Classifying AI-Empowered Software for Nonprofit Fundraising 

The expansive library of software options makes it an exciting and difficult choice to choose the most feasible system for an organization’s needs and budget. Upon beginning my research on AI in fundraising software, I was surprised at the distinctive and unique ways that each organization manipulates AI technology. Kanter and Allison Fine categorize these services in AI4Giving: Unlocking Generosity with Artificial Intelligence: The Future of Giving, a select few listed below:

• Donor Prediction Models and Automated StewardshipWorkflow: Machine learning core methods to train algorithms on donor data to identify the most likely donor prospects.
• Online Fundraising Campaigns: Machine learning analytics to analyze donor databases, sometimes appended to third-party data sets or social media data.

• Donor Research/Data Collaboratives: Platforms that are sharing data for research purposes with privacy protocols and ethical standards.
• Reporting and Workflow Tools: Helps platforms efficiently generate reports or automate administrative tasks like customer service for donor

At the forefront of the argument for integrating AI in fundraising is that machine learning technology stands to revolutionize workforce efficiency. Gravyty is a donor marketing and outreach tool that auto-generates emails based on repetitive learning the software has performed off of the organization’s donor data. Gravyty claims its tool empowers fundraisers to do four times more personal outreach and inspires first-time donors at a scale previously unthinkable. The software capabilities align with the “Online Fundraising Campaigns” model mentioned above; the donor relationship is the focus, instead of the actual identification of the donor. 

Along the lines of workforce efficiency, AI-equipped fundraising software stands to eliminate mundane administrative tasks and help direct more attention on strengthening donor relationships and strategies. Boodle.AI’s platform trains donor prediction model algorithms using third-party data, a 500-something data points across a 220-miilion-record data set. This includes demographics, lifestyle, and behavioral analytics to help the fundraiser make decisions. Similar to Gravyty, Boodle.AI seems to prioritize marketing data and consumer behavior in its training model. Their platform even offers a Predictive Advertising module, which uses a combination of data enrichment, AI modeling, and segmentation to micro-target consumers. 

Donor Prediction Models and Automated Stewardship

You may have heard of the “spray-and-pray” fundraising approach, a term coined by Gregory Warner. It’s a strategy where the fundraiser sends out a mass of donor appeals (“spray”) and proceeds to wait (“pray”) so that enough donors respond so they can make their goal. The spray and pray approach is transactional, expensive, and ineffective. Retention rates fall and donors lapse from burnout. Yet, moving to a targeted and focused fundraising approach can be time-consuming and tedious. Combing through prospect lists and current supporters can take hours and weeks of analysis.

This task might take an experienced fundraiser a week of tedious desk work, but the algorithm can accomplish it in minutes. The fundraiser is freed to use that time for face-to-face meetings with donors, delving deeper into their interests and needs to personalize cultivation and an ask.

Beth Kanter and Allison Fine, AI4Giving

This is where another facet of AI tech is altering the fundraising workflow: donor prediction models and automated stewardship. In this capacity, machine learning is used to train algorithms on donor data to identify prospects and predict upcoming donor lapses. These providers typically integrate with existing customer relationship management databases. 

Product Review

Introducing, Dataro. The “GiveTech” startup’s propensity modeling software uses machine learning technology to generate predictions about how donors are likely to behave. Dataro’s AI software aims to maximize income and minimize wasted costs, and transform the way that fundraisers stay alert and relevant to how donors are engaging with their organizations. Back in the product’s early development, it was selected as one of the top 10 fundraising innovations for not-for-profits in a competitive global open innovation challenge in 2020. Winning the competition built momentum for Dataro’s international plans. Dr. Tim Paris, David Lyndon, and Chris Parver, co-founders of Dataro, were able to raise $2.5 million in seed funding by 2021. With over 100 clients between Australia and the U.S. after three years of operations, Dataro’s rise certainly should be noted. 

