Papirfly unveils seamless integration with Ungapped
Fredrik Davidsson
2minutes read
In an exciting development for marketing and brand professionals, Papirfly, the renowned brand management platform, has announced an integration with Ungapped, the user-friendly communication platform equipped with a variety of marketing tools.
This collaboration promises to streamline brand consistency and enhance user experiences for both Papirfly and Ungapped users.
A hub for marketing excellence
Ungapped is a Swedish digital communication platform that caters to various marketing needs, including email marketing, SMS campaigns, event management, surveys, and marketing automation. The platform is also hosted on GDPR-compliant servers. The new integration allows users to easily pull assets from Papirfly’s Digital Asset Management solution, Place, and integrate them seamlessly into their marketing campaigns in Ungapped.
The integration between Papirfly and Ungapped introduces the ability to find, select, and retrieve assets from Place directly within Ungapped. This integration web plugin bridges the gap between brand management and marketing automation, making it easier than ever to maintain brand consistency throughout marketing materials.
How the integration benefits marketing users
Seamless asset retrieval: Users of Ungapped can now seamlessly access assets stored in Place through Papirfly’s integration. This streamlines the workflow, ensuring that marketing materials are consistently on-brand.
Time efficiency: With the integration to Papirfly’s Place, marketers can save valuable time by eliminating the need to switch between platforms. This streamlined process allows them to focus on creating engaging on-brand content and campaigns.
Brand consistency: Maintaining brand consistency is paramount for businesses. The integration ensures that all assets used within Ungapped adhere to brand guidelines, promoting a cohesive brand identity.
Enhanced user experience: Users of both platforms will benefit from the convenience of this integration, resulting in a more user-friendly experience and greater productivity.
In a world where branding and marketing are more critical than ever, the Papirfly-Ungapped integration represents a significant step forward. It empowers businesses to manage their brand with precision while efficiently executing marketing campaigns. With this partnership, users can look forward to a future where brand consistency and marketing automation seamlessly coexist, ultimately driving success and growth for their organisations.
AI beyond the hype – Staircases to (AI) Heaven and Hell
Craig Unsworth
4minutes read
We’ve looked at a number of areas where Artificial Intelligence will drive real and meaningful change in this AI-Illuminate series. We’ve looked at how Hollywood has convinced us of eternal doom, we’ve considered how machines will rid us of meaningless tasks, and we’ve discussed ways that Machine Learning might not build Society 2.0 as equitably as we’d like.
But as with all situations, there are two sides to the coin.
The Staircase to (AI) Hell
Let’s start with the depressing take on the journey ahead. Introducing the Staircase to (AI) Hell.
Beginning with ‘simple automations’ doesn’t feel that scary. Human beings are inherently lazy, we don’t generally like repetitive things, and if there’s a faster way to do something we’ll usually opt for it. Enter the robots! It’s easy to envisage a world where anything even remotely repetitive is simply done by a machine.
Even as we move to the next ‘step’ of the staircase, and we start to see some ‘low priority’ jobs being replaced, most people have little-to-no concern yet. Perhaps because most people discussing the AI debate right now consider their own jobs to be higher priority.
As we approach the step where Deep Learning can do a lot of things better than humans, we end the ‘light blue’ section of this staircase and start entering darker territories.
The first real grey area is the point in time where Deep Learning transfers billions of tasks from humans, replacing hundreds of millions of jobs. We’re no longer ‘just’ talking about the jobs most people think are not theirs – we enter a period of wholesale change with white-collar and blue-collar jobs equally threatened. What do the newly-unemployed do? How do they survive?
As we enter the ‘dark blue’ steps of the staircase we see General Artificial Intelligence (AGI) surpassing most abilities of most humans, which then leads to the point of ‘Singularity’ where machines become too powerful for their human creators to control.
At this point it really is humans vs. robots and, by all accounts, we don’t look set to win.
It is somewhat depressing.
Who can save us?
The Staircase to (AI) Heaven
I believe we can, as is common with many of the debates around AI, look to the past for our saviour. Isaac Newton to be precise. In Newton’s Third Law, he stated that for every action in nature there is an equal and opposite reaction. So perhaps we can turn the Staircase to (AI) Hell into a Staircase of (AI) Heaven? What could that look like?
Well, in Newton-friendly terms, it’s equal and opposite. When we flip the pyramid upside down, we start with the same simple automations that help us humans not have to do the boring things we don’t want to do. This, in itself, can only be a good thing. More efficiency can definitely help us focus on other things. It could likely also be part of the solution to some of our big global problems like waste and the distribution of equitability.
As we progress through the next two steps, there’s a positive to each too.
Complex AI replacing ‘low priority’ jobs is fine if what we mean by ‘low priority’ jobs are jobs humans are not very good at, where it’s dangerous to their health, or where we expend resources doing things unnecessarily. As long as we start to migrate those same displaced people into new, better, roles and / or find ways to replace their income.
Likewise, where AI can do things better than humans, let’s use AI. Of course it makes sense. If a machine is 10x more accurate at doing something, let the machine do it. Where a human+machine combination excels, like in the visual detection of some cancers, then let’s make it happen. Again, we just must not forget to plan for the displaced. Is it time to look at Universal Basic Income (UBI) models again, for example?
Where that displacement of jobs becomes wider and deeper, we do need to be ready. If AI is set to change a billion jobs within the decade, as some academics predict, our policymakers, lawmakers, and politicians need to be working on Plan B now. If we are to leverage the opportunity of the technologies we have created, we need to be ready.
We’ve been here before. The agricultural revolutions of the 19th Century – forever changing what farm labour looked like through the introduction of machinery – are the very reason we’re all able to sit here and read this article when we’d otherwise be out bringing in the harvest so our families could eat. I love the countryside but I’m very grateful for the historic jobs displacement that means I don’t need to grow my own wheat every year.
