Fundraising+ Understanding Your Data

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• Transcript

  - Hello everyone and welcome to the first Philanthropy Series Webinar of 2025, Fundraising and Understanding your Data. For those of you that I haven't met before, my name is Jake. My pronouns are he/him, and I am a fundraising specialist based here in New York. And I'm joined today by two other members of our global fundraising team, Sara Stevens based in Austin, Texas, and Miriam Wright based in Manchester. Today we're also very excited to be joined by Raice Bassett, a reporting specialist here at Spektrix who will introduce some of the reporting tools that Spektrix provides and some tips about using them. We are recording today's session and we will send the recording and any other resources mentioned by the team today by email after the session. This webinar will last approximately an hour, including time for questions at the end. And we do have live captioning available that you can turn on and off using the CC button at the bottom of your screen. So leveraging your data to solve a problem is a lot like putting together a puzzle. All the information that you record into your system is there, but without you taking an active role and making sure the puzzle has all of its pieces in the box, putting the right pieces together, and knowing what kind of picture you're trying to make, your data will remain a collection of pieces that don't quite fit together. While Spektrix provides a host of tools that can sort those pieces and speed up the process, it all relies on us as fundraisers to understand how to put all of that information together to tell a cohesive story. Working with data this way is incredibly and increasingly necessary in all aspects of our fundraising work. As the sector as a whole faces mounting pressure to raise more in less time and with fewer resources, using these data-driven insights can unlock new fundraising opportunities and identify challenges early. For some of you, data entry and pulling reports may be a far cry from the relationship building skills that make you a strong fundraiser. And even for those of you that specialize in this area, you may still face existing challenges or unrealized potential. Today we want to help demystify how you can engage with your information and emphasize that the fact that it is your data you are working with. From the way you enter information into your system and the tools you choose to organize it, to how you pull data out of the system, and even how you interpret the story your data is telling, we'll be diving into how you can better understand each step of this process. Next up, Sara will talk about the importance of clean data entry. Followed by Raice from our reporting team who will introduce the tools Spektrix offers to organize and arrange your information. I will expand on what to ask your system to pull the data you want to see. And finally, Miriam will walk us through interpreting what it all means once you pulled it, challenging biases and assumptions we may bring.
  
