45. Some Personal News

Confusing Cost with Value; How to Build an AI Use Case Roadmap + a Reader’s Question Answered

Jun 25, 2023

Happy Sunday. To those I spoke to this week or spent time with in-person - thank you. A very warm welcome to all the new subscribers. I’m thrilled to have you as readers and truly appreciate your feedback and support.

Let’s dig in!

Ads & Waste

The US study aiming to emulate the previously discussed ISBA study that showed £billions were being lost through poor use of programmatic advertising went public in Cannes this week.

It showed similar issues to the UK report - primarily chasing cheap media, confusing cost with value. The average campaign ran on 44,000 websites. How reckless.

The ANA study said at least 23% of the $88 billion spent on programmatic across the open web was “rife with waste”… to the tune of $20 billion. The total value of all goods and services produced (GDP) within Iceland or Cyprus amounts to that, let alone Jamaica.

The report shared the following: “Programmatic advertising across the open web allows marketers to leverage a wide array of data to reach and grow audiences. But if marketers take their eye off the ball, suspect inventory can find a way into media plans, diminishing the potential of programmatic advertising as a useful tool to drive results.”

What should happen next?

This affects every industry. Maybe a media agency group (as opposed to an independent i.e. ISBA/ANA) should break the mold and say “Hey, we’re genuinely transparent and here’s how we make our money. What you see is what you get, and here’s how you should compare us with our peers.” Seems a smart differentiator at the very least.

Wouldn’t it also be good if the industry at large held its hands up and collectively said that these reports hold a mirror up to an industry that isn’t healthy when the people funding it (the advertisers) can’t even work out what their money has bought, or where it’s even gone? Feels like we need to bring back the naughty step, draw a line in the sand and start again.

Idealistic? Maybe. But with double digit billions to be saved, I’d like to see the platforms and media owners step up.

Work in advancing accountable digital and cross-media measurement, in which advertisers, agencies and media owners take an active stake has never been more critical.

Push your agencies.

Q&A: How to Bring For-Profit Thinking into a Non-Profit Team

In edition 39 of SPN I wrote about setting goals for fundraising teams. In reply a reader shared a question - paraphrased: “What advice do you have to help foster more of a startup-like culture at my Org and put a sharp focus on revenue generation, create more of a donor-centricity vibe and an openness to doing things differently?”

Loved the question. There’s a lot to be learnt and inspired by from the private sector. Here’s a redacted version of what I replied:

Talk to People Directly. Every successful for-profit marketing team talks to their customers at physical locations or asks repeat buyers to take a call. At the individual/small donor level most non-profit organizations have separate face-to-face fundraising teams. Invite them over and listen to them download on their interactions with donors. Better still, join them! It helps to break the wall of impersonalized qualitative research and hear from advocates directly on what persuaded them to engage – or even more helpful, what didn’t.
Be the Squeaky Wheel. Raising money is a team sport. And like in a team there are different roles, all of which are necessary and contribute to winning. From a revenue perspective, that team needs to communicate loudly and regularly that its role is to maximize the amount of funds raised and help internal teams understand how. None of this stuff should happen behind closed doors or in a silo.
People benefit when they understand how and why things are done. In Org’s where most of the fundraising is done through Corp. Partnerships or Government, digital marketing is often seen as the supporting, top-of-the-funnel advocacy initiative. It’s very effective there and can be much more than that. Helping other teams in the organization understand that takes time. Make the time.
Invest in Cross-Industry Education. The for-profit sector innovates faster - expose your team to it. These verticals face problems very similar to several donor journey elements:
1. Branding paid media - CPG and Automotive.
2. Performance paid media - Direct-to-consumer eCommerce brands seem to be the most agile.
3. Website donation funnel - Travel, specifically Airlines, have done a lot of work here.
4. Data enrichment and analytics - Financial services, from traditional large banks and insurance companies to FinTech startups.
5. Content recommendation engines to move one-time donors to monthly - Streaming and Entertainment.

Attend conferences and talk to teams and vendors working on these problems. Their case studies will give you plenty of ideas to implement and flag pitfalls.