The Technology & Model

Here is how the process works with Dataro’s machine learning tools: 

1. The machine learning software analyzes the fundraising data, splitting it into hundreds of discrete “features” like gift recency and donor age.
2. The AI searches through these features to find trends, patterns, and relationships. It uses these insights to begin training algorithmic models that can make predictions for the specific desired propensity.
3. The software evaluates its trained models to find the one that performs the most accurately based on all the complex relationships in the organization’s historical data. 
4. This model is used to generate propensity scores and ranks for each individual donor, showing how likely they are to behave in a certain way and how they compare against the rest of the donors in the database. 
5. These scores and ranks are exported back to the organization’s CRM dashboard for easy use and analytic metrics, giving a real-time AI-driven list of the ideal donors who should be contacted. 

Differentiating Dataro

Let’s differentiate Dataro from competitors: 

• Unlike Boodle.AI, Dataro does not augment data from external third-party sources. Dataro’s predictive models are only trained using the nonprofit’s data. This ultimately takes the guesswork out of fundraising. 
• Dataro is not a wealth screener. It doesn’t generate artificial wealth scores to grasp a prospect’s capacity. The software predicts donor behavior to help fundraisers prioritize attention on specific donors.    
• Having originated in Australia, Dataro’s system is built around compliance with the Privacy Act of 1988 and the Australian Privacy Principles. The company encourages and uses anonymized, tokenized data wherever possible, preventing an organization’s data from potential data breaches. 

Getting Started with the Software

To better understand the interface and technology, Dataro offers a free-of-charge demo trial with mock organizational data. I will use the demo trial as a basis for my product review below. 

Branding and UX Design

When I first opened the Dataro on my laptop, I was immediately impressed with the branding aesthetic and interface design. In terms of its visual identity, the pastel color palette and vector graphics distinguish Dataro as an approachable and brand-conscious platform. Moving around the software feels as if I’m scrolling through my phone. There are no obtrusive pop-ups, delays, or distracting background information. It doesn’t feel like I am navigating an AI-equipped donor platform, and I think that is intentional. 

Campaign Results

Under Campaign Results, Dataro users can track basic data such as total raised, net return, return on investment, response rate, and average gift size. For the purpose of this review, I chose the June Direct Mail Appeal to analyze, which has yielded a fantastic ROI of 155% and a response rate of 8.7%. 

Moving further down the page, Dataro evaluates its prediction results. Dataro scores approximate the probability of a patron doing a particular action, and the higher the score, the higher the probability of an individual taking the associated action. From the graph below, 79.63 percent of patrons with a 90 to 80 percent score responded to the DM appeal, which is just under the threshold of 80 percent. From the trend of the graph, it appears that the model just slightly underperformed in each score band. 

Dataro ranks order the organization’s donors from most to least likely to take a particular action. A person with Rank 1 is the most likely to act. Here, the graph illustrates that the individuals Dataro ranked from 1 to 1,000 had a collective response rate of 45.5 percent.

We can dive deeper into this data with Dataro’s prediction segments, listed in the tables below. Dataro gives us insight into the average gift amount, gifts over $500, and the largest gift of those top-ranked 1,000 individuals.

My favorite graph in the Dataro app is the Dataro Score Band by Net Returns. This graph illustrates where the organization starts to lose money on its ranked individuals. In this case, supporters ranked 4,000 to 9,815 yielded negative returns for the organization. 

The DM Appeal report is just the beginning of Dataro’s impressive array of propensity models. These include a Regular Giving (RG) Churn report, which measures the likelihood that a regular giver will churn/attrite in the next six months. The RG Upgrade, RG Reactivation, and Convert to RG propensity models are all available within Dataro to support the organization’s Regular Giving. Each report contains a similar dashboard as the DM Appeal campaign, a results table, and a table with results segmented using relevant segments from your CRM. 

Takeaways: The Campaign Results tab demonstrates Dataro’s accessible design and user-friendly interface. I do wish there was an option under each campaign to view the names of campaign supporters. The dashboard gives a substantial overview of the successes and failures of the campaign, but nonprofit fundraisers often operate from a degree of familiarity and name recognition. It would provide a more holistic and individualized outlook on the campaign and lead fundraisers where to focus their time and money. This isn’t a major issue, since we can see each individual’s rank and score through the Dataro dashboard within the organization’s CRM, which I will cover soon. 