Daring to dream of AI’s future
The next few steps on the Staircase to AI Heaven are not filled in yet. We don’t know what the future will hold – but we can dare to dream…
Eradicating waste. Making human error a thing of the past, being able to predict things perfectly. Transforming health outcomes. Increasing quality of life for everyone. Curing cancer. Moving beyond fiat money. Driving equitability. Understanding where we come from. Closing the income gap. Working globally as one. People living longer. The end of discrimination. Solving the climate crisis.
It might not all be possible – and certainly not within our lifetimes – but it’s a wonderful AI Heaven to believe in.
In part 3 of this series we explored the Three Pitfalls of AI – privacy, replication, and bias. Each pose significant threats to how we live and work today, barriers to mass adoption of machine intelligence, and complex questions about safety and regulation.
That said, the opportunities AI promises are equally significant. We are likely at the start of a fourth industrial revolution and – even if we don’t know exactly how yet – artificial intelligence is going to change a lot (if not everything).
Operating in a new world without new regulation, the onus on companies like Papirfly – building the technology of tomorrow – to self-regulate becomes critical. Good corporate citizenship, acting responsibly, and pursuing opportunities ethically all require guidance and leadership.
To help our people make promises to our customers and our users about how we’ll build our software, we have created our Ethical Charter.
Comprising eight action statements within four ethical themes, it governs how we – as a company – will build technology, and it sets out a pledge for how we will put users at the heart of doing so.
Today we publish it openly.
Papirfly’s Ethical Charter
Be a good corporate citizen when it comes to the rightful privacy of our users
1. We must always obey local, regional (including GDPR), and international privacy laws. Beyond the question of legality, we must always treat users ethically too. This includes creating AI applications that do not invade their privacy, do not seek to exploit their data, do not collect any data without express (and understood) consent, and do not track users outside of our own walled garden. We do not need data from the rest of their activities, so we should not seek to obtain and use it.
2. We do not, as a hard rule, use data to create profiles of our users to facilitate negatively scoring, predicting, or classifying their behaviours. We must never use their personal attributes or sensitive data for any purpose. Neither of these tactics are required for us to make better software for them (which is what we are here to do) and so it is inappropriate. We must always understand where our ethical red line is and ensure everything we do is on the correct side of that.
Ensure we act in an unbiased manner – always – as we’d expect to be treated too
3. We acknowledge that there can be unacceptable bias in all decision making – whether human or machine based. When we create AI applications we must always try to eliminate personal opinion, judgement, or beliefs; whether conscious or otherwise. Algorithmic bias is partially mitigatable by using accurate and recent data so we must always do so. Remember, a biased AI will produce similar quality results as a biased human – “garbage in, garbage out” applies here, always.
4. We must use AI to augment good and proper human decision making. We do not want, or need, to build technology to make automated decisions. As in other areas of our business, like recruitment, we have not yet proven the strength of affirmative action (sometimes called positive discrimination) and, so, mathematical de-biasing is not considered an option for us. As such, all decision-making inside any application must include humans. Their skillsets, experience, and emotional intelligence can – and should – then be added to by AI.
5. We work to the principle of “you get out what you put in” and understand that in order to build technology for the future we can neither only look to the past (using out of date data, for example) nor build AI on top of existing human biases. Gender, ethnicity, age, political and sexual orientation bias (this list is not exhaustive) are all discriminatory and we must proactively exclude this human trait of today and yesterday in our search for technology solutions of tomorrow.
Build in the highest level of explainability possible, because output is important
6. We are not interested in only building black box solutions. If we can’t create defendable IP without doing so then we’re not doing our jobs properly. We want to, wherever possible – and always when possible – be able to explain, replicate, and reproduce the output of a machine we have built. We owe this to our users and it’s also how we’ll get better at what we do. The better we understand what we are building the quicker we can evolve it.
7. We actively subscribe to the “right to explanation” principle championed by Apple, Microsoft, and others. We must build applications that give users control over their personal data, determine how decisions have been made, and be able to easily understand the role their data has in our product development. We can do this without affecting our ability to defend our IP and, therefore, should do so as a default. Whilst full replication is not always possible (within deep neural networks, for example) our mission – and policy – is to do as much as we feasibly can.
Overall, our task is simple – we must build technology that is designed to do good
8. Technology is a wonderful and powerful thing. As a software company, we must believe that. But behind any, and every, application for good there are usually opportunities for evil too. As we depend more and more on AI it will take on a bigger role inside our organisation. As we craft and hone it, it is our responsibility to put ethics at the forefront and build responsibly. For now, we are our own regulators. Let’s be the best regulators we can be.
Moving forward with ethics at the heart of AI innovation
At Papirfly we have defined an Ethical Charter that governs what technology we build, how we build it, and the ethical parameters within which we build it. In Part 5 of this series we’ll provide a comprehensive analysis of various scenarios related to AI, highlighting both the benefits (“heavens”) and potential drawbacks (“hells”). This balanced perspective will present a clear view of AI’s capabilities and limitations.
You don’t have to look far to feel the air of AI-scaremongering. Robots coming for our jobs, AGI (General Artificial Intelligence) surpassing human ability, reaching the point of ‘Singularity’ – the hypothesis that AI will become smarter than people and then be uncontrollable, and the end of humankind as we know it. Movies like The Terminator, Minority Report, and Ex Machina have Hollywoodified Earth’s surrender to technology for years now.
When you ask corporate leaders about the risks AI present to their organisations, you tend to get similar answers based on similar themes:
What about copyright and intellectual property?
What about job displacement and human capital?
Are we at the start of a machine-led world?
Are we at risk of machine intelligence replacing human intelligence?
How do we compete against infallible machine intelligence?
How do we move fast enough to mitigate the risk of being out-run?
Who wins in the end – us, our competitors, new entrants, or AI generally?
Who can be trusted to regulate this new world?
