usage behaviour

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I aim to use this website as a testing ground for new ideas and to publish everything interesting I find right here on it.

If you have any concerns please tweet me at @joshwayman and I will try and clarify anything.

This is a privacy policy guys. Nothing fancy here.

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Keep reading

Is AI revolutionising creativity or diluting authenticity?

Fundraising segmentation 101: the basics

Thank You and Goodbye Google Analytics 3