Prediction Insights

The Prediction Insights tab provides a summary of each campaign’s predictive model, including the score band, the number of supporters in that band, and the percentage makeup of that band in the total cohort. For the DM Appeal campaign, four individuals are 90 to 100% likely to give to a DM appeal, just .01% of the cohort. Dataro’s machine learning pipeline is detailed below. 

Takeaways: The Prediction Insights tab could also benefit from a “See Who” button to list the individuals that Dataro has generated using the predictive model. Especially in a Churn, Activation, or Upgrade campaign, the donor most likely to act is a digestible volume of data and names for the fundraiser to use. 

Model Performance

Dataro has a system of checks and balances to test the quality and accuracy of their models. The Model Performance tab details the prediction quality and top predictors for each campaign. The DM Appeal earned a 96% stellar grade according to Dataro’s evaluation, and the model was trained mostly on the frequency of DM gifts, payment method, and total single giving.  

In true deep learning nature, the machine learns from the organization’s data. Each Dataro-trained model is unique to the organization’s data and unique to the moment in time. Considering how people are motivated at different times in the year to give (ex. tax season), it’s highly advantageous that Dataro constantly retrains its models to provide the most accurate predictions at each point in time. Dataro’s models are constantly self-adjusting, eliminating mistakes that didn’t accurately predict the right likelihood of action. 

Takeaways: It’s impressive to see such transparency behind Dataro’s modeling techniques. I wonder how the volume of available data dictates each model’s performance evaluation. The DM Appeal had a training data size of over 8.7 million, with 1.9 positive cases and 362 total predictors. The data training size isn’t that large for other evaluations, but it still begs the question: will Dataro’s models be as effective on my organization’s smaller data set? More on this below. 

Putting a Name to a Propensity Score

Dataro’s CRM integration is where the data starts to come to life. In the organization’s CRM, Dataro’s dashboard appears on the donor’s contact page. The above example is within Salesforce. Here, we can see the propensity scores that are only relevant for the specific contact. Propensity scores are listed on the left (on a scale of 0 to 1) and measure the individual’s likelihood to take a certain action. The propensity ranks, listed on the right, compare the individual’s likelihood to take the action against the entire cohort of donors.

When the fundraiser begins to initiate a campaign, they will want to create a report in their CRM using Dataro’s propensity scores. In the video below, Dr. Chris Parver demonstrates the simplicity behind creating a list based on the top 500 ranked individuals likely to give to a DM appeal. Dataro’s models and predictions are also dynamic, updating weekly so the organization will always get the most accurate predictions in real-time.

Takeaway: I cannot evaluate the effectiveness of Dataro's CRM integration capability as I am using the free trial software and not any organizational data. From Dataro's Help Desk videos, it seems like accessing the donor's scores and ranks on their contact page help the fundraiser make meaning of the data. It's important to remember that these scores are predictions and not guarantees of a donor's behavior. 

Data Requirements and Integrations

AI and machine learning are only as good as the data they are working with. Machine learning algorithms require large volumes of data in order to learn patterns and behaviors, and to create models based on that analysis. Prospective nonprofits hoping to utilize Dataro or other AI technology may be at a standstill if they don’t have enough usable data. 

For Dataro to achieve predictive accuracy, the client needs at least five years of giving history, and at least 10,000 constituent records. Those records don’t all necessarily need to be active, but it is crucial for the system to work on a substantial amount of active data. 

Dataro currently works with the following CRM systems: Donorfy, Salesforce, ThankQ, Blackbaud, Raiser’s Edge, NXT, Microsoft Dynamics, and Advanced NG. 

Takeaway: Looking through Dataro's current clients, the case studies are powerful applied examples of the technology Dr. Paris said the Royal Flying Doctor Service Victoria increased net revenue in their 2020 Tax Appeal by nearly $35,000 by using Dataro’s propensity scores. But I'm surprised to see very few, if not any artistic clients listed. After speaking with Debbie Stanley, Leader of U.S. Expansion at Dataro, it doesn't seem that Dataro has partnered with many arts organizations. This is in part due to their very recent expansion to the U.S. 

I see contradictions between Dataro's mission to democratize the use of AI technology and the limitations inherent within the nonprofit infrastructure. The current CRM systems that Dataro integrates will not work with the systems of many nonprofits. Even if nonprofits have the correct integrated software, the organization likely will not have enough active data records. These issues point to how the emerging use of AI for philanthropy may potentially skew towards better-resourced platforms and nonprofits, bringing AI inequities to mind. 