Each of these questions can be unravelled to create opportunity alongside risk, but many leaders currently find it hard to differentiate. There is so much noise. There is still a talent gap – it’s estimated we need 10x the computer science graduates we have today in order to meet the hiring plans already announced by major software companies. The rapidity of change is increasing faster than existing operational models, such as fiscal years or quarterly reporting, were designed for.
This means, in all likelihood, that we must look elsewhere for a rational and unencumbered view. Let’s look at what academics have reached agreement on.
Academics don’t routinely agree with each other but for the past decade thought leaders from the biggest and best technology education institutions (including Massachusetts Institute of Technology [MIT], University of Oxford, Stanford University, Indian Institute of Technology, National University of Singapore, etc) have settled on the Three Pitfalls of AI as being Privacy, Replication, and Bias.
Privacy
Defined as the ability of an individual or group to seclude either themselves (or information about themselves), and thereby be selective in what they express, privacy is a phrase we’re all familiar with. The domain of privacy partially overlaps with security, which can include the concepts of appropriate use and protection of information.
But the abuse of privacy can be more abstract. Consider the (existing) patents that connect social media profiles with dynamic pricing in retail stores. The positioned use case is usually a discount presented to a shopper because the retailer’s technology knows – from learned social media data – that they are likely to buy if presented with a coupon or offer. This feels win-win for both parties and therefore the data shared feels like a transactional exchange rather than a privacy intrusion.
However, where that same technology can be used to inflate the price of a prescription for antidepressants – because the data tells the retailer’s system that the shopper is likely struggling with their mental health – it quickly becomes apparent that the human cost of privacy abuse could be very high indeed.
Privacy is considered one of the three pitfalls of AI because data (and so often personal data) is so intrinsically linked to machine intelligence’s success. The conversation around who owns that data, how that data should / should not be used, how to educate people about the importance of data, and how to give users more control over the data has been happening in pockets (but far from all) of society for a long time. As AI advances, it’s widely acknowledged that this area has to evolve in tandem.
Replication
The inability to replicate a decision made by Al – often referred to as a ‘black box’ – occurs when programmers and creators or owners of technologies do not understand why their machine makes one decision and not another.
Replication is essential to proving the efficacy of an experiment. We must know that the results a machine produces can be used consistently in the real world, and that they didn’t happen randomly. Using the same data, the same logic, and the same structure, machine learning can produce varying results and / or struggle to repeat a previous result. Both of these are problematic – and can be particularly troublesome when it comes to algorithms trained to learn from experience (reinforcement learning) where errors become multiplied.
The ‘black box’ approach is often excused by claiming IP protection or ‘beta’ status of products. But the prolonged inability to interrogate, inspect, understand, and challenge results from machines leads to an inability for humans to trust machines. Whether that’s confusion about how a lender has credit-scored your mortgage application, or something even more serious like not being able to prove that a prospective employer has used machine learning to discriminate against a candidate.
Replication is considered one of the three pitfalls of AI because we need to know we can trust AI. For us to trust it we need to be able to understand it. To be able to understand it we need to be able to replicate it.
Bias
“A tendency, inclination, or prejudice toward – or against – something or someone” is how bias is usually defined. Today, Google has more than 328m results when you search for “AI bias”. Unfortunately AI and bias seem to go hand-in-hand with a new story about machine intelligence getting it (very) wrong appearing daily.
As the use of artificial intelligence becomes more prevalent, its impact on personal data sensitive areas – including recruitment, the justice system, healthcare settings, and financial services inclusion – the focus on fairness, equality, and discrimination has rightly become more pronounced.
The challenge at the heart of machine bias is, unsurprisingly, human bias. As humans build the algorithms, define training data, and teach machines what good looks like, the inherent biases (conscious or otherwise) of the machines’ creators become baked-in.
Investigative news outlet ProPublica has shown how a system used to predict reoffending rates in Florida incorrectly labelled African American defendants as ‘high-risk’ at nearly twice the rate it mislabeled white defendants. The system didn’t invent this bias – it extrapolated and built upon assumptions programmed by its creators.
Technologists and product leaders like to use the acronym GIGO – ‘Garbage In, Garbage Out’ – and it absolutely applies here. When we train machines to think, all of the assumptions we include at the beginning become exponentially problematic as that technology scales.
Replication is considered one of the three pitfalls of AI because technology is often spoken of as being a great ‘leveller’, creating opportunities, and democratising access. But so long as AI bias is as bad as, or worse than, human bias, we will in fact be going backwards – with large sections of society disadvantaged.
Responding to AI’s challenges
Each of these Three Pitfalls of AI are serious and they have attracted a lot of attention – including from the leaders of the very companies at the forefront of AI’s development and evolution. When more than 1,100 CEOs wrote the now-infamous open letter calling for a halt to AI development they were essentially asking for time for humans to catch up and think about the possible consequences of our actions.
There are further questions about regulation – with lawmakers struggling to keep up with the rate of change. Trust of politicians remains stubbornly low globally and the public is also hesitant to trust a small group of technology company billionaires with what realistically could be existential threats to parts of how we live today. But self-regulation is where we are at currently and that means it comes down to individual technology creators to build responsibly and ethically.
At Papirfly we have defined an Ethical Charter that governs what technology we build, how we build it, and the ethical parameters within which we build it. In Part 4 of this series we’ll share our Ethical Charter and demonstrate how it works in our company.
AI beyond the hype – “AI was invented in December 2022…right?”
Craig Unsworth
6minutes read
Cast your mind back to the end of last year and think about your Instagram, Facebook, and Twitter (as it still was) feeds. If they were anything like mine they were likely full of friends’ AI-generated photos. Or, at least, the ones that made them look smarter, prettier, taller, thinner, etc. Generative AI had exploded into the mainstream.
You could be forgiven for thinking it was invented around then too – and are possibly surprised to know that Artificial Intelligence is as old as the aunties and grandmothers who asked you about it around the dining table at Christmas 2022.