Obtain Technical Skills. Encourage your team to learn skills outside their comfort zone and day-to-day job. Spend the team’s learning and development budget on obtaining an AI certification from Google Cloud or become a Chat GPT prompt expert for less than $20 with Udemy or go deep into understanding persona’s, positioning and messaging with the PMA. Marketing is changing extremely fast, and having these skills in-house will help ensure you stay in the “early adopter” phase.
Rely on External Partners. Choose agencies and vendors that work with a variety of industries. If you structure the relationship right - I shared some thoughts here and here - your partners will bring the best and latest practices from other industries into their work with you.
A Marathon is Still a Race. Relentlessly share the data that matters. Start your team’s meetings by looking at the key metrics across the Donor Lifecycle from the previous week. Publish a daily fundraising tracker on a TV in your team’s office or set up a daily email to land in inboxes at 9am.
Partner Externally. Explore marketing partnerships with for-profit companies that share your mission and target similar demographics. This example from Save the Children’s Centennial celebration is an excellent case in point. Moreover, this will expose you to the day-to-day of marketing teams from other sectors – seeing how they operate and possibly giving you more ideas to apply internally.
Partner Internally. There’s likely so much experience and expertise to be tapped within your Org. There are riches to be discovered when striving to work cross-functionally. Some of my most rewarding and enjoyable learning experiences have been spent on cross-functional projects collaborating with Advocacy and Corporate Partnerships teams.
Stir It Up. Routine is innovation’s worst enemy - introduce a change in how your team interacts. I recently attended a “solve-a-problem-ethon” where teams were split into a few groups and worked on solving a marketing problem for another brand outside of my sphere of knowledge. The brain was put to work and I wore a different hat for a few hours. I left refreshed.

I recognize I need to do more of bullet 3. Bullet 1 is both fun and eye-opening. I’ve loved partnering both internally and externally (bullets 7 and 8).

In summary, all these bullets seem to fall into 3 categories:

Invest in cross-industry education and learn from companies that solve similar problems but have more room - and budget - to fail.
Collaborate and partner externally to get out of the echo chamber.
Measure frequently and fail fast. Measurement is key to the success of any program. It’s table stakes. Fast measurement is what differentiates innovative ones.

Building a Flexible AI Roadmap

A well-crafted use case roadmap is a critical tool for any Org to gain value from AI. Inspired by conversations with @ztobi I thought I’d outline some different non-profit use case types and share how I’d combine them for clarity and direction, instead of chasing the hype.

What is an AI Use Case Roadmap and Why Bother?

It forms part of an Org’s AI strategy that defines the specific AI use cases that will be implemented, together with their goals and associated timelines.

By “AI Use Case” I mean a specific scenario where artificial intelligence solutions can be applied to solve a problem or improve a process within an Org.

Sometimes this use case roadmap is the AI strategy. Creating such a use case roadmap can be challenging. Here are some common reasons I hear why creating a good use case roadmap is so hard:

• Complexity: Juggling multiple use cases, data types, and technologies can be overwhelming.
• Uncertainty: The fast-paced nature of AI makes project planning a challenge since the landscape is always shifting.
• Resource Allocation: It’s tough to predict which use cases will give the best ROI.
• Time Constraints: The pressure to deliver quick results may make planning seem like a luxury.
• Lack of Clarity: Defining the potential impact or feasibility of a use case can be difficult.

It’s worth remembering that a roadmap is a high-level guide, not a blueprint. And it should be designed to evolve. Embrace its flexibility, and it’ll become a less daunting, more helpful tool. As the old saying goes: “Planning replaces coincidence by error.” That feels like a good line to live by.

Step 1: Assess Feasibility and Business Impact of Use Cases

When crafting your AI roadmap, first assess your use cases based on two core factors: feasibility and business impact.

Feasibility refers to how easy or hard it would be to implement a use case, considering the state of donor data, infrastructure, expertise, budget, and other constraints.

Business impact, on the other hand, gauges the potential value the use case could bring to your non-profit.

Step 2: Identify Use Case Categories

After assessing your use cases, you need to map and rank them based on their scores.

This process helps you categorize your use cases into 4 broad types:

(My design talent and steady hand shining through courtesy of Canva ^)

Champions: High Impact, High Feasibility

These are the dream use cases for any AI project.