  - Thanks Jake. So before we even think about pulling data, there needs to be data to pull. And how you enter your data has a huge impact on how you can report on it. My personal golden rule of Spektrix is that you can only get data out of your system that you put into your system. And how you input that data tells a story and affects your reporting. So data hygiene is crucial. So let's chat through some of the root causes of uncleaned data. First things first, when importing data across, in some cases, multiple system, there's room for duplication. And some fields in one system may not have a direct translation in another system. For example, if you're importing donation data from a third party that runs your gala's auction, maybe a donor created an account for that auction using a different email address. And since that email address doesn't match an email address in Spektrix, it's going to create a duplicate customer record, which means you won't have a clear history of that donor without merging those accounts. We also recognize that there has been a lot of staff turnover in our sector over the past few years. And with new people come new ways of working, which can be exciting and refreshing because it leads to innovation and new ideas. But additionally, those new ways of working could be out of necessity because the way things were done haven't been documented in a clear way. And if there's no documentation then that could lead to inconsistent data entry. You may have been tracking something as an attribute in the system and someone new comes along and creates a tag because they didn't see how it was tracked before. Now there's two fields doing the same thing, creating that duplication of work. So before creating new fields, you need to ask yourself, how are we going to use this data once it's in the system? 'Cause that will inform you on how you enter it. And when making decisions about how to enter your data, I want you to think about it in like two different categories, quantitative and qualitative data. Determining this will help you decide on how to track it in Spektrix. So let's chat through some scenarios. So when we're talking about quantitative data, we're looking at things that can be counted or measured like transactional details, like the price or the number of donations made within a timeframe, or say the number of donors who meet a criteria like who live in certain zip codes or have certain tags on their customer records. If you simply wanna know the number of those people who meet that criteria, you can use the Update count button on your customer list to get that quantitative piece of data. But if you need more qualitative information like who those people are and what their names are, their contact details, things that you can't assign a numerical value to, then you can pull that from the output of a customer list which Raice will touch on a little bit later. Other examples of qualitative data include anecdotes about a donor that may be tracked on something like the blue or pink sticky note, research on the donor that may be tracked via something like our activity functionality, and additional custom fields tracked as tags or attributes in the system to capture things like relationship manager, drink preferences, accessibility needs, or interests, as shown here in our tag group. It's important to know how you want to track both types of data in your system for clean and consistent entry and what modules in Spektrix align with those goals. And if you're ever unsure about the best way to track that data in your system, reach out to us on the support team. We're more than happy to dig into this with you and ask these questions to guide you in the best direction for what you need. And that's what happened when two team members from Donorly reached out to us. Now Donorly is one of our partners. And they partner with other organizations looking to jump state, jumpstart their fundraising by setting up robust pipelines. So sorry, my script just disappeared. Two ticks. Sorry about that, folks. Live events like live theater, we're all used to little technical glitches. So sorry about that. Donorly partners with organizations looking to jumpstart their fundraising by setting up robust pipelines and systems. They work with organizations who are ready to take bold steps in growth and can help fast track capital campaigns to achieve 75% of their goals in just six months. Weston Ganz, head of Prospect Research and Strategy at Donorly, and Lauren Siff Siegel, a consultant at Donorly, were brought on to work with a mutual client of ours after some staff turnover that occurred at their organization so they could come and fill in some gaps while they found more permanent staffing. In their first few weeks, they ran into a big issue with some of their data. And that was that their finance team was unable to reconcile their Spektrix fundraising data. So they reached out to our support team to help. This led to a consultation led by yours truly with a clear goal, to be able to translate the data that lives in Spektrix, to be mutually beneficial with their accounting software, which would then help their finance team. Now I worked with them to do a bit of discovery work, which included reviewing how they enter gifts into the system and what their current fund and campaign structure looked like. We realized that they had quite a few funds in their system. Now funds are the buckets of money that a donor can designate their donation towards, like general operating support, education, production, et cetera. And this client had a fund per revenue bucket and per revenue stream like individuals, board, foundation, government, and more. With upwards of five streams of revenue per bucket of money, there were nearly 50 funds in their system that I knew that we could consolidate and streamline into between about like five and 10. And all of these funds were duplicating a lot of the work that they're already pretty robust campaign structure was accomplishing. For those who may be less familiar with campaigns, they're a way for you to track progress against target amounts that you have set on an organizational level. Now campaigns are a tool of the Opportunities interface. So if you don't have that feature and are inspired by all it can do, please reach out to us. So that campaign structure can be up to three layers deep and answer crucial questions that align with your budget and your finance team, which is perfect for the goal at hand. The top level campaign aligns with the question when. This typically aligns with your organization's financial year. However, if you have a campaign that spans multiple financial years and also has its own separate budget like a capital campaign that might be split out. Then you can take that a step further with your sub-campaigns, which oftentimes answer the question who. This represents the stream of revenue coming into your system, like individuals versus foundations. So in this client's case, they were duplicating the work by creating a fund per stream when that's already captured by the sub-campaign. And lastly, we have the sub-sub-campaign, which tends to answer the question how. How are you soliciting this donation? Did it come through an end of year appeal or was this a Giving Tuesday gift? The client's campaign setup mostly aligned with this framework which is great, but there were also some things that could be added to it. So here's an example campaign structure we have, Financial year 25, Individual Giving, Giving Tuesday. Each of these have a code assigned to them, one flagging that this is an FY 25 gift, and the other two indicate that this goes into the