Some Personal News
#40. Lessons on Strategic Alignment: language, tools, data models and culture, and Why Distribution is the Most Defensible Part of any Donor Acquisition Strategy
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.
Objective-Driven Growth Experiments + Distribution
Assuming donor acquisition is a priority for you – here are some shifts I’ve been implementing either in my approach or in practice to reduce friction in the donor experience. See what you think:
1. A Shift to Donor-Related Problems
We’re not nearly as donor-centric as we’d like to claim.
We tend to prioritize Org/business-related problems over donor-related problems.
“We need more engagement on our organic social posts”
“Our ads don’t seem to be working”
“We need content that drives fundraising AND advocacy”
Timely business outcomes is what every Org prioritizes.
But what would happen if we looked at it from the donor’s side? It might sound like this:
“Do donors find our raison d’être credible and relatable?”
“Is our messaging and urgency in line with how they want to donate?
“Is our value prop even clear enough to donors?”
The idea is to run objective-driven growth experiments through the lens of donor-related problems. Meaning, develop hypotheses based on what you’re seeing from donors.
“Yeah, yeah – we’ll get to that. But we need dollars right now!”
Short-term thinking gets in the way. But breaking that cycle is one of the key ways to lower friction for donors. And one of the shifts I believe winners embrace.
2. A Shift Away from Funnel Thinking
I’m still hearing and reading that most ideas in non-profit marketing are still either based on or leading to “funnel thinking.” It’s still no different in B2B.
People can’t seem to shake this false idea that prospects move through some kind of linear path to become donors, or in the case of B2B “buyers”. We just have to fill it up and add friction as we go in order to get the results we need.
It just doesn’t work that way. And especially not when you’re operating with LTV as a critical metric.
It seems to work when times are good. But when times get tough, and that time feels like it’s galloping around the corner, the truth is revealed. A funnel orientation is inefficient at best – and detrimental at worst.
So what’s a better way?
Distribution. Do whatever it takes to establish a reliable way of getting in front of your donors. One that can sustain and compound.
Like creating a comprehensive content marketing ecosystem that includes various forms of content and first party data. Your data. Or a newsletter people actually read. Or an event strategy that builds on itself.
Distribution is the most defensible part of any donor acquisition strategy and a requirement for low friction acquisition. There is oodles of opportunity awaiting the Org that makes a conscious shift from funnel thinking to distribution building. More on this soon.
4 Key Practices for Better Alignment
You can’t afford disconnected AI and BI teams. Bear with me …
In many organizations, data science and BI teams are historically located in different departments, have different levels of leadership, and lack common goals. This hinders effective collaboration, resulting in confusion, errors, and diminished trust - especially among business users in organizations with low analytical maturity like most Non-Profits.
Now switch out “data science and BI teams” and insert “Marketing and Fundraising”. My experience working with BI and AI teams this week mirrored past experiences trying to align marketing and fundraising teams.
In addition to organizational challenges, the data science and BI teams were struggling to collaborate effectively. They had:
Different data models
Different data cultures
Not too dissimilar to Marketing and Fundraising teams. To overcome these hurdles, there are likely many best practices for teams to work more closely together but I’ve narrowed it down to 4 key ones. I’m going to use data science and business intelligence (BI) teams as the example here and let you fill in the gaps for similarities to your situation. Take what is helpful, leave what is not.
Let’s unpack this piece by piece!
1: Align Language
One of the biggest challenges for teams is the lack of a shared common language. Data science and BI teams often use different terms and concepts, which can lead to confusion and misunderstanding.
For example, a Data Scientist may use the term “model” to refer to a predictive machine learning model, while an BI analyst may use the term to refer to a relational or dimensional data model in an OLAP cube.
Similarly, terms like “measures”, “dimensions” or “report filters” are everyday language for BI professionals, while Data Scientists may argue about the right set of “training examples,” the best “predictive features,” or “sampling methods.”
The lack of a common language can lead to a lack of trust between teams and poor communication overall, which in turn leads to poor collaboration, as it can be difficult for teams to share and understand each other’s work.
Using a shared vocabulary
To establish a common language between data science and BI teams, organizations could create a glossary of terms used by both teams. It feels very prescriptive but in practice it’s incredibly handy. I’ve seen glossaries include definitions of common terms, as well as explanations of how they’re used in the context of data science and BI. Particularly useful when onboarding new team members.
In addition, cross-functional training sessions can be held to ensure that both teams have a clear understanding of the terms and concepts used by the other team.
Maintaining centralized documentation
Furthermore, having clear documentation and procedures in place, which the team can refer to, can also help to align the language. I’ve seen this done with product teams a lot.
This documentation could include data governance policies and procedures, and other important documents that teams may need to refer to.
2: Align Tools
Another challenge faced by data science and BI teams - or Marketing and Fundraising - is the use of different tools and technologies.
Data Scientists often use programming languages such as Python or R, while BI analysts typically use tools such as Tableau and Power BI. These different tools can make collaboration difficult, as it can be challenging for teams to understand and share each other’s work.
