5 Steps for getting your data house in order
I have a confession, data organization and cleansing is not my favorite part of Data iNFORMED Fundraising. I much prefer the analysis and the insight. The development of innovation governed through the insight of data informed. The world of content development, segmentation of donors, budget development and campaigns catered to specific audiences etc. etc. etc.. Not to worry, all of that is coming but this is the most important place to start.
It’s one of my favorite questions, one little word…..that packs quite a punch!
Let’s start with: why do you want to be data-informed? What problems are you trying to solve?
I hate the obscurity of big statements, like “We strive to be Donor Centric”. I agree with it but inside of that statement is the real problem. You want to be in complete understanding of your audience to position the correct messaging to them at the right time, using the most effective media to:
- Retain, the relationship (Churn Prevention)
- Grow the relationship
- Develop a New relationship
- Increase engagement
- Access their network
In Scott Stratten’s Book, “The Book of Business Awesomeness: How Engaging Your Customers and Employees Can Make Your Business Thrive” he states: “Most of the time, we focus our sales and marketing on acquiring new customers – even though it’s a well-known fact in business…that it costs waaay more to acquire new customers than keep your current ones….We are losing customers in the name of getting new bright and shiny ones.”
Keeping existing donors relies on a solid engagement strategy. Donor engagement is built by analyzing your donors’ giving patterns over time and using these insights to inform your actions.
If you have the knowledge of the content, timing and media that would drive donors to action you would, of course, use this knowledge to build a campaign, plan an event, build a budget or maybe just thank a supporter (engagement).
So, why do you need to get your data house in order?
In an article by Colin Stewart (“A Practical Guide to Creating a Donor-centric Fundraising Program”, July 11th, 2016) he states that: “While a robust donor strategy and experience delivery are at the core of any donor-centric program, it is also true that an organization must have the following enablers in place to support them: Data and data infrastructure: Do you have a 360-degree view of your donors? Does it integrate online and offline donor records, transactions, promotion history, and fundraising costs?”
How you go about becoming data centric rest fully on the state of your data. Bad data leads to bad decisions, and bad decisions mean less than optimal performance and results. You need to have high-quality, decision-making information at your disposal. Your data needs to be accurate and consistent, and structured in a way that serves you, the decision-maker. Your Data House needs to be built to support you and your organizational communication needs!
Perhaps you are an organization that seeks engagements through petitions and surveys, or you are grass roots and conduct your activities through events and face to face. Each of you have unique needs that influence how you collect, retain and use data and what benefits you expect to derive from this data.
Many NFPs rely on direct response programs (and the data they collect through these programs) to connect with their donors in unique and diverse ways: War Amps and their Key Chain Program, Kidney Foundation and their Kidney Car Program, Heart & Stroke Foundation’s Jump Rope for Heart Program, to name a few. Information is used by these organizations not only for fundraising but to increase their engagement with supporters in other, non-financial ways.
Where to begin?
There are 5 simple steps to getting your data house in order. Well, simple to explain, that is. Actually following these steps may be a bit less simple. But still very necessary.
- Step 1: Requirements definition: What decisions do you need to be able to make with your data? What data do you need to make these decisions?
- Step 2: Information types: Types of information needed (hierarchy, indicator, totals, averages); Breadth and depth of information (history); Raw versus transformed data;
- Step 3: Data organization and accessibility: How should data be organized and made available for use? Frequency? Tools available for this.
- Step 4: Data quality: What quality means for strategist, analyst and programmer
- Step 5: Data rules: Organizational memory rules (Lexicon); database rules;
In the coming weeks, we will be blogging about these steps from 3 very important perspectives:
Strategic / Business (Dean McJannet):
Analytics (Amanda Pagoulatos)
Systems/Database (Maciek Klimkowski):