individual giving line of their budget or 505 as their finance team knows it. Codes can also live on the fund level. So we were able to determine that there was one code in their accounting software that aligned nicely with the purpose of Spektrix funds and another code in their finance software that nicely aligned with the purpose of Spektrix campaigns. And lastly, the target amounts of campaigns would ultimately be beneficial to help them track progress to targets throughout the year, which could be tracked via some of our standard reports like the campaign summary report as shown here in a condensed way to fit onto the slide. Now this report shows you how close you are to those target amounts that we just looked at by tracking what has been actually donated against each campaign, what has been pledged but not yet received, and what is forecasted based on potential revenue in your opportunities pipeline. So not only did enhancing the structure help their finance team with their structure, but taking the data entry a step further also unlocked the ability to use the standard report that they weren't able to take advantage of beforehand because they weren't inputting those target amounts, thus digging into some of that untapped potential that Jake was mentioning earlier. Because this was impacting their finance team just as much if not more than the fundraising team, Weston stressed, "Spektrix is not just a tool for your fundraising and marketing teams, but a database that your finance team should also be comfortable with, too. Getting clarity on how they look at data will help your reconciliation, keep data organized, and improve communication." So we worked really closely together in a Google sheet so that they could draft out what this fund and campaign structure would look like. And this also meant that they could have their finance team go in and provide the corresponding codes so we knew exactly what they needed. This also offered me the opportunity to look at it and provide comments and feedback from Spektrix's perspective. And this overall alignment was crucial and collaborative. Which led to a new structure that consolidated their funds into the six shown here and to continue their well-thought-out campaign structure, but add in those helpful fields like codes and target amounts to help with their reporting. We then ensured that they felt comfortable processing gifts in their system into the proper funds and assigning the proper campaigns so that every gift had every data point assigned to it. Again, to ensure that all stakeholders had the data they needed to do their job. Once everyone signed off on the plan and the fields associated with each metric, it was time to put it all into their Spektrix system. We decided it would probably take up too much time to update previous year's data to this new structure. As Weston put it, it can be a hassle. And if those books are closed and the audits are complete, you may wanna implement this change in the upcoming year. So they were almost done with their financial year at the time of this project, so we decided we'd implement the new way of working at the turn of their financial year so that no previous data needs to be modified and they could continue report consistently with that data and report with the improvements moving forward in a clear way. Now with this new structure, the client has a clear connection between what they need in Spektrix and what finance needs from Spektrix. And adding those target amounts against campaigns allows them to get real time information on how close they are to their budget goals at any given time throughout the year without having to check in with their finance team. I wanna thank Lauren and Weston for their dedication to this project and for letting me share their story with you today. If you're interested in learning more about Donorly, you can scan the QR code on the slide here. And we'll also be sure to send out links to connect with them in the post-webinar communications. So data cleanliness can be a big project and take several weeks as it did with the team at Donorly, but you can start small and still make a big impact. So whether you have one hour, one day, or one week to dig into your data hygiene, let's take a look at some things that you can accomplish in those timeframes. So if you only have one hour to dedicate right now towards looking at your data hygiene, I'd start with your and customer lists and getting rid of what isn't necessary anymore. They have dates that you can reference about when they were run or last edited. So go through the ones that haven't been run over a certain period of time and either run them to see if they're still relevant and maybe they'll give you insights that you didn't know that you had or delete them if they're no longer useful. I'd also recommend adding descriptions wherever they're available, on reports, customer lists, attributes, tags, and more. This allows you to provide context on what each field is in your system and what it's meant to accomplish, which will ultimately clear up any future questions and prevent duplication of work. If you have a day's worth of time to dedicate to this project, I'd recommend doing the same customer list and report audit, but also assess your data entry paths. Build out a customer record, enter a donation and a ticket into your basket, add an opportunity to the customer record and build a corresponding activity for it. Where are the areas of friction when you're doing those things? And are there unnecessary things like customer or order attributes that could be removed? Or are there tags that could be consolidated? And finally, if you have a week's worth of time to dedicate and invest into this type of crucial cleanup, I do everything that has already been mentioned. Plus, flag potential duplicates in your system and begin merging customer records. Audit your fund and campaign structure similar to how we did with the Donorly team. Add things in like codes and targets so you can optimize usage of some of our standard reports in the system. Engage with other team members about what roadblocks they're hitting. We've seen a few clients create Spektrix user groups at their own organizations and they have regular meetings about what they're doing in the system. This has ensured that these teams maintain their data cleanliness and are staying on top of system projects that they're working on to reduce any sort of duplication of work. And last but not least, document your practices, like creating a customer record and processing a donation. This way, if you ever do end up leaving your organization, then there's a smooth transition for those that take over, and they can pick up where you left off. A smooth transition allows folks to spend less time on digging into processes, which unlocks more time to spend on innovation and the ability to dig into untapped tools of the system to increase your donor base. Now I do wanna flag our beautiful Support Centre article called System Efficiencies: Analyzing your data. There are tips and guides on how to do everything that I have just laid out. And if you need extra guidance, you could reach out to our support team and we'll link you with one of our fantastic consultants who can work with you based on your specific organizational goals. Now I'm gonna pass it over to Raice, one of our wonderful reporting specialists who will talk about what you can do with your squeaky clean data and the data tools in the Spektrix toolbox. Now, some of this may be a refresher for some of you, but he'll also dive into tips and tricks for deciding which tool to use based on your specific needs. Take it away, Raice.
  