Putting AI to the test
AI is around 70 years old. Its roots can be traced back to Alan Turing (of ‘The Imitation Game’ fame), the British WWII codebreaker. Turing was a leading mathematician, developmental biologist, and a pioneer in the field of computer science. His earliest work created the foundations for AI as we know it. His eponymous test, The Turing Test (created in 1950), tests a machine’s ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.
Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another.
The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine’s ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine’s ability to give correct answers to questions, only on how closely its answers resembled those a human would give.
A thought experiment
The Golden Age of AI followed, spanning roughly 1956-1976. During this period, scientists and researchers were optimistic about the potential of AI to create intelligent machines that could solve complex problems by matching human intelligence – or even surpassing it.
Whilst the era fizzled out it delivered many a ‘first’ that still holds value today. ChatGPT’s ‘great-great-grandmother’ could be considered to be ELIZA – one of the first chatbots (then called ‘chatter bots’) and an early passer of The Turing Test, which was created from 1964 to 1966 at MIT by Joseph Weizenbaum.
Moving into the next decade, John Searle (a prominent American philosopher) set the tone with his Chinese Room Experiment theory. Searle proposed the Chinese Room Experiment as an argument against the possibility of Al, aiming to illustrate that machines cannot have understanding.
Searle uses the following scenario to demonstrate his argument:
“Imagine a room in which a man, who understands no Chinese, receives, through a slot in the door, questions written in Chinese. When he receives a question, the man carefully follows detailed instructions written in English to generate a response to the question, which he passes back out through the slot. Now suppose the questions and responses are part of a Chinese Turing Test, and the test is passed”.
Chess and penguins
The years that followed this ’downer’ of a start to the 1980s were low in ambition and confined to what we now look back as ‘Behavioural AI’. Knowledge based systems, sometimes called ‘expert systems’, were trained to reproduce the knowledge and / or performance of an expert in a specific field. They mostly used the “if this then that” logic flow and they didn’t always get it right – with the identification of penguins (birds but flightless birds) being an oft-cited example of basic errors of the time.
This era produced a few big wins – especially in the efficiency space, like Digital Equipment Corporation’s ‘RI’ application which saved it $40m per year by optimising the efficiency of computer system configurations. But it was prior to the advances of computerised automation which really made corporate adoption commonplace. It’s also acknowledged to be the period of time that birthed the first bias in AI.
A lot happened in the world of AI in the 1990s – seeing major advances in defence, space, financial services, and robotics. So it’s perhaps surprising that most AI historians and computer scientists point to the same turning point for machine intelligence. In 1996 ‘Deep Blue’, a chess-playing computer from IBM, beat then-champion Garry Kasparov. Prior to this, chess had been singled out as a ‘frontier’ for machine vs. human intelligence, with many people believing the human brain to be the only one capable of mastering a game with between 10¹¹¹ and 10¹²³ moves. (A ‘googol’, being the inspiration behind Google’s name, is 10 to the 100th power, which is 1 followed by 100 zeros). Machine intelligence had arrived.
A new era emerges
As the use of computers in domestic settings proliferated, there was an exponential surge in Internet usage in the mid-1990s, with the last few years of the decade renowned today for the dotcom bubble (1995–2000) and its ultimate implosion. Throughout this time AI took a backseat in social contexts, despite already starting to power many consumer applications and early-version software, websites, and applications. Commercially the focus was on automation and efficiency. Neither of which were particularly “sexy” or fun.
Enter…the self-driving car. A longheld obsession and science fiction staple, the period between March 2004 and October 2005 was to become the start of a whole new age. The DARPA Grand Challenge was a competition for autonomous vehicles funded by the Defense Advanced Research Projects Agency, the research lead within the United States Department of Defense. The race saw 21 teams, each with their own self-driven vehicle, prepare to compete in a race spread out over 150 miles / 240km.
A grand total of zero entrants finished the race in 2004. But in the 2005 race, five vehicles successfully completed the course. Of the 23 entrants, all but one surpassed the 7.32 miles / 11.78 km distance completed by the best vehicle in the 2004 race. The winner on the day was Stanley (named by its entrants, the Stanford Racing Team) but the overall winner was AI itself, with optimism levels rallying and the machine intelligence conversation building in reach and volume.
Humanoid robots and sci-fi dreams
In the late 2000s, AI entered its ‘modern era’. A number of humanoid robots brought AI closer to science fiction, driverless car projects became abundant, AI was being built into consumer and commercial applications, and the Internet of Things (IoT) emerged – with the ratio of things-to-people growing from 0.08 in 2003 to 1.84 in 2010 alone.
The 2010s were really where we saw mass proliferation of AI in society. When we think about the mainstream tech we take for granted today much of it was born (or matured) in this decade. Virtual assistants like Siri. Machine learning tools. Chatbots capable of human-quality conversation. Mobile phone use cases. Photography aids. In-car innovations like satnav and cruise control. Smart watches. Smart appliances. Real-time share trading platforms that everyone can use, not just financial giants. Even the humble product recommendation engine. They all use AI.
We arrived in to the 2020s with 70 years’ build-up in artificial intelligence, machine learning, and deep learning. The past few years have seen significant advances and the next few will undoubtedly too.
Embracing AI’s evolution
When you next ask ChatGPT to write that report for you, when you use Papirfly’s Generative AI to create hundreds of illustrations in an instant, or you think about the potential pitfalls of AI (we’ll address these in an upcoming article) do remember that we’re not dealing with a brand new toy here.
Instead, we are working with technology that started its journey in the 1950s. A journey that has seen an amount of change its early creators could never have predicted in their wildest dreams, and one that is likely to transform almost every aspect of human life in the next decade.