For example, a Data Scientist may use Python to build a predictive model, but the BI team may have difficulty understanding what the code is doing and how to interpret the results of the model - let alone integrate it into their ETL pipeline.
On the other hand, a BI team may use Power BI to build a dashboard. Meanwhile, the Data Scientist can’t access the transformations and KPI calculations that are done there - a real life example from a meeting of mine this week - so they have to painstakingly reconstruct the results in Python and verify that they’re getting the same results shown in the BI dashboards.
Define a core data stack
To overcome this challenge, organizations should be encouraging the use of common tools for both teams - each for their specific tasks. Data Scientists could be trained in BI tools to use their data visualization capabilities, and BI analysts could be trained to read (and perhaps even write) Python or R code. An even more elegant solution would be for data science and BI teams to commit to using a common technology as much as possible. (Oftentimes, that’s SQL).
While it may be more tedious for a Data Scientist to write a data preparation pipeline in SQL, the advantage would be that BI teams can easily use this pipeline. So in this case, the upside would be not only increases in efficiency and improved data quality, but also that both teams are empowered to understand and interpret each other’s work.
Needless to say, having a common data platform for data storage and management is an important catalyst. By using a common platform, teams can access the same data and collaborate more easily. This also helps with data governance and security compliance.
3: Align Data Models
Nothing separates data science and BI teams as much as their data models!
Data Scientists typically require data in a denormalized, granular form, while BI analysts typically require normalized, dimensional, and relational models.
These different models can lead to confusion and errors when teams share data.
For example, a Data Scientist might use a complex model to predict donor behavior but might use a different logic to create critical business entities, such as defining donors, calculating their revenue, or assigning donor-specific attributes. These different definitions can create a lot of confusion and distrust among teams - and ultimately lead to poor results.
Using a shared semantic layer
To overcome this challenge, you could use a centralized, well-managed semantic layer that includes feature stores for models used in production and serves as a source of truth for both Data Science and BI teams. If you pursued this route then data models used by both teams would need to be documented, including the purpose of the model and the appropriate owners who can answer further questions. Result being that both teams have a clear understanding of the data models and how they’re used.
In addition, joint data modeling sessions can be held to ensure that both teams have a clear understanding of the data. Below I talk about the power of working together to discuss the data, identify any gaps or inconsistencies, and develop a common understanding of the data. But ultimately you want teams working with the same data and for the insights gained to be accurate and actionable.
4: Align Culture
The cultural differences between data science and BI team members - or Fundraising and Marketing - can hinder effective collaboration.
Data Scientists and BI analysts often have different communication styles, problem-solving approaches, and ways of working. These cultural differences can lead to misunderstandings and delays in problem solving.
Personal experience has shown that Data Scientists have a more experimental and research-oriented approach, while BI Analysts have a more regimented and business-oriented approach. Neither is better or worse, just an observation.
BI teams and processes have typically evolved over decades of working with data warehouses and well-managed reports. Data science teams, on the other hand, have often been hired with a more agile mindset, commonly incubated in “data labs” trying to explore and push the boundaries of what’s possible with data. Different priorities and a lack of understanding of each other’s perspectives can become the norm.
Let people collaborate in cross-functional projects
To align cultures, some of the most fun I’ve had is when working on cross-functional project teams. You quickly develop a better understanding of each other’s roles and responsibilities, and learn to appreciate the value each team brings to the org.
Regular communication and knowledge sharing sessions are also very helpful, although for the record I’m not a “Lunch&Learn”-type. But whatever is going to give teams the space to share their insights, experiences, and best practices is going to win. And can help break down barriers and build trust between teams.
By aligning cultures, you can ensure that teams work together more effectively and contribute to an overall more data-centric and data-driven culture across the organization.
I laid out four key practices for bringing people closer together and producing more value: aligning language, aligning tools, aligning data models, and aligning culture:
Align language: Ensure a common understanding of the terms and concepts used in your world.
Align tools: Ensure that teams work together with high efficiency.
Align data models: Ensure teams produce insights that are accurate and actionable.
Align culture: Increase the effectiveness of teams and make sure their combined output is larger than the sum of its parts.
Implement even just one and I’ll wager that you’ll create a more seamless and effective ecosystem within which operate.
Now onto the fun stuff! >>
Good Reads this Week
Listen > Equity and AI in Global Health: Exploring AI, Building Chatbots and Embracing Discomfort
TikTok published a new report on How TikTok drives business impact for advertisers.
Beli: Food for thought - Breaking down how a restaurant-ranking app works shows us the general principles of vertical social networks.
Jobs and Opps
ACLU: Head of Brand Studio
American Diabetes Association: VP, Marketing Operations
Charity Water: Experience Lab Director
The Marfan Foundation: Chief Comms and Marketing Officer
The Partnership to End Homelessness: Director, Donor Relations
The United Way of New York City: SVP & Chief Development Officer (CDO)
UNICEF UK: Head of Sport
VOW for Girls: SVP Fundraising
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How can I help you? I use my experience, expertise and network to help mission-driven organizations solve interesting problems and grow.