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Ami On The Run: Harvesting Big Data To Fund Your Mission And Achieve It [Big Idea]
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With a belly full of lunch and some more pep in my step, I'm out to visit the afternoon sessions. For this session, I'm checking out the "Big Idea" panel on Big Data and how to harness it to fund and achieve your mission. Let's check it out!
1:33 p.m.: The panel begins with a question. "What if bandwidth or funding wasn't a problem for your organization?" More importantly, what can we leave behind for the next generation of change makers? At Blackbaud we want to make you more effective and efficient in your daily practices.
1:35: "We now support the entire social good community." Not just non-profits, but corporations, individuals, education, and foundations. Enter, the Blackbaud Institute. Blackbaud Institute will help bridge the gap between these different mediums for social good with industry insights and big data. Visit blackbaudinstitute.com
1:36: With that, let the panel begin! Steve MacLaughlin takes the stage with Andrew Means and Jen Schultz.
1:37: You usually think of data as the most abundant element in the social good sector. Everything we interact with has data. To what degree is the non-profit sector taking advantage of all of this?
1:38: Let's take a step back to April 5, 1905. Washington, D.C. On the second page of the Washington Post, a story titled "Money is the Best Talker." about a dinner held in D.C. with Charles Sumner Ward and Lyman Love Pierce. They planned to raise the last $80,000 of a $300,000 goal in 27 days. They were the first fundraisers to hire a publicist, utilized engagement tactics like countdown clocks, crowdfunding, and more. Sounds a lot like modern fundraising, doesn't it? These same men would go on to do truly innovative things in the non-profit space between 1905-1923, including crowdfunding, with NO TECH. Impressive!
1:41: What is big data? "Using predictive analytics to extract as much value form large data sets as possible."
1:42: Let's fast forward to the present. June 14, 2016. 373.25 billion dollars given to non-profits last year according to Giving USA, the highest ever recorded. But, percentage of income given to charity has been stuck at 2% for 40 years. Houston....we have a problem.
1:43: Steve talked to a large spread of organizations, old and new, large and small, to figure out what it was they did to be successful using data. He learned:
1:47: Andrew Means now takes the stage, and dives into an intro into data science. What happens when you search for "data" on Google images? Tunnels, masses of 1's and 0's. Almost 80% of it is...blue.
But what IS data? Data itself isn't valuable, it's only valuable because of what you can do with it. To turn it into something valuable you need tools and methods.
1:49: "Data is when experience, thoughts, movement, places, interactions, and more are captured and stored."
1:50: "Sometimes I go for runs along the lake front. If I don't bring my phone, fitbit, or other device--there is no data on this run. It happened, and now it is gone. But if I do bring my phone, or other device....there's a ton of data. Length, heart rate, elevation change, route. Did you know there is more computing power on your iPhone than on a space shuttle? There's a dramatic increase in computing power, and a dramatic decrease in cost of storage. Meaning we're beginning to track more and more information.
1:52: We're wearing data capture data devices on our bodies, they're posted around our cities, they're in our planes and cars. Data capture is everywhere.
1:53: Data science is an emerging field involving a combination of a few differing things: Hacking Skills, Substantive Expertise, Math and Stats Knowledge. The convergence of all of these things...is data science.
1:54: How is data science changing the non-profit sector? It's changing the kind of interventions we can offer. It changes the way we can solve our world's problems, and increases the speed at which we can solve them. Example, one organization used satellite imagery to help analyze (using machine learning) to determine what villages in Africa were the most in poverty and used that to help figure out where to distribute funds to be most effective.
1:57: High schools who use data to determine students at risk of dropping out, can actually decrease drop-out rate.
1:58: We as non-profits exist to create change. To change something in the world today to make a better tomorrow. But, how do nonprofits compete with each other? Is it over quality of the impact? No, not right now. We actually compete via stories. "But the problem with a lot of our stories is that they are not true." Andrew goes on to qualify this by showing a McDonald's ad with a beautiful Big Mac. "This is not what we actually expect when we go to McDonald's.
2:00: The problem is that donors rarely have insight into the quality of the "product." They have very little insight into who is benefiting from their giving and how much. They get these "stories" and have to trust that this is an accurate picture of their contribution which it may not be. But, with Big Data we can change these things, and give donors an honest and accurate portrayal of the difference that they are making.
2:02: Jen now takes the stage. "I'll tell you the truth. I flunked 3rd grade long division." You don't have to be frightened by the terms, words or process of collecting and analyzing big data. You can do it too. She talks about Project HOPE. Providing knowledge, tools, and support to local healthcare practitioners in underserved communities to improve community health.
2:02: Jen needs a few things to be successful with data:
2:06: Create context through benchmarking. 3 years ago we were at the bottom of the barrel compared to similar orgs. But now I can use that benchmarking data to inform board members and encourage them to invest in the right places.