Managing GDPR compliance in Digital Asset Management – the role of a GDPR manager tool for handling consent
Papirfly
3minutes read
DAM products and their relation to data management and GDPR
As businesses become increasingly reliant on digital assets, managing data and ensuring its privacy has become more important than ever. One of the ways companies are doing this is through digital asset management (DAM) systems, which can store and organise vast amounts of digital files. However, as these systems become more sophisticated, they can also become more complex, and managing the privacy of the data stored within them can be a significant challenge. This is where a GDPR manager comes in.
The GDPR, or General Data Protection Regulation, is a European Union law that sets out guidelines for the handling of personal data.
This law applies to any company that processes personal data, regardless of whether it is located within the EU or not.
The GDPR manager is responsible for ensuring that a company’s DAM system is in compliance with this law.
Digital asset management (DAM) systems are becoming increasingly important for businesses that deal with large amounts of digital files. However, with this increased use comes a greater need for data privacy and compliance with regulations such as the General Data Protection Regulation (GDPR). To address this need, some DAM systems include a GDPR manager tool that allows companies to manage consent for individuals who appear in photos uploaded to the DAM.
How Papirfly’s DAM can ensure company compliance to GDPR
The GDPR manager in our DAM enables users to create “photo sessions” and handle consents from the individuals depicted in the photos. These individuals are referred to as “data subjects” under the GDPR. The GDPR manager tool allows users to store information about data subjects and their consent status, providing a consent overview that displays a list of all persons who have confirmed consent or are pending.
The GDPR manager’s primary responsibility is to ensure that the company is collecting and storing personal data in compliance with the GDPR. This means that data must be collected for a specific purpose and individuals must be informed about the collection and use of their data. The GDPR manager tool allows users to easily manage consent and ensure that data subjects are aware of how their personal data will be used.
The GDPR manager tool also allows users to monitor the consent status of data subjects and track any changes in consent over time. This is particularly important in cases where individuals withdraw their consent, as companies must ensure that the data subject’s personal data is removed from the DAM system in a timely manner.
Finally, the GDPR manager tool can be used to provide training to employees on GDPR compliance as it relates to the handling of personal data in the DAM system. This may involve providing guidance on how to manage consent and how to respond to requests from data subjects regarding their personal data.
GDPR compliance is key in digital asset management and consent management
The GDPR manager tool in our DAM plays a crucial role in ensuring that companies comply with the GDPR when handling personal data. By providing an easy-to-use tool for managing consent, tracking changes in consent status, and providing training to employees, companies can ensure that they are protecting the privacy of data subjects and avoiding any potential breaches of the GDPR.
A template is not just a template – ensuring your brand can evolve over time
Natalie Wilding
5minutes read
A template is not just a template
A “template” can cover a lot of area and be as simple or as complex as you need it to be. It can be a simple “change the name and address” ad all the way to using advanced layout engines to make multi-page documents of various sizes and pull information and images from integrated databases.
Pay now or pay later
Everything is a compromise and has trade-offs. Simple templates can be made in minutes by a designer, but more complex, bespoke templates can require over one hundred hours for a developer to create in order to implement all the logic. So when does spending 100x more time on a bespoke template make sense?
A simple template can produce simple outputs — there can be variation in content, but little more. A complex, bespoke template can handle many variations in centrally controlled messaging, languages, colours themes, brands, layout logic and sizes. Using the formula of (conservatively) five of each possible variation, a complex, bespoke template can output 5^5 variants (i.e. 3125 variants)! Even just a handful of colour variations combined with sizes can output 20-30 variations, making the numbers of hours to implement quite valuable.
By investing in and utilising complex, bespoke templates, you have the ability to update the templates as your brand evolves over time, ensuring that all of your present and future templates are consistently up to date. This feature provides an added level of simplicity and a return on investment, making it easier to maintain brand consistency and coherence across all of your materials, while giving you time back on creating simple templates for every possible variation.
Production time is also time spent
It’s the total cost that counts, and while you may be saving on template creation, that helps little if those savings result in more time required by end users (who are potentially not designers) to actually create collateral. Many simple templates can do the same job as a more flexible bespoke one, but what are the trade-off?
Imagine you create a few social media (SoMe) posts for Facebook and LinkedIn. Using simple templates for either, you can quite easily copy and paste the text, choose the same image, and crop the image to fit the size requirements defined by Facebook and LinkedIn. The post will be done in less than ten minutes, but will require the knowledge of how Facebook and LinkedIn limit posts (in terms of size, length of text, etc.). If you compare that process to a flexible, bespoke template where you can select new size (already set-up with the size requirements of the SoMe platform), verify that the text still fits, and “save as new”. This process was done in less than a minute.
While each individual task does not save that much time, everything counts in large amounts. With an organisation that produces hundreds of SoMe posts per week across all locations, the savings become significant. Not only are you getting a significant return on investment, you’ll have happier, more efficient employees who will have spent less time on cumbersome processes and more time on more important work.
Papirfly goes above and beyond by offering an additional time-saving feature: continuously and proactively monitoring sizes of various social media channels. With this approach, you can rest assured that all of your social media templates are consistently up to date, ensuring that your brand is always represented accurately and consistently across all channels.
Create templates without InDesign
Everyone starts their exploration of templating systems somewhere, and it’s natural to assume that creating templates from InDesign or similar design tools is the best way to go. In some cases that would be correct – e.g. if your need is to produce a vast quantity of very different templates that need only a few edit options, and you have designers that know your brand well.
Another appropriate use case for a design tool like InDesign may be that you need to provide end users with many SoMe and/or print templates, where the content — text and imagery — varies. In this case, using one bespoke template to create all needed variants could be the quickest way to reach the goal. Creating content variants with Papirfly’s Create & Activate product (and the amazing products we can wrap around it) is literally as easy as editing a document, where an administrator (non-designer) can easily create a content variant in minutes. The best part is that the end user gets the same quality, bespoke user experience (UX) in each and every one of these templates.