Conclusion

Researching Dataro gave me a certain appreciation for AI-equipped fundraising software that focuses on donor identification. Using donor prediction models puts the fundraiser first and the AI second. AI-powered donor communication systems, on the other hand, do the opposite. I would not advocate for a nonprofit to begin using AI-equipped donor communication tools for several reasons. The ethics of using AI for donor solicitation are tricky to navigate considering authorship issues. Additionally, nonprofit staffs are typically their own experts on donor messaging. Between acknowledgment letters, email campaigns, and day-to-day communications, professional fundraisers know how to speak with their donors. It is the decision-making, tasks like optimizing a list of 5,000+ potential donors for solicitation, that development staffs stand to benefit the most from.

I see Dataro as a revolutionary force in nonprofit fundraising. The technology, if truly effective with the right organization’s data, harnesses the potential to shape how we prioritize our time with donors. At this point in time, Dataro is an elite solution. If the product can scale to organizations with smaller datasets and a broader selection of CRM software, then the future is bright for this tech startup.

AI is like hot sauce. A little bit, used in the right place, can be perfect. But dumping too much on or using it everywhere will probably ruin your meal.

Nick Hamlin, AI4Giving

My advice to organizations curious about Dataro or another AI-equipped fundraising platform: approach AI as an experimentation strategy with evaluation metrics baked into the process. We experiment with ideas and projects that are never fulfilled. If the experiment indicates growth for your organization, then move forward and implement the technology into your existing system. AI should not completely reinvent the nonprofit fundraising wheel.

Bibliography

Australian Government. 2014. “Privacy Act 1988.” Legislation.gov.au. Attorney-General’s Department. March 12, 2014. https://www.legislation.gov.au/details/c2014c00076.‌

Bindbeutel, Duncan. “Navigating the Ethics of Using AI for Donor Solicitation.” AMT Lab @ CMU. April 20, 2022. https://amt-lab.org/blog/2022/4/how-can-nonprofits-navigate-the-ethics-of-using-ai-for-donor-solicitation.‌

“Boodle for Nonprofits.” BoodleAI. Accessed April 22, 2022. https://boodle.ai/nonprofits/.

“Dataro Demo Account.” Dataro. Accessed April 22, 2022. https://www.app.dataro.io/home

“FAQ – Dataro.” Dataro. Accessed April 22, 2022. https://dataro.io/faq/.

“Fundraising Powered by AI – Wisely Product.” Wisely. Accessed April 22, 2022. https://fundraisewisely.com/wisely-ai-powered-fundraising-software-product/.

“Gravyty First Draft.” n.d. www.gravyty.com. Accessed April 22, 2022.    https://www.gravyty.com/firstdraft/.

Kanter, Beth, and Allison Fine. 2020. “AI4Giving: Unlocking Generosity with Artificial Intelligence: The Future of Giving.” Bill & Melinda Gates Foundation.

Martel, Adam. 2021. “How Artificial Intelligence Is Transforming Fundraising.” Education and Career News. March 7, 2021. https://www.educationandcareernews.com/future-of-higher-education/how-artificial-intelligence-is-transforming-fundraising/.‌

Alex Moltzau. 2019. “Artificial Intelligence and Nonprofits.” Medium. September 1, 2019. https://towardsdatascience.com/artificial-intelligence-and-nonprofit-e6cdaaae166f.‌

Parver, Chris. 2021. “Fundraising Analytics: 20 Critical Metrics & How to Use Them – Dataro.” Dataro. March 25, 2021. https://dataro.io/2021/03/25/fundraising-analytics/#future.‌

—————.“Help Desk – Dataro.” Dataro. https://support.dataro.io/hc/en- us/articles/1500004945662-What-are-the-definitions-for-each-Dataro-model-.

—————-. “How Do I Tell If Dataro’s Predictions Worked? – Dataro.” Dataro. 2021. https://support.dataro.io/hc/en-us/articles/1500004945662-What-are-the-definitions-for- each-Dataro-model-. 

