SPN. 158: How to Build Your Data Strategy
Plus, Cannes winner; TikTok AI tools radically changing the nature of content and influencer marketing; plenty of Jobs & Opps
A very warm welcome to all the new subscribers. And apologies if you received a draft of this late last night! I’ve added some jobs since.
You’ve joined a community of over 3k marketing and fund raising operators at mission-driven Org’s. I’m thrilled to have you as readers and truly appreciate your feedback and support.
In this week’s SPN:
Short “2025 guide to…” data use and collection with 5 must-have categories
New Zealand Herpes Foundation wins Cannes!
TikTok release 5 new AI tools that will radically change the nature of content and influencer marketing.
and, plenty of Jobs & Opps that took my fancy this week
Let’s jump in!
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Cannes Grand Prix Winner: Glass Lion for Good
A New Zealand creative agency Motion Sickness and New Zealand Herpes Foundation landed the Grand Prix at the Cannes Lions International Festival of Creativity in the Glass Lion for Good category.
The Grand Prix was awarded for “The best place in the world to have herpes”, which also picked up Gold in the Health and Wellness Lions category too.
The core campaign asset is made up of a 90-second spot, which leverages tons of dry humor and features former rugby union (star) coach Sir Graham Henry as its spokesperson. Every single person in NZ knows who he is - rugby is life there. Henry announces that New Zealand has lost its purpose… so he challenges people to make New Zealand the “best place in the world to have herpes”.
The goal of the campaign was to destigmatize herpes in New Zealand by talking more openly about the subject.
The Glass Lion for Good Grand Prix is a specialist award, meaning it’s a category that can’t be entered directly at Cannes. Work becomes eligible to be considered if it’s awarded a gold Lion, or an equivalent Lion like a Titanium Lion, and is created for a nonprofit org.
There’s a cracking quote from the jury president of the award Judy John, global chief creative officer at Edelman Global from the press conference where she just gushes about how the campaign is so “brave, outrageously creative, wildly ambitious, educational, not the usual combinations of words you will find to describe the Grand Prix for Good.”
→ Hat tip to the Foundation. That kind of risk-taking is brave AF and to pull it off - woah! The creativity is off the charts. The execution is truly flawless. It takes the notion of marketing “effectiveness” to a whole other level. Bravo 🇳🇿 👏 🇳🇿 👏 🇳🇿
Last year’s Cannes SDG winner was Renault - covered in SPN #107 last June - scroll to the headline “Proving the Value of Creativity”.
Changing the Nature of Content and Influencer Marketing
Big news from TikTok. They’re releasing 5 new AI tools that will radically change the nature of content and influencer marketing.
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1. Image to Video: instantly transforms static images of campaign, program shots and brand messages into legit engaging TikTok-style videos.
2. Text to Image: generates full motion videos with a single prompt, just from describing a creative concept you have in mind.
3. Virtual influencers: generate digital avatars to create donor-generated content (UGC) style content, selecting from authorized stock influencers or by customizing your own. This is massive.
4. Showcase products: get your digital avatars to showcase a product, whether it’s a piece of brand swag they try on, a bucket they hold up, or a mobile app they demo on their phone.
5. AI dubbing: translating videos into 10+ languages, allowing global assets to be localized at speed.
I’m pretty sure that out of all the features, the virtual influencer is the one that will attract the most discussion. It’s hard to overstate what a game-changer these tools are.
Worth noting that TikTok says the avatar influencer content will be clearly labelled as AI generated, to make sure it’s not confused with “real” creators. Blurred lines.
Which poses the question → is it helpful to think about digital avatars as an extension of influencer marketing? Or should we think of them more like interactive mascots, or fictional characters that star in ads?
It’ll be fascinating to see which Org’s experiment with these features first, and how you position each to your donors.
Jobs & Opps 🛠️
Johnson & Johnson: Director, Social Impact ($146k - $251,850)
Fundraise Up: VP, Customer Succes ($196,000 - $210,000)
Feed the Children: Director of U.S. Foundations, Organizations and Institutions ($110,000 - $120,000)
NYC Outward Bound Schools: VP, Marketing, Development, & Communications ($180,000 - $220,000)
Twilio: Social Impact (they have a “Sr. Director of Marketing, Social Impact” role open too but not listed. Tap Erin https://www.linkedin.com/in/erinreilly2)
National Geographic: Senior Manager, Paid Media
Crisis Text Line: VP, Philanthropic Partnerships ($170,016 - $200,000)
NextGen America: CEO ($212,000 - $220,000)
Peninsula Open Space Trust: CFO ($279,990 - $311,100)
Visit Napa Valley: Director of Communications
University of Minnesota: Chief Communications Officer ($200,000 - $245,000)
National Domestic Violence Hotline: VP Communications ($150,000)
National Museum of Women in the Arts: Marketing & Communications Leader
Goodwill SoCal: Chief Marketing Officer ($200,000 - $250,000)
Global Citizen: VP Content
British Asian Trust: Executive Director, Fundraising & Comms
→ Many more job opportunities listed on SPN’s sister site: Pledgr
Privacy-Compliant Data Strategy
The 2025 version.
→ What to collect, where to get it from, and how to use it.
The data landscape has shifted - the entire industry is moving from “deterministic” (precisely identifiable) data to “probabilistic” (estimated) data.
Google Analytics 4 was a canary in the coal mine, with a move away from “cookies” and “sessions” to “events” and “users”.
And the most recent announcement from the Google Marketing Live conference is that Google is slowly moving GA4 away from attribution – a pinnacle of deterministic measurement and a report that we all got so used to – to Marketing Mix Modeling, based on… who knows what Google will base it on! Most likely some “proprietary model”, taking into consideration even more “proprietary data”.