If you require more control over how these templates are used (e.g. locking the background image or heading), we provide solutions for this as well. Template creators can have additional granular controls that let them lock elements down, to the extent that even which parts of the element are editable. An image element can, for instance, be set to fill its frame and the end user can only select images from a specific set of predefined backgrounds. When the template creator has approved the final version of the template, the template is immediately available for the end user.
So what about InDesign?
An InDesign document is a static design, but allowing for different amounts of text and various images requires careful thought and design, as design for dynamic content is important, but easily overlooked.
InDesign to template offers a very quick way to distribute templates throughout an organisation, but the use cases are narrow. The lack of flexibility in designing for content makes it mostly appropriate for stamping logos or addresses on locked designs.
Bespoke templates can be so much more than InDesign-based templates. It may seem like a template is a template, but the value of flexibility and easy-to-use bespoke templates offers should not be underestimated.
Enormous amounts of combinations of sizes, layouts, colours and brands.
Keep content intact when changing the above allows for quickly and efficiently pushing out variants for wider use
Keep content aligned when your brand is updated, as bespoke templates be changed in terms of logo, font, colours, etc. (e.g. when opening a document saved with an older version, the document is updated to the latest version of brand)
Additionally, bespoke templates can have bespoke integrations with external data sources and use them intelligently. Whereas Chili templates can only simply replace text and images, bespoke product elements can react to the content, which allows for a larger range of settings and change in size and proportions while staying effortlessly on brand.
An InDesign file can become a template in minutes, but it is restricted and not a truly usable template. To implement a template that requires a text size change that moves elements and shifts the content around of the template around, the customer either needs trained staff (~2% of our customers) or consultant to do the implementation — exactly the same as with a bespoke template. With bespoke development, all templates are packed into a single application that shares fonts, text styles, colours, etc. This means that the second template will be drastically faster to implement than then first.
Templates that empower
Design templates are more than just InDesign files; they are powerful assets that drive creative excellence, operational efficiency, and brand consistency. They provide a roadmap for crafting visually stunning marketing materials, from landing pages to social media graphics, and empower companies to deliver compelling visuals that captivate audiences. Design templates save time and effort, enabling rapid content creation and iteration, and facilitating seamless collaboration among teams. They also serve as a critical component of brand management, ensuring consistency in visual identity and messaging across various marketing channels. As companies strive to stay ahead in a competitive landscape, design templates are indispensable tools that foster creativity, streamline workflows, and elevate marketing efforts to achieve remarkable results.
Want to learn more about how Papirfly’s templating technology can save you time, ensure consistency in your marketing, and make your team more efficient? Book a demo today!
Co-op advertising model – professional services powered by unique innovative technology
Natalie Wilding
7minutes read
What is co-op advertising and why do companies need it?
Co-op advertising is the sharing of the advertising costs between a major brand or manufacturer and its local retail channel partners. The local retailers get the benefit of additional marketing dollars to attract customers to their stores, and the manufacturers get the benefit of local targeted marketing to increase sales.
There have to be rules set up for this to be beneficial – manufacturers want marketing to be consistent with the brand corporate identity and marketing philosophy, and therefore will provide the marketing dollars to incentivise the retailers to market ‘on-brand’. The local retailers can target the specific demographic locally for the product and can increase their marketing power with a shared marketing dollars model.
Usually the manufacturer will outline the potential funding available to a particular retailer and detail a set of rules to follow. The retailer will then get reimbursed at an agreed rate for marketing efforts that follow the rules.
Local retailers may not have the resources or expertise to develop effective marketing strategies themselves, so the manufacturer will provide access to brand assets and run campaigns from which the local retailer can select and tailor to their needs. These assets will have the blessing of the brand leads, legal departments, and are accessible by retailers across the market at a fraction of the costs that the local retailers would incur doing it themselves. Manufacturers can also incentivise local retailers to target particular marketing strategies or products by increasing the reimbursement rate.
While the co-op advertising model incentivises retailers to follow the brand rules by submitting for reimbursement of costs, this is often coupled with a compliance activity whereby the marketing activities of the retailer are checked via audits of their websites, social media sites, etc. to see what potential customers will actually experience.
Why companies need co-op?
To control brand consistency across all franchises, dealers, and retailers. The financial support is a very strong motivation for individuals, dealers, and stores to follow corporate identity and advertising guidelines.
The budget each dealer is allocated depends on the volume of cars sold (that’s the logic used by a major car retailer). The more cars sold, the more money is reiumbursed, but only under the condition that the brand guidelines and other rules are followed.
Thanks to the co-op program and managed audit, there is a guarantee that the money a major car retailer’s centralised marketing function invests into each dealership is being used only for compliant and eligible marketing activities. In cases where the dealer doesn’t spend the money, the unused money is forfeit after a determined time period.
To have one centralised place for money distribution, not only for tracking purposes, but also for approval process and auditing.
The side effect is that brand leads and regional managers can supervise and have an overview of marketing activities realised across hundreds of subjects, which allows them to see the whole market, or individual marketing efforts. The overview also helps for money redistribution.
The tool comes with statistics, which is useful to brand leads and managers to understand on which kind of activities the dealers are in investing the co-op funding.
How Papirfly is supporting our customers with co-op
Papirfly works with a number of customers on their co-op advertising programs and can support with SaaS platforms for complete brand management.
We can provide the DAM, where advertising assets are held, and the SaaS platform for the presentation and distribution of advertising assets to the retailer network. Retailers are able to quickly gain access to the appropriate brand assets and tailor them using our Create & Activate solution to their own local campaigns while staying on brand. Retailers can download the imagery and creatives and use that as the basis of the local campaign, and even utilise a local ad agency to finalise the ad. This removes the possibility of ads not being on brand, not aligned to the brand corporate identity and, potentially delivering messaging that is not inline with the brand.