—————.  “Minimum & Suggested Data Requirements– Dataro.” Dataro. 2021. https://support.dataro.io/hc/en-us/articles/1500004945662-What-are-the-definitions-for- each-Dataro-model-.

Paris, Tim. 2022. “Beat the Machine by Dataro.” GoToWebinar. Presented at AI for Fundraising: Learn from an Expert, April 19. 

Rizal, Paulo. 2020. “Web Login Service – Stale Request.” December 16, 2020. https://courses.ideate.cmu.edu/62-830/s2022/wp-admin/post.php?post=1415&action=edit.‌

“Predictive Advertising for Modern Agencies and Brands.” BoodleAI. Accessed May 6, 2022. https://boodle.ai/predictive-advertising/.‌

Stanley, Debbi. 2022. Dataro Product Demo Meeting Interview by Evan Aanerud. Dataro.

Thomsen, Simon. 2021. “AI-Led ‘Givetech’ Dataro Lands $2.5 Million Seed Round to Help        Charities Innovate Fundraising.” Startup Daily. December 16, 2021. https://www.startupdaily.net/2021/12/ai-led-givetech-dataro-lands-2-5-million-seed-        round-to-help-charities-innovate-fundraising/.

Warner, Gregory. 2019. “3 Simple Steps to Break Your Transactional, Spray-And-Pray Fundraising Addiction.” www.linkedin.com. September 10, 2019. https://www.linkedin.com/pulse/3-simple-steps-break-your-transactional-fundraising-addiction-warner-1e/.‌

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“10 Must-Have Features to Have on an AI-Driven Fundraising Platform.” 2019. BoodleAI. September 19, 2019. https://boodle.ai/blog/artificial-intelligence/10-must-have-features-on-ai-driven-fundraising-platform/.

“If you’re wearing your Disney MagicBand and you’ve made a reservation, a host will greet you at the drawbridge and already know your name—Welcome Mr. Tanner!…The hostess, on her modified iPhone, received a signal when the family was just a few paces away. Tanner family inbound! The kitchen also queued up: Two French onion soups, two roast beef sandwiches! When they sat down, a radio receiver in the table picked up the signals from their MagicBands and triangulated their location using another receiver in the ceiling. The server—as in waitperson, not computer array—knew what they ordered before they even approached the restaurant and knew where they were sitting.”

This is the matrix of communication technology that Disney Parks and Resorts have mastered to craft a magical guest experience. Underneath that magic is Near-field communication (NFC) technology, which has been transforming live event experiences for years. Disney Parks is at the forefront of the change, having utilized the all-in-one-device MagicBand since 2013, which renders the park into dynamic computer, streaming real-time data about where guests are and what they’re buying.

This “invisible” wearable technology has created a seamless guest experience where people are happier by stripping their options away. MagicBands let Disney guests set their must-do’s and let everything else unfold naturally around what they have selected. (Disney recently shifted to “MagicMobile,” which allows Apple users to access that MagicBand technology for free with their mobile device.) 

Beyond MagicBands, NFC tech has built rapid momentum in credit card payment processing, digital wallets, wearable tech, and the broader Internet of Things (IoT) universe. New NFC activations increased 71% from 2018 to 2020, while NFC interactions increased 63% over that same period. In fact, 1.6 billion NFC-enabled devices are expected to reach the market by 2024. The numbers are even more significant when looking at the IoT beyond NFC. By 2030, an estimated 500 billion IoT-enabled devices will be in circulation. NFC is expected to become more widely adopted outside of payments, particularly among the IoT, due to the rise in demand for contactless technology. For performing arts organizations wanting to streamline their front-of-house operations and design a patron-centric user experience, NFC technology is promising and remarkably cost-effective.

What is NFC technology?

Near-field communication is a series of communication protocols that allows two electronic devices to communicate over a distance. NFC is based on radio-frequency identification (RFID) technology, which allows compatible hardware to use radio waves to both controls and communicate with otherwise unpowered and passive electronic tags. NFC transmits electromagnetic induction with unpowered and passive electronic tags, so the passive device doesn’t need its own power supply. The principal mechanism of NFC communication is the relationship between the emitter or reader/writer, and the tag or card. The antennas of the Emitter and Tag are coupled via an Electromagnetic Field, and an alternating current passes through the primary coil (Emitter), which induces a field through the air, inducing a current in the secondary coil (Tag). 