Champions have both high impact and high feasibility. They’re your star players, set to bring significant value to your Org while being reasonably straightforward to implement.

Prioritize these use cases, as they’re instrumental in driving your AI roadmap.

Examples: Donor churn prediction, fraud detection, next best offer/step

Quick Wins: Lower Impact, High Feasibility

Quick wins may not bring a massive business impact, but they're just as vital.

With high feasibility but lower impact, these use cases are typically less complex and quicker to implement.

They serve as excellent showcases of what AI can do, and they're great stepping stones toward more complex projects.

Examples: Donor segmentation, sentiment analysis, Q&A Chatbots

Research Cases: High Impact, Lower Feasibility

These use cases promise high impact but are not readily feasible.

Whether it’s a lack of data or some technical constraints, these use cases need more time and resources to materialize.

However, given their potential value, you should keep them on your radar.

Examples: Predictive maintenance, personalized donation amounts… and autonomous vehicles

Reassess Later: Lower Impact, Lower Feasibility

Don’t discard these just yet!

Use cases falling into this category may seem less appealing due to their low impact and feasibility. But as technology evolves, what seems unfeasible today might turn into a quick win tomorrow.

For example, voice recognition technology was once considered a reassess later use case, but thanks to advancements in audio processing and LLMs it’s now almost a quick win. Donating via voice command has been available for awhile. I’m intrigued to watch its adoption.

Revisit these cases periodically to check if they've become more feasible or impactful.

Step 3: Build a Balanced Roadmap by Mixing Use Case Types

Creating a compelling AI roadmap is all about understanding how to mix and match different use case types to strike a balance between ambition and pragmatism, between quick wins and long-term goals.

Here are a few thoughts to achieve that:

1. Mix Champions and Quick Wins: While it’s essential to focus on high-impact, feasible use cases (Champions), don’t overlook the Quick Wins. These less complex projects often have shorter development times and a high probability of successful implementation, which can keep your team motivated and demonstrate the value of AI to stakeholders - especially when you’re just starting out.

2. Keep an Eye on Research Cases: These are your long-term goals. They might not be feasible right now, but their potential impact is high. Regularly revisit these cases to check if technological advancements have made them more feasible - ideal for more mature organizations.

3. Don’t Dismiss “Reassess Later” Use Cases: These are low on both feasibility and impact, but that doesn’t mean they aren’t valuable. They often provide great learning opportunities or yield unexpected benefits. Also, as technology changes, these use cases can turn into quick wins. Keep an eye on them throughout your journey!

4. Identify Common Data Sources: When choosing between use cases, favor those that utilize the same data sources. It helps to streamline your efforts and reduce complexities.

5. Maintain a Flexible Approach: Remember, your roadmap is a guide, not a strict plan. Keep it adaptable to accommodate new insights, changing Org goals, and technological advancements.

Wrapping up

Crafting your AI use case roadmap isn’t a one-time task, but an ongoing process.

Start by assessing your use cases for business impact and feasibility. Categorize them, then balance your roadmap with a mix of Champions, Quick Wins, and Research Cases. Remember, “Reassess Later” cases might soon turn into tomorrow’s Quick Wins.

Your roadmap is flexible. As new insights and technologies emerge, revisit and adapt it. The key to AI success lies in iteration.

Don’t just create a roadmap - evolve it.

Now onto the fun stuff:

Good Reads this Week

Nike moves to embrace more wholesale - Forbes call it A Masterclass In Omnichannel Strategy

The value of personal data in internet commerce - academic study working with Alibaba that demonstrates the value of using data well in ecommerce (think donation transactions)

SPN readers don’t need to be convinced that brands can be built with digital, but many others do. So this piece by a Jellyfish exec is worth reading: Ignore the sceptics, brands can be built on digital platforms

Global Entertainment & Media Outlook 2023–2027: TMT | PwC

Best read of the week on AI is probably the McKinsey take The economic potential of generative AI - The next productivity frontier

Advertising is not just TV spots

The rapid growth around retail media isn’t limited to the West or just stores, and I’m fascinated by other markets to see what can be learned, so sharing these resources:

Ecommerce in Southeast Asia 2023 (snapshot) - Singapore and Latin America Retail Media Advertising Trends 2023