We provide a co-op platform, seamlessly linked to the DAM (our Manage & Share solution), that enables all the retailers to access and download the co-op rules, corporate identity guidelines, and brand bulletins. These integrated solutions also provide access to the co-op and compliance platforms.
The co-op platforms that we provide can be configured to your needs, and are built to allow for easy set-up of the necessary workflows to guide the retailers through the co-op processes.
This platform provides the process for retailers to get assets they have created to be reviewed and submit claims for co-op advertising funds, but the platform is only half the story. Just as important is the need for a team to run your co-op program who can audit that your brand rules are being followed and are compliant.
Papirfly provides the experienced professional services to run the co-op program, with a dedicated team of brand specialists that are proven and reliable.
How a major US client works with Papirfly’s co-op funding model and Professional Services team
For one of our major US automotive clients, we provide the platform that can handle the requirements and volumes of a major brand marketing in the US market.
We also provide the Professional Services to run those co-op programs for the client:
We studied the brand philosophy and rules to understand the brand vision
We established a dedicated team that reviews creatives submitted by the retailers to ensure their brand compliance
We worked with the retailers to suggest improvements that can be made
We reviewed the co-op advertising payment claims and determined whether the claim rules have been followed
We also provided a compliance review team that will actively review the marketing of the local retailer
We provide a dedicated support desk to answer retailers questions
We align with the clients business processes to ensure that the payments to retailers go through the required approval processes of the manufacturer and align with the back-end systems to create a seamless payment process
Using the co-op funding model for other use cases outside of car manufacturers
Any franchise business with multiple locations, besides automotive industry may also include fast-food restaurants, hotels, gyms, spas, or any other organisation with a franchise model
Manufacturers can support their retailers to increase in-store sales
Travel and tourism franchises like hotels, airlines, car rentals, etc. could partner with local attractions, restaurants, events, etc. to promote their destinations and packages
Education franchises like tutoring centres, language schools, online courses, etc. could partner with local schools, libraries, community centres, etc. to promote their programs and services
Health and wellness franchises like gyms, spas, clinics, etc. could partner with local doctors, nutritionists, therapists, etc. to promote their facilities and treatments
It can be used for governmental institutions/organisations to control budget handling. e.g. European Parliament and reimbursement of expenses to members for various activities, trips, food, etc.
Other possibilities and variations on how the co-op funding model can be used:
The managed service can be tailored to what the customer needs. The audit can vary depending on the media category etc. (e.g. a major car manufacturer offers financial support only for events, but dealer can still enter a marketing activity for CI compliance check to get support/advise on anything he is trying to publish)
The program can serve for CI compliance check only, but the motivation for dealers to submit creatives for review is higher when supported with marketing money reimbursement
The co-op portal is linked directly to ‘My Creatives’”, which means that the brand leads can decide to only support (or offer higher reimbursement) for activities that use official templates that are available for dealers at dealer marketing portal (brand hub/point). The dealer can select a predefined template in brand hub, use it for his marketing activity and at the same time send a request for funding into the co-op portal.
The benefits of having a Papirfly-run co-op model
Fixed contracted costs for the service
Experienced team with strong product knowledge
A world leading product suite focused on branding and co-op
Adaptability – with an experienced professional services team we can provide insights when you get challenges and adapt to meet your needs
Running all ads through the co-op portal guarantees how the money is spent thanks to the compliance audit there is a guarantee that the money is not being spent to promote other brands, for example
The audit team can control minimum advertising price and check that the advertised product is not being offered/sold below MSRP
The budget each retailer is allocated depends on the volume of sold products, which ensures return on investment of marketing budgets and ensuring local marketing efforts are given the time and attention they deserve
Through co-op, the brand leads can offer ‘certified providers’ for certain marketing activities – the logic being to have limited number of authorised agencies that work for the retailers. This is not only for better brand consistency and control, but is also often more cost efficient, e.g. cheaper cost for broadcast when you buy media in huge quantities, centralised creation of the marketing materials which can give you lower costs per ad
Your next step is to take control of brand consistency, master the art of helping others speak directly to your target customers, and build strong relationships with those that sell your branded products and services. The potential to activate your brand in every location you serve, and achieve significant growth, is possible with Papirfly’s platform and support.
Book a demo today, and talk to us about how we can help you with your co-op advertising programme.
In today’s fast-paced business environment, disruptive technologies and new innovations have become the new normal. These technologies are characterised by their ability to challenge the status quo and, in some cases, significantly alter the way businesses and industries operate – creating new markets and disrupting existing ones.
These changes represent opportunities. In having the ability to quickly and accurately analyse and understand the situation, organisations can stay ahead of the curve by making decisions that capitalise on the moment and give them the competitive edge.
Everything is digital. Now what?
Digitalisation is the process of converting information into a digital format. It has been a core business strategy in companies for years. No wonder, with promises of automation of manual processes, streamlining of tedious workflows and access to real-time data-driven decision making.
In today’s digital world, there exists a reporting system for almost any business activity. The challenge, however, is no longer to convert activities into a digital format. Instead, the challenge is to access and relate data across systems. Identifying the most relevant reporting system and accurately relating its data to your goals and objectives are key activities to capitalise on the opportunity to become a data-driven company.
How can you engage in data-driven decision making?
First, you have to clearly define your goals and objectives. This helps identify the metrics you need to monitor in order to ensure you stay on target. Furthermore, this helps identify areas and specific activities that provide you with the most reliable data.
Let’s take a look at an example:
Say you want to measure how many people attended a conference. The first assumption is to count the amount of tickets sold for that event. This will give you the number, but not how many buyers actually attended the conference. The most accurate data would instead be to measure how many tickets were QR scanned at the entrance. Therefore, the system you want to access is the ticketing system at the entrance door, and the data you want to measure are the number of scanned tickets compared to tickets sold.
Thinking of data in this way means you can speed up the process of finding relevant systems and activities to be used in data-driven decision making.
What are the success factors for data-driven decision making?