  - Hi everyone, I'm Raice, a reporting specialist and part of the global reporting team. As a team, we're here to support you with achieving your goals by providing expert reporting knowledge so you can make confident data-led decisions. Today I'm gonna show you some of the tools that Spektrix provides that allow you to analyze your data. We'll start by exploring customer lists and then we'll take a deeper dive into how they integrate with reports, provide even deeper insights into your customer base. Customer lists are a tool to find a group of patrons based on their demographic or purchasing behavior. This is also known as segmentation. The filters you use to find customers are called segments. For example, if you're building a customer list to find customers who you think could become regular donors, you could create a segment to find customers who have attended three or more events this year and have also donated $50 or more. By grouping customers together this way, you can quickly understand who they are and what they have in common, making it easier to engage with them effectively. You'll want to use a customer list anytime you need to identify or count customers who share a specific trade or action. Once you've got your cohort, you have a few options on what you can do next. As Sara has already mentioned, you can output a count of customers, which is a simple figure of how many customers fit your segments. You can pick specific customer outputs to appear in a CSV file, which will give you an unformatted list of customer details. Or you can add or remove tags in bulk. For example, using the segments I mentioned earlier, you could tag all these customers as prospective donors with just a few clicks. You could then use your customer list to select this group of customers who have agreed to mailings to send a targeted mailing that encourages them to support your organization. Finally, if you want to find out even more about the specific purchasing behavior of customers in your list, you can run your customer list through any of our reports. For example, you could run a customer list through the donor giving history report to see a breakdown of their donation history and we'll take a look at this in more detail shortly. Reporting is a way of extracting data from your Spektrix system. Data can be filtered based on criteria and output into a document or spreadsheet. The key differences between reports and customer lists is that our report can provide data that isn't limited to individual customers and also includes calculations for analysis. Think of reports in Spektrix as data snapshots. There are a way to summarize information. For example, you can summarize your donations by campaign name or fund and present it in a clear structured format. The information or report outputs is based on its report type. Within the Insights and Mailings interface, pressing new report allows you to see what report types are available and a brief description of what that report type is used for. A few common report types you'll come across are. Membership reports. This report shows a row for each membership purchase and they can be filtered to show active or expired memberships. Campaign reports which are available to those of you who have the Opportunities interface. They show a row for every item related to each campaign. Items can include donations, pledges, memberships, and opportunities. We also have analysis reports that show a row for each item sold or returned, allowing you to look specifically at items such as donations. In summary, each report type in Spektrix serves a unique purpose. As a fundraiser, you'll primarily be using campaign, opportunity, membership, and analysis report types with membership reports being particularly useful if memberships are central to your strategy. Use a report anytime you want a detailed breakdown or a summary of your data. Maybe you're reviewing donations for a specific campaign using the campaign summary report, or analyzing the purchasing patterns of your customers using the booking behavior report. If you need a number-focused overview, rather than just identifying who your customers are, reports are your go-to tool. They let you quickly see trends, totals, and other metrics to help you make informed decisions. If you need to identify your customers, for example, prospective donors, a customer list will do the job. But if you also need more quantitative information like a customer, like customer donation, sorry, you can use a report to find this information out for you. You can either run a report directly from the Insights and Mailings interface. Or by combining customer lists with reports you have access to a powerful tool which will help you further understand your patrons and their behaviors. Let's look at an example. We have used our customer list from earlier to find people that have came to three or more events this year and have also donated $50 or more. We want to know how much these customers have donated in their lifetime. So we'll run the customer list through the donor giving history report. This will identify both the customers from your customer list and the donations they've made. The donor giving history is just one example. There are lots of different reports in Spektrix each designed to output specific data from your database. Something we like to encourage all of our clients to do is experimenting with and building unformatted reports. When we look at reports, we have two ways to run them. We have formatted and unformatted. Let's start by looking at unformatted. What are they? An unformatted report is raw data exported from Spektrix that has no styling or complex calculations. It can however be groups and perform some calculations such as totaling donations. Unformatted reports can be opened in Excel to further manipulate the data. So why would you run unformatted reports? You would run unformatted reports to get quick answers to questions you may have about your data. For example, what was the average donation by sales channel last year? Another reason is to dig deeper into the data that makes up the information you see in your PDF or Excel report. Unformatted reports give you the flexibility to manipulate data on your own terms. We often receive report requests where the data can be quickly accessed by building an unformatted report. We want to give you the tools along with the confidence to be able to dive into unformatted data whenever you may need that quick insight. We also have a spotlight session called Introduction to Building Customer Reports on the Support Centre, which is a great tool for building reports. Formatted reports are reports that can be pulled as a PDF or formatted Excel file. All of our standard reports are formatted and use more complex calculations to organize your data as opposed to manually manipulating with an unformatted report. Think of formatted reports as a neat, ready-to-distribute summary of your data. Formatted reports are great for quick snapshots and sharing with others. They have a fixed layout and include additional calculations with the option to add visuals like charts. An important note on formatted reports is they can still be run as unformatted. So if you see something unexpected within a formatted report, you can run the same report using the same criteria set as unformatted to see where that data is coming from. Unformatted reports give you the raw data without styling. They're perfect if you want to do your own calculations, including creating pivot tables or merging datasets. These report formats both share the exact same data crossover. Even if you're not yet sure which report fits your need, Spektrix has resources to help. One key resource is the guide to standard reports. This is in our Support Centre. It explains each standard report in your system. It shows what it looks like and suggests how you can run it. Think of it like a library. Use Control + F to quickly find a report that might already do what you need. And if you find a standard report that's almost right, you can try a new criteria set or export it as an unformatted file to see the raw data. If you still can't find exactly what you need, you can submit a report request. Make sure to include as much information as possible about your goals and how you'll use the report. This helps our team find the best solution, whether it's an existing report, an unformatted report, a customer list, or a brand new report altogether. Now that you know the available tools, Jake will walk you through how to pull the data and what questions to ask to ensure you are getting exactly what you need.
  