Google is increasing its partnerships across the industry to include more data straight into the models. Whether it will prove accurate or not is the question to be answered. But the pattern is clear.
At the recent Cannes Festival, probabilistic measurement, machine learning-driven segmentation, and Generative AI for creative were the talk of the show. All major for-profit advertisers are claiming the pivot towards privacy-preserving technologies, underscoring the role of AI as the sole enabler of euphemistic 1:1 marketing - but this time, unlike before, with no visibility into what’s happening behind the curtain.
This is becoming a new reality – more modeled data, with less insight about what that data “is”.
How can Orgs adapt, make sense of the data they have or can get access to, and use it to increase the dollars raised?
Here’s a short “2025 guide to data use and collection”, with 5 must-have categories:
1. Direct Donor Data Collection (First-Party Deterministic). The most useful dimensions are:
Email address
ZIP code (or city, if ZIP is impossible)
Donation frequency (one-time vs. recurring)
Donation amount ranges
Donation topic or mission pillar
Donation timeframe (EOY donor, emergency donor, post-paycheck donor)
Use Cases:
Enrichment: use Email and ZIP codes for enrichment with other data types below.
Google and Meta (same application): Create Customer Match audiences to retarget existing donors and build Similar Audiences for prospecting, broken down by donation dimensions to inform creative assets.
Google-specific: Use donation dimensions in paid search ads, overlaying them with RLSA targeting. Only serve Paid Search ads with language aligned with each donor’s donation topic.
2. Behavioral Website Data (First-Party Deterministic & Probabilistic). The most useful dimensions are:
Specific page views (donation form, "About Us", "Our Impact")
Donation form abandonment (yes/no, how deep in the form)
Engagement depth (number of pages viewed, video views, PDF downloads)
Number of sessions on the website
For donors, tie this data to the dimension in category #1 using the Transaction ID.
Use Cases:
Remarketing across platforms: Build lists based on users who visited the donation page but did not complete a donation. Align the creative with the depth of the donation form activity before drop-off and adjust CTA accordingly (“finish your donation”, “consider giving again”).
Prospecting: Apply increased bid modifiers to look-alikes of those with deep engagement on the website.
3. COOP Data (Second-Party Deterministic). COOP is not going anywhere anytime soon. While it’s hazardous for prospecting since COOP databases include only those already donating to at least some Orgs and inadvertently have no “true” prospects, it still has use in your Org’s fundraising tactics to improve performance amongst “core” donor audiences. The most useful dimensions from any COOP your Org is partnering with:
Affinity segments (e.g., causes previously supported, environmental vs. health causes)
Work with the COOP provider – Data Axle, Wiland, etc. – whenever possible to get the source data instead of already heavily modeled data. Their models tend to underperform against the Google or Meta ones.
Donation amount ranges
Most recent donation time
Number of Orgs a person is donating to
Donation share of wallet out of annual HHI.
Use Cases:
Look-alike prospecting: Use COOP data for direct targeting and LAL modeling in your Org’s prospecting efforts. Be unapologetic about RoAS – COOP data comes with strings attached, and the only way to justify these strings is if you are getting at least 20% better performance on first-donation RoAS against your other, “pure” prospecting efforts.
Enrichment of your donor file: Use the COOP database to add unaltered, non-modeled attributes to your Org’s donor file. Segment your donors based on the COOP donation dimensions to better understand who your donors are and their share of wallet and differentiate by age group to spot the generational trends and predict a profile of “new” donors that aren’t in any of the COOPs yet.
4. Machine Learning-Modeled Data (First-Party Probabilistic).
Predicted donor lifetime value (LTV) from Google Analytics 4
Likelihood to churn from Salesforce (other CRMs are starting to include it, too)
Predicted upgrade potential (more and more CRM tools start including it, also disguised as “likelihood of next purchase”)
All these dimensions can also be modeled by your in-house data team or an agency based on the 3 data types from above.
Use Cases:
Remarketing: Use the Churn and Upgrade dimensions to create targeted campaigns to your existing donors. Experiment with tiers of likelihood - e.g., target campaigns to “90%” vs. “85%” churn probability audiences, cross-excluding them from one another.
Prospecting: Apply bid increases based on the predicted lifetime value. Create separate CPA targets by pLTV buckets, always keeping the Target CPA at 30% of the pLTV value.
5. Contextual Targeting and Cohort-Based Data (Probabilistic)
Content categories consumed
Broad audience cohorts based on Affinity and In-Market segments
Use Cases:
Across all tools, use these categories with ZIP Code-level targeting. This way, your campaigns can later be merged with the CRM data to act as free enrichment data, but not at the individual level.
OK, that’s all for today.
I hope you’ve found one nugget today that you can put into play next week.
If you enjoyed this SPN, please consider sharing with your network. Thank you to those that do.
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And huge thanks to this Quarter’s sponsor Fundraise Up for creating a new standard for donor experience.
Now onto the fun stuff!
Weekly Reads 📚
A.I. Might Take Your Job. Here Are 22 New Ones It Could Give You (NY Times)
The CMOs comeback - good McKinsey thinking (McKinsey)
Why YouTube is trying to replace your favorite TV shows (BI)
The FT interviewed Samsung’s European marketing chief Benjamin Braun on emotional storytelling in the age of AI and got into their Olympic product placement: “I can’t just give out 17,000 phones. It needs to return value.” (FT)
The AI Monetization Dilemma: Beyond Keywords and Clicks (ExchangeWire.com)
Data in the age of AI: A conversation with Mark Birkhead of JPMorganChase (McKinsey)
How the culture war is remaking advertising (FT)
The Effectiveness Equation (Google)
How McKinsey Destroyed the Middle Class (The Atlantic)
Britain says Google's online-ad commitments no longer needed (Reuters)