Data-driven decision making is the process of using data to inform decisions and business strategy. The quality of the decision can only be as good as the quality of the data. Making sure the data quality is at the highest level becomes a key success factor for any data-driven decision:
Ensure data is accurate and reliable – data needs to be verified for accuracy and come from error-free records that can be used as a reliable source of information.
Ensure data is relevant to the decision at hand – clearly defining your goals and objectives can help you identify which decisions you need to make to stay on target, and consequently identify which focus areas, products or user activities need to be measured to provide you with the most relevant data.
Ensure data is timely and complete – data usually consists of many smaller pieces of data – referred to as data fields – that, only when combined together, contribute to a useful dataset. Therefore, when collecting data fields from an operational log or system, you need to make sure you collect it along with enough supporting data fields. This means that when transferred to a business intelligence tool, it does not lose its context and, consequently, its value in reports and analysis.
Ensure you have a complete set of data necessary to inform the decision, as well as making sure the data is up-to-date and reflects the current situation.
By ensuring that our customers have the right data to back decisions, track their output, and are able to stay on top of daily activities, we’re giving them the tools to operate with the utmost speed and time to market – and ultimately maintaining their competitive edge and continuous growth.
Papirfly’s Measure & Optimise solution can support you and your team’s performance to reach their goals and objectives. Get in touch today to find out how.
A solid product vision is existential for any company
When I first joined Papirfly in the summer of 2022 I joined an exceptional group of people. Through various mergers and acquisitions we had strategically assembled a dream team with a fantastic group of products.
With the new larger company only being a few months old – yet with a heritage stretching back more than 20 years – it was the perfect time to create a long term product vision; one which would underpin our growth planning.
There are many traits and attributes that differ between successful companies but one thing that unites those who grow the fastest, offer the best customer experience, and continuously innovate is a strong product vision.
What I looked at when creating a product vision for Papirfly
With industry leading products we had a great starting point. For brand control we had Brand Portal and Brand Hub, both of which will become Point in 2023. Our enterprise-grade asset management products include award-winning DAM tools we’re combining to create Place in the next 12 months. And our best-in-class template creation products – that will come together as Produce in the year ahead – help our customers activate their brand.
Add to that the collaboration, planning, workflow, and approval functionality within the product group (all of which will be relaunched as Plan in the coming months) and the Papirfly product offering has everything marketers need to build, launch, scale, defend, and control their brands.
But we didn’t quite yet have it all within one product suite. Which is why we launched Unification; our major program to unite and unify the best of everything we’ve ever built into one cohesive product suite.
Unification, which started at the end of 2022 and will be launched to the market in May 2023 (be the first to get access), will see us bring all of our products’ strengths together for the first time. Where customers might have only used – and loved – one or two products in the past they’ll be able to access all of them in one enterprise-grade tool. One access, one look and feel, one ecosystem. Further, we’ll be able to add our latest product – deep analytics and reporting capability – to the heart of the joined-up product, launching what will be known as Prove simultaneously.
Completing the vision of having one interoperable product suite – designed to put the greatest brand management tools at our customers’ fingertips – is our integrations bench strength, Plus. A marketplace of connectors, plug-ins, and integrations, designed to extend the power, flexibility, and scalability of Papirfly.
Power. Flexibility. Scalability. These became the pillars of our new product vision. The newly unified Papirfly product suite offers customers the ability to do everything they need to in one place, move simply and swiftly between different functionalities, and access a product offering that is absolutely greater than the sum of its parts.
Deciding on, and believing in, Unification
It was relatively obvious from day one that Unification was the right strategy. Why? Because we had a market-leading product in each of our service areas. We didn’t have glaring gaps, we didn’t have to focus on ‘fixing’ broken products, and we didn’t have significant innovation gaps in the market either. Which meant we knew, very quickly, that our primary task was to bring everything together in one family.
The best automotive brands do this – with the commonalities of platform sharing used to build competitive advantage. The best retail groups do this – with shared services, logistics, and scale all used for leverage and to build the best customer experience. We’re doing something similar and Unification is how we’re doing it.
Creating a product vision that works
One thing worse than having no formalised product vision is – of course – having one that doesn’t work. When I, and the product team, set out to create a vision that worked for all of our products, all of our people, and – most importantly – all of our customers, we did our research thoroughly.
The resulting product vision reflects what our customers need, what our people can build (better than anyone else), and where we sit in the market. We have ensured product-market fit by collaborating with customers (including via our new Customer Council), working hand-in-hand with partners (including via our new Product & Partners group), and partnering with industry tone-setters like Forrester.
The result of this multi-pronged approach is that we have a product vision that anyone at Papirfly can communicate to anyone we work with. Indeed our internal studies show that 99% of Papirfly employees, when surveyed, know exactly where the product team is taking our product.
Aligning external with internal
We have worked hard on communications – which is one of the not-so-secret secrets of successful product development. Rather than doing it in isolation we have communicated, collaborated, and engaged with all of our stakeholders. What product builds affects what sales sells, how customer success delivers customer experience, and how marketing talks to the world. So we’ve made sure everyone has been involved in the process.
One of our key outputs has been the opening up of our product development process. Our product roadmap is public and allows all of our customers to feed in ideas, thoughts, and feedback. Our RoadmapTV channel on papirfly.com brings together all of our product assets transparently and simply – from release notes to webinars, from whitepapers to ‘how to’ videos, and from ‘meet the maker’ content to the latest news on our product suite’s evolutions. And our internal program of product knowledge, education, information, and engagement means that everyone at Papirfly knows everything about our product at Papirfly.
The result is a product vision that directly translates to product innovation and one which builds a customer experience we can be proud of – reflected in statistics like 99% of Papirfly customers saying they would happily recommend us to a friend or colleague.
We’ve only just started but our product vision is guiding us for customer-led growth. Find out more about the future of brand management by booking a demo today.