  - Great, thanks so much Raice. So equipped with your toolbox that Raice just guided us through, you'll have a lot of ways of pulling data from your system at your disposal. But in practice, how can we better understand what we need to ask to pull the data that we actually want to see? When you pull a customer list or a report, you're asking your database to present certain information you've entered in a certain arrangement. Recapping some of what Sara and Raice have already explored today, the whole point of entering the data we do into the system is to pull it out when we need it. And Spektrix provides several kinds of tools to accomplish different tasks with that data. However, understanding which tool to use to pull your data and how to actually use it requires us to step back and consider what are we asking for in the first place. This is why it can be so useful to go through the process of crafting the question that you will ask your database to answer before you pull data from it. Now you may have only thought about the concept of crafting an ask when cultivating a donor in the people-facing part of your fundraising work. So I know it may sound strange to do the same for a database. But consider how much more comfortable you are in making asks of donors when you have researched what kind of person they are and created a pitch about how they can meet the needs of your organization. It's much easier to ask for what you want when you go in prepared and understand who that person is. The same principles apply to having a crafted ask of your data. Of course, compared to asking a person for a donation, your database is much more likely to give you something every time you ask something of it. But without understanding how to ask it the right question to solve your problem, the information it gives you won't necessarily be all that helpful. I found that in most cases when fundraisers want information from the system, there are three core components to that ask that are necessary to have fleshed out before you actually pull any reports. Who do we want to know about? What do we want to know about them? And how are we going to use that information? These three questions can only reasonably be answered by a human being. And the reporting tools at your disposal rely on you knowing the answers to these in order to do its job effectively. An apt example of this comes from my time working in development where I was often the person people would go to to pull reports and lists from our database. I remember one of the first reports my boss asked me to pull when I started was a report on our $1,000 plus level donors. After logging into our database, I immediately got stuck. There was no report that was specifically set up to look at donors who gave $1,000 or more. And any report I did find would prompt a host of further questions about date range or what kinds of donations we were looking for. So using the framework of who, what, and how, I was able to clarify what question my boss really wanted to know the answer to. So let's start with the who of this question. We know that these would be donors who have given $1,000 or more already, but when did they give to us? Is this amount about single donations they've given or the cumulative amount they've given over that time period? And are these donors who have given to any funder campaign we have or is this specific to certain ones? Are we including donations to the gala for example? Next, what do we want to know about these donors? Do we need to see how much they gave or do we just need to know that they gave at least $1,000? Do we need their emails or contact information? Or do we need to see their giving totals each year or cumulatively for this year, or even the amounts and dates of each individual gift they've given over that timeframe? And finally, how are we planning on using this report for $1,000 plus donors? Is it renewal season and we're planning on sending them an email asking to give to us again? Or are we giving all of our donors of a certain level priority booking access? Or are we trying to pull names for a program listing for our upcoming show's playbill? The answers to each of these questions and more completely changes how we might tell a report to pull data for us. So using this questioning framework, I was able to go back to my boss and gather enough information to understand what I was really going into Spektrix to look for. In a couple of weeks was the opening night reception for the first show of our season. And what he was looking to do was to send personalized invites for the event to our major donors, which we would generally classify as $1,000 or more in a year. He was also newer to the role, so he needed their names and email addresses to introduce himself, and if they did come to this event, get to know them as key stakeholders in the organization that he would be stewarding. This was also mostly a separate group of donors from our gala donors because we had many large donors to the gala who didn't regularly give on account of our programming, but instead because of their connections to our board. This context helped me craft this ask to pull this data from the system. What are the names and emails of our donors who have cumulatively given us $1,000 in the last year or more, excluding their donations to the gala fund? This kind of question is something that your database will understand. Because this who, what and how structure is more than just an abstract practice to make your questions more specific. When you pull data from Spektrix, these three fundamental components of your reporting question will translate into how you pull data mechanically from the system. If we recall the tools that Raice laid out for us earlier, crafting this ask actually makes selecting the appropriate tool much easier on top of making our use of that tool more precise. The question you ask of your data will tell you whether you need a list of names and contact info like a customer list would give you, or if you need additional information like someone's donation history that you would need a report for. And if so, whether you need to see the raw data or the analytics averages and calculations that a formatted report would give you over an unformatted one. In this case, since the information my boss actually needed to see was about these donors was the names and email addresses, I used a customer list to pull this data about these donors. This is a great example where we thought we needed a report going in, but the tool that best met our needs was something different. And knowing which of these tools we needed was a lot easier to figure out because of how specific our question was. And on top of all of that, the exercise of crafting a specific and thoughtful question that you'd like data to help answer can even be crucial to the interpretation of that data. This is a core topic discussed by groups like the Data Literacy Project, a movement that emphasizes the importance of data literacy among all kinds of organizations. Kevin Hanegan, the chair of the Data Literacy Project's advisory board puts it this way, "Asking open-ended questions is a great way to make hidden assumptions visible." Or in other words, if you don't ask a specific enough question of your data, its answer won't be very specific either. And that leaves a lot of room to paint what you see with biases and misconceptions about the story your data is telling you. Miriam will now help us dive in deeper to this very crucial piece of the puzzle, how to understand your data once you've pulled it.
  