These tags store data, such as URL addresses, Wi-Fi or Bluetooth pairing, SMS, Email, Vcard, and phone configurations. In the user experience, that Tag is the guest’s MagicBand, phone, or any wearable technology that provides the energy-supplying electromagnetic field and reads the tags. After the Emitter determines if the tag can be read, it reads the NFC Data Exchange Format (NDEF) and stores it in the Tag’s EEPROM (electrically erasable programmable read-only memory).

There are three different NFC Modes of Operation, as defined by the NFC Forum.

In Reader Mode, the NFC-enabled phone can read or write data to any of the four supported tag types in a standard NFC data format. Reader Mode is highly applicable in NFC advertising, such as Samsung’s ‘Share to Go’ posters. Using NFC on signage allows market segments to access a more in-depth product experience, just with a simple tap. For organizations interested in applying this technology, Beaconstac offers simple solutions to embed NFC hardware through tags and stickers, including cost-effective NFC sticker tags for proximity marketing. 

In Card Emulation mode, a device acts as a Tag during interaction with another NFC-enabled phone. Card Emulation Mode technology is utilized in public transportation networks and contactless credit card purchases, such as Google and Apple Pay.

Peer to Peer enables two NFC-equipped devices to exchange data, acting as emitters. In this Mode, two smartphones can share a Bluetooth or Wi-Fi password to start Bluetooth or connect to Wi-Fi.  

NFC and Ticketing

In the live event and attraction space, NFC (which is a specific type of RFID) has streamlined ticketed entry and payment. Line length is the number one frustration while at an event, and guests will often change their intentions upon seeing a long line unless they feel it is unavoidable. RFID solutions have been shown to reduce the average transaction time from 90 seconds to 15 seconds, while delivering as much as 30% higher spend per guest. Wearable NFCs can minimize this behavior. 

But what about QR codes?

While Quick-Reference codes are quick, cheap, and easy to develop and deploy, they are vulnerable to certain security risks and performance issues, especially in bottleneck crowds. For ticket validation, users need to position a QR Code at the correct angle and distance from the reader. On smartphones, QR codes need to be opened, unlocked, have sufficient power, and ensure it is displayed clearly and brightly. Alongside these poor ergonomics, users could be victims of fraudulent codes to inadvertently download malicious software or make compromised payments. According to a consumer poll, 71 percent of people cannot tell the difference between a legitimate or malicious QR Code and 35 percent are unaware that QR Codes are used by criminals. The security issues of QR codes for ticketing means that despite these low initial costs, longer-term spending may increase as agencies deploy resources to mitigate risks. 

NFC, alternatively, validates transactions in only 120mms with its ‘tap and go’ technology. NFC contactless devices draw their power from the reading terminal, so even if a phone battery is flat, they are still usable and won’t cause line backups. Additionally, using NFC ‘taps’ to enter and exit a space make it nearly if not impossible to fraudulently duplicate, effectively rendering forgery of tickets useless. NFC is also the most secure because it can implement added encryption that is only accessible when the user taps the Tag with their device. 

NFC and Payment

The future is contactless and commerce-on-the-go. NFC technology has changed the system of credit card payments through mobile wallets/ apps that offer a secure place to store digital valuables for convenient on the go use. Data involved in an NFC mobile wallet transaction has two layers of protection, one being the device itself, and the other as the data encryption. Similar to EMV chip cards, mobile wallets that use NFC tech are more secure than magnetic strip cards. Merchants also benefit from NFC payments because it allows for effective integration of customer loyalty programs into payment processing. For instance, customers can redeem a coupon immediately with the tap of their phone. 

Does NFC Technology have a Place in Arts Enterprises?

The system of attending a performing arts event doesn’t involve as many points of contact as a visit to Epcot or Animal Kingdom. Patrons aren’t running their credit cards up for five to six attractions, but instead sitting down for a single event. While the modern structure of event attendance at arts enterprises limits these points of contact, organizations can still harness these “NFC Tap Points” to gather meaningful data on their patrons’ habits. The figure below displays these flexible points in the customer journey that organizations can integrate NFC technology. 