  - Thank you so much Jake. So I'm now gonna build on what Raice, Sara, and Jake have already discussed to expand on the importance of challenging the data that you see in your reports. And how you can ensure that the stories you tell from your data are accurate. And Jake's approach to asking the right question is an exercise that can be considered at every stage of your reporting process. So data-driven decision making is everywhere across all industries. It allows us to focus on facts based on evidence, improve our customer and donor experience by knowing exactly how they behave, and also avoid risk in making decisions based on intuition rather than fact. But approaching the data that we have around us with a critical lens is crucial to using the data in the most effective way. Now what's important to remember is that data such as transactional information that's been aggregated and formatted into a specific report can't tell a story by itself. Humans are the ones that assign meaning to data and craft the trends and stories surrounding it. But where humans are concerned, assumptions based on our own experiences and culture can affect the story we tell about the data we see. And assumptions, when not properly addressed, can lead to inaccurate conclusions and perhaps even wrong decisions. And this is so important for fundraisers because our data-driven decisions can impact our relationships with our donors and our prospects. And so getting it right is crucial. Everything that we've discussed today form part of the process of challenging our data, from understanding how the data is processed to what tools we use to pull it, to what questions we ask of it. You may have come across this model before. This is the ladder of inference, which was developed by organizational psychologist Chris Argyris in the 1970s. And this model describes how humans interact with reality. It's applicable to many different situations, including things like people management and interpersonal relationships. But I also think it's a great model to assess how we can and should challenge the data we are presented with through reporting. Now in its most basic form, the ladder of inference describes the steps that we as people go through when we are trying to make sense of situations, and it's an observation about how humans typically make decisions. Now pairing it right back for the purposes of this webinar, humans tend to take specific points from observable data and move from observation to assumption to conclusion, and finally, action. The rungs on the ladder from observation to action are informed by our own knowledge, experience, and bias. Now this ladder can be a helpful model to understand how decisions are made, but it can also model how important it is to not jump too fast up the ladder and potentially make bad decisions due to any preconceived biases or incorrect assumptions. So let's look at some examples of this. So I've got a really simple example for you from my own experience working in fundraising administration. So in a previous position, I had a colleague in another department come through to me to show me a pie chart from a post-show customer survey that was embedded in an email. And this pie chart showed that 100% of customers had answered no to the question were you asked for a donation when booking your tickets? His action based on that observation was to come through and tell me you should look into that and change it. Now what happened here was there is an initial observation of the pie chart on the left taking the aggregation of that data at face value and zooming right through the meaning, assumptions, and conclusions to go straight to action, which was telling me to add an ask on the way to checkout. In this case, we then together looked further into the raw or unformatted data and found that those 100% no responses came from three people. It wasn't a required question to answer in the survey and the majority of respondents did not answer. So what we did was add additional data points to the initial observation to understand why that pie chart said the thing that it did. Challenging the data like this isn't saying that the data we're seeing is wrong, but instead it's crafting meaning behind what's being shown by understanding all pieces of the picture. And this is a key exercise in having ownership and confidence in our own data insights. Now this is a a really simple example, so let's look at some more detailed ones. So let's take a look at how we can challenge our data in the form of our standard Spektrix reports using what we've learned up to this point. The first report I'll present is our donations analysis report, which provides an analysis by sales channel of the proportion of transactions containing donations. This is a really common report to be added to a report schedule. So showing transactions processed in the last day, week, or month, for example. So in my example, let's say I've received this report into my inbox on a Monday morning, four transactions that were made last week. And one Monday morning, what I can see in this report is that, my average donation was over 2,000 pounds, my percentage donation conversion for phone transactions was 71%, and Jane Smith was the number one cashier for donations. Now, what would happen if we jumped to conclusions by jumping up that ladder from observation straight to action? What I might do is assume that the average donation amount is how much people generally give to us. So maybe I increase the average donation amount online to 2,000 pounds. I might assume that our box office teams who process orders of the phone have 71% of transactions containing donations. So maybe I celebrate with the box office team for their amazing conversion rate. And finally, I might assume that Jane is just the best fundraiser ever and give her a bottle of champagne for being the top cashier for donations. Of course, those actions would be a little drastic. So let's continue the exercise of crafting the ask by determining the who, what, and how. Starting with the who, who or what customers is this report showing transactions from? By paying attention to the criteria set here, I can see that we are telling the report to look at all order items with an accounting date of last week. So we know that it's showing all transactions and the proportion of those that contain donations, meaning we are not just seeing donations that individuals make such as ticket donations or regular giving. Many of you will be processing large donations through the system in the form of grant payments or corporate sponsorships, and those will always be returned in this report with a criteria set like this because they're also donations. So if we remember that our average donation amount was over 2,000 pounds and we had an assumption that that meant that was the average that people give to us, this assumption has been successfully challenged. So let's go to the what. What are the outputs of this report and what can they tell me about the story that my data is telling? Again, remember, this report shows all transactions. And this particular report breaks down those transactions into different funds for the donations as well as the different cashiers. Firstly, one of those funds is our membership fund, meaning last week we had a number of memberships processed into the system. I know that our memberships contain a donation, so I add that piece of data to my challenging exercise. Then I can also see Jane Smith is our top cashier. And what other data might I know about Jane? Well, I know I work with her, she's our membership administrator. So by observing what the report is showing me in terms of the output, I can determine meaning without jumping to conclusions. Of course, Jane is the top fundraiser for last week because we had a number of donations into our membership fund and Jane is the one that processes a lot of renewals. When she does that, she uses the phone channel, meaning there's a high conversion rate. Finally, how are we using this data? So as mentioned, this report is one that typically gets sent on a schedule on a regular basis. And it's a great report for an overall picture of all donations through the system. The report is doing the job that it was designed to do. And for our particular purpose, which is a general overview of the donation processing, it's doing a great job, but I'm still challenging it to avoid assumptions. Where our how comes into play here is if I was actually wanting to use this report for a different purpose such as actually I only want to know about online transactions. We might actually consider what we're asking of the report and if this is the right report for us. The exercise of crafting our ask here has been done when the report comes to us. So it allows us to view it within its entire context and avoid assumptions. So we use our exercise of crafting an ask at all stages of our reporting to help us slow down and not jump right from observation to action. So if we go back to Jake's example and we take it one step further, this is the example about being asked to find donors who have given us $1,000 or more in the last year. In this example, the purpose of finding this data was to email individually donors to invite them to a cultivation event. Now I have something else I need to find out. Let's say that I am the one that's going to be having individual conversations with the people that come to this event because I have a new initiative in mind I'm looking for sponsors of. And in order for those conversations to be informed and meaningful, I'd like more information about the quantitative donation history of those donors. So let's reframe the ask that we're making of our data. Our who is exactly the same. I'm still wanting to look at these particular customers. Our what is I want to see details of these customers' donation history. And our how is it's going to be used to inform one-to-one conversations specifically about understanding people's interest in supporting a new initiative. Seeing as our who is the same, I'm gonna use the same tool that Raice explained earlier, running a customer list through a report. And the report I've decided to choose is also the standard donor giving history report, because my what is lifetime donation history and my how is having informed conversations. Now I've got this lovely report that shows me the lifetime donation history of people who've donated more than $1,000 in the last year. And this gives me great quantitative data about their behavior. But is this everything I need in order to have those conversations? If I ran straight from observation right to action based on this quantitative data, what would happen? Well, I might approach Jane who's also the membership administrator apparently. But my intention for using this data is to inform one-on-one conversations. It's my responsibility to investigate these data points individually and these people individually because of what I'm using it for. And that means including additional things like qualitative data such as, ah, a pink note on Jane's record that says she only supported our previous capital project and she's not interested in further asks. Now, does this mean that that report is wrong? No, of course it's not. It's doing exactly what it's supposed to do. But my job when I'm taking action based on information is to craft meaning around the data I'm seeing and that includes searching for additional context. So here are some ideas for you to consider when you are challenging your data to avoid bias. Have you chosen the right data to look at, and have you completed your ask? Is there any missing data or have you considered all sources of data in order to make your decisions? What other meanings could there be? Even if the data is showing a particular trend, there may well be another option. And so seek out other perspectives. Data isn't just about the numbers that we're seeing on a screen. It could also be the knowledge of a colleague. And so sharing your thoughts with another person can help to seek out different perspectives. And of course, you can speak to us about this. So the Spektrix support team is on hand to help you with your reporting and your data visualization. So if something doesn't seem quite right, we can help you unpick and understand.
  
  - Great, thank you so much Miriam. So to highlight the big takeaways we want you all to leave this session today with. First and foremost, reliable reporting starts from the moment you enter data into the system because you can only get out of your system what you put into your system. Knowing what's in your Spektrix toolbox can help you identify which tools can arrange your data in ways that help you tell a story and solve problems. Asking specific questions with our who, what, and how framework will help you get specific answers from your data when you pull it. And always remember to challenge any biases or assumptions you may have when interpreting the data that you pull to understand what it is and isn't saying. Now, before we jump into some time for our Q and As, I wanna highlight that the Asking for Donations workshop is now live in the US and Canada. The Asking for Donations workshop is a consultancy offering Spektrix provides to our clients to help your box office and ticketing teams ask for donations confidently. Edinburgh International Festival who presented in our last Philanthropy series, Fundraising and Culture, saw a 45% increase in one-off donors in their first year as a result of the methods of asking for donations they developed in this workshop with us. And like all of our consultancy solution, I want to remind you all that this is completely free of charge. We will send the Asking for Donations request form by email along with other resources we've mentioned today. So if you are interested in what this workshop can do for you and your team, you can get in touch with us via that form or reach out to us via the Support Centre. So we have about six or seven minutes for questions. As a reminder, these can be submitted via the Q and A tool at the bottom of your screen. And we'll do our best to answer as many as possible in this time. But if we don't get to yours, don't worry, we will reach out directly to you via an email or a support ticket if you provide your information to us. So I'd love to dive in with our first question I see here. It says there's a specific report that I need but haven't been able to get the data from the guide to standard reports. How can I request or build a new report? I'd love to pass this over to Raice on our reporting team who could talk a little bit about that process.
  