Organizations can brainstorm additional creative tap points as they develop various customer journeys. Perhaps one of the biggest takeaways from using NFC in a performing arts venue is streamlined purchases from the customer’s digital wallet. According to research from Intellix, the media platform for the events sector, festival organizers saw their revenue jump by up to 87 percent by switching to a cashless payment solution. There is enormous potential for arts enterprises to think creatively about implementing NFC terminals at various stages in the customer journey. 

Getting Started

As a growing number of devices gain NFC technology, how do arts enterprises design an efficient system that engages wearable technology that the patron already owns (iPhone, smart watch, NFC ring, etc.)? In order for an organization to begin experimenting with the technology, there are three straightforward technical requirements to meet:

• Blank NFC Tag: Tags store data. These are affordable ($11.99) and readily available.
• NFC-Compatible Device: Modern Android/iOS devices incorporate NFC technology.
• NFC Programming Application: These are meant for writing your NFC tags, including uCreate by Identiv, NFC TagWriter by NXP, NFC Tools (app), and Smart NFC.

Key Considerations in UX Design

Performing arts organizations will also need to strategize implementing their NFC tech with a human-centered design approach. Research shows definitive gaps between user perception of NFC and reality of the technology. Among the most important user peceptions that performing arts institutions should address is that most iOS users are only familiar with Apple Pay. Modifying NFC instructions on payment and ticket check-in terminals to one (maybe two) consistent logos is importnat to streamline the user experience and not create confusion.

There are also important NFC usability differences between iOS and Android devices. The NFC chip is located at the top of iPhones, and in the center of Android mobile devices. One way to address this potentially confusing interaction is to use device-shaped physical or digital maps that communicate where the NFC chip lies on each device.

Along with the initial message guiding the user where to put their mobile device, it is also necessary to design an applicable error caption that prompts the user to continuosly move their phone around as part of the scanning process.

The current design for NFC technology is underdeveloped, with no consistent visual language or branding. The few NFC-specific icons do not communicate the breadth of NFC functionality and behavior. These are important perceptions that organizations will need to take into consideration when desigining approachable and NFC operations for their patrons.

When implementing NFCs that will engage with the user’s mobile devices or wearable tech it’s also important to know the ratio of your patron base that have NFC-enabled devices. Organizations might poll patrons on the compatibility of their mobile devices with NFC tech to get a better idea of the range of usage.

The iPhone has had NFC capabilities since 2014 with the release of the iPhone 6. In-app NFC read capabilities were later introduced in 2017 with iOS 11. Users with an iPhone 7 and newer can read NFC with an app like NFC Tools. iPhone XS, XS Max, and XR, released in 2018, have the ability to read NFC from the home screen without the need of a 3rd party app. As of fall 2019 Apple unlocked the ability for the iPhone 7 and newer to write NFC tags with an associated app. The latest iPhone 11, 11 Pro and 11 Pro Max all support NFC reading without the need for a 3rd party app and the ability to write NFC tags with a 3rd party app.

Android devices were first to the market with NFC support in the early 2000s. Unlike iPhones, Android devices do not require an app to utilize NFC. Instead, reading is enabled from any screen whenever the device is unlocked. 

Final Thoughts

NFC technology is an ambitious endaevor for performing arts organizations to implement across the entire user experience. There are assumptions to confront, technical barriers to hurdle, and ethical questions to ask. For instance, do I want to collect patron data through NFCs from my audience, and will they be comfortable with it? There are some applications where the answer will be no. The accessibility of the technology across most mobile and IoT devices, however, emphasizes the potential of NFC tech in the performing arts sphere. By simply buying blank NFC Tags and downloading any NFC programming application, any staff member can begin to play with the technology and understand its applications in their organization’s space. The growth of NFC technology in our day-to-day lives demonstrates that institutions must maintain relevancy in our increasingly frictionless, commerce-on-the-go, and interconnected society.

References

This is how IoT will play a big role in the future of art

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How Near-Field Communication (NFC) Works: Benefits of NFC for IoT and the Real-world

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3 Music Festivals That Went Cashless and Never Looked Back

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