  - Hi, so if it's something you think you'll use often, a report request might be the way to go. You can use a report request form and we'll ask for a mock-up, alongside the field you need to make sure we provide exactly what you're looking for. So report request, if you can't get anything out of the unformatted that you're looking for.
  
  - Great, thanks, Raice. Next question here. Hi all, is there a way to mass update profiles with information? For example, if we get a stack of returned mail, is it possible to update all profiles at one time with the preference of do not mail? A great question here. Miriam, would you like to answer that one?
  
  - Yes, absolutely. So we are able to do imports for you of sort of data into your system and that data might be updating a contact preference like that or adding information on saying do not mail. In order for us to do that import, what we need is a formatted sheet including either a customer ID or the email address of a particular customer. And that might be the most efficient way to do it if you've got sort of a big stack coming in at one time. Raice did talk about doing sort of bulk things from customer lists, so sort of bulk adding a tag onto particular customers. But I think in this particular scenario, if you're talking about sort of your stack of returned mail, it might be more efficient to try and consolidate that into a sheet and then send it over to us so that we can bulk update those details for you. Yeah.
  
  - Great, thanks Miriam. Our next question here is, is the only way to see the data from a formatted report to run an unformatted report with the same criteria or can I opt to include the data in a formatted report too? That's a great question. Sara, do you wanna take that one?
  
  - Yeah, I'm happy to. I would say that's likely the ideal way to view that data. However, like depending on the report, we may be able to add in a data table that features all of the unformatted data. However, I would say it's probably better to keep it on its own separate document so that you can easily sort and filter and manipulate the data kind of in the same way that Raice was chatting about. But if there's something that you wanna chat about, reach out to us on the support team and we can kind of talk you through if that's an ideal scenario for you.
  
  - Great, thanks Sara. Our next question I see here is how do we easily see how much someone has donated this fiscal year? That's a great question and I actually can take that one. So there are a couple like shorthand ways to easily see that information. There is a very nice tool in the Opportunities interface if you have that turned on, called the donor profile report. And this is an auto-generated report that lives sort of separately than the other reports we've talked about today on the customer record in the Opportunities interface. And what that can show you is all of the interactions that a certain donor has had with your organization over a specified time period. So this includes how much they've donated. This will also include really useful information like opportunities you might have with them, activities or touch points they've had with you, memberships that they have, ticket purchases. So that's one way to get a snapshot that's very detailed about one person. But there are also ways of pulling reports for specific donors just with the regular standard reports that exist in your system. And this is something where we could talk about like your campaign structure, seeing if that like is a good way of organizing fiscal year history there, or just using a criteria set in one of your donation reports to just run for one specific customer. So there's a lot of options there. And you can reach out to us about specifically what you're looking for there. Great question. Let's see. I think our last question we have time for here is, is there a LYBNT report? Miriam, do you wanna answer that one?
  
  - I can, yes. So for those who might not be familiar, a LYBNT report would be a last year but not this year report. So finding donors who donated last year but they didn't donate this year. Now everyone's gonna have different needs for a specific LYBNT report, especially dependent on where you are in this particular year. What we'd probably suggest as a starting point is starting with a customer list of people to identify those people. And in its most basic form, it would probably be two different segments. One that says they donated last year, so last calendar year for example. And another one combined with that saying they have not donated in this calendar year. Now of course you might have different needs for that. You might be working on a financial year basis or a campaign basis. But I'd say that's probably the starting point that we generally start with to explore. And then what you could do is you could then run that report through something like a donations report to get those specific donation amounts. But yes, there will be a way to do it. We tend to recommend a customer list as a starting point. But if you've got specific scenarios, you can reach out to us and we can dig into it with you.
  
  - Okay, thanks Miriam. So that is all the time we have for questions today. Thank you again so much for joining us and for contributing. We will follow up on any questions that we didn't get the chance to answer today. Please do check out the Philanthropy series webpage to register to our next event of the year taking place in October. And you can also register by scanning the QR code once it pops up here on screen. This will also be available in the email information that we send out after this. The Philanthropy series that we make is designed for you and to support your teams. As such, when you leave this webinar, you will see a Zoom webpage with a short survey. We've made this as short and sweet as possible, but it's really helpful to understand what you all need and want from these events like this. So please take a few seconds to share your thoughts when you leave as we strongly take these into account when planning these sessions. In addition to the Philanthropy series, there's just a few additional events that are coming up that I wanna share really quickly. And we hope to see many of you there. Especially with our final event announcement. I want to share a bit of breaking news. Earlier today, we launched our UK Hubs 2025 programs. So this year, hubs will be taking place in Edinburgh, London, and Manchester. You can submit your registration by following this QR code on screen. And we hope to see you there for this year's program themed, Transform Your Audience Experience. Also, for those of you in North America, in just a few short weeks, we will be announcing our hubs program for the US and Canada. So keep an eye on your inbox for more information. And once again, a reminder that we will be sending the recording of today's webinar along with a list of additional resources so you'll have a chance to review today's content and share it with any team members who are unable to join us today. Thank you all so much for attending today's session. And we look forward to seeing you all with us in our next session later this year. Bye everyone.

• Resources

  Resources from our Support Centre:

  • A Guide to Standard Reports in Spektrix
  • System Efficiencies: Analysing your data
  • How to Request Help with Reports
  • How to Build Customer Lists
  • Introduction to Funds and Donations
  • Introduction to Campaigns and Campaign Structures
  • How to Build a Custom Report
  • Asking for Donations - Consultancy Request Form

   

  Other helpful webinars to check out:

  • Spektrix Spotlight Sessions: Understanding Data Through Effective Reporting
  • Spektrix Spotlight Sessions: How to Build Your Own Reports

   

  Donorly Resources

  • Learn more about Donorly
  • Connect with Weston Ganz and Lauren Siff Siegel from Donorly on LinkedIn