2:08: Created a new lexicon based on the words: Win, Keep and Lift. WIN new donors, KEEP the donors through retention efforts, and then LIFT them up to greater giving and more engagement.
2:09: For her org, Jen created big posters featuring key metrics and placed them outside the breakroom as a start to educating and changing culture within the organization. All employees had to walk by and look at the posters on the way to get coffee so they were more likely to take a look at this imporanted data. This actually helped secure some modest investments in acquisition.
2:11: "Now that we're getting new donors we have to make sure they don't flow out the other side. We dug into the donor experience, and discovered that donors were rating their service/experience poorly due to limited hours and inability to connect to someone at the org." She responded by using this data to promote working with a third party to extend support hours and add e-mail support.
2:13: "We also analyzed other touch points with the donors. We thought our "Thank You" e-mail was fabulous! It was beautiful, had big images and calls to action....but it was not a good experience. We knew that because we implemented donor feedback surveys at various touch points throughout the donation experience. We ask for feedback everywhere, on every communication, including every e-mail. That's a lot of data. It helps us identify issues and help resolve them quickly."
2:15: "One of the biggest points of feedback was that our e-mails were too long and did not contain the amount of donation. Thanks to this feedback, we were able to shorten the e-mail, include new donation info, and since then have received 0 complaints."
2:17: We also use data to tell a story to our donors. To make the data tangible and understandable we have to tell a story.
2:18: Did it work? Yes. Using data to help build company culture, increase acquisition and donations, and improve retention resulted in improved metrics across the board. Becuase....
1:19: What get's measured get's done. Don't let big data intimidate you, use it to improve your organization, again by...
2:21: Jen answers: You have to have that internal champion first and foremost, but having an external partner to work on it with you helps too. Sometimes having that outside perspective helps. Andrew chimes in: Sometimes having that culture in place first is better than starting with tech. If you don't have the culture, the tools won't be well utilized. Steve: You have to build the "Coalition of the Willing" inside your org.
2:22: Sometimes it takes some digging to find your internal champions.
2:23: "Andrew can you talk about having this explorational mindset, and how there might be some trial and error when looking for insights in your data?" Andrew: Deputize everyone to be a scientist. We can do experiments and test at a much more rapid pace, but to do this you have to be willing to just go in and say "I'm going to go where the data will lead me." even if you don't really like where it takes you or the answer it provides.
2:25: Steve: "Jen you talked about making conscious decisions about things you wanted to own outright vs things you want to partner on. How did you approach those decisions?" Jen: "It requires partners to do all the work." A lot of times you can rely on internal people, but sometimes there isn't enough bandwidth or there isn't someone with the expertise you require, so you have to look outside. Ask "What's the question?" first, and then look for the person who can best answer it.
2:27: Steve: "Andrew, what are some key traits of people that are particularly successful with using data?" Andrew: Data science is a team sport. You're not going to find all of the aspects of a data scientist in one person all all the time. Sometimes a really excellent data scientist doesn't understand the constraints under which we operate. So, I find someone who has worked like that before. A person that understands those constraints is usually successful. Also, someone with communication skills. Being able to convey the story that the data tells is equally as important as interpreting it.
2:29: Learning the jargon is sometimes a barrier. Things like "mid-level giving" or "lapsed donors" have flexible definitions within the industry. It's important to make sure these things are clear to better determine desired outcomes and how the data should be interpreted. Andrew: Something that I hope to see in our sector soon is "data sharing" and having a more unified set of parameters to determine how we compare to each other. It's difficult with differing missions, goals, and benchmarks.
2:34: Steve: What are the kind of reactions you get internally when you use benchmarking data? "It's really easy to think you're the only organization--that if your organization is suffering others must be too. Like during a recession." But the benchmarking data helped to show that this was not the case, that there was something more impacting the program than the recession. Sometimes there's just more information that anyone can process, so picking the exact pieces of data to tell the right story are important. And sometimes you're going to need help, and bring in an outside resource to identify what's important and what data to keep tracking to discover trends and insights.
2:36: Steve: What kind of data should you keep? And what kind should you let go? Jen: "We're actually seeing that with the donor feedback we're receiving. Rather than tracking all donors, we may only look at someone who has responded to a survey and only track their behavior to hone in on trends with people who have given us the feedback we acted on."
2:38: You've got to ask yourself, "What is the value of the data we're trying to collect? What can it answer for us?" Filter out the data that is not serving a purpose for your org.
2:39: If we're collecting data from people, we need to give it back to them in a way that's valuable. Jen: " I have a story that ties into that. During the earthquake in Nepal we saw a lot of requests for the ability to donate via Paypal. We took it back to the team and were able to give them a Paypal button. I then e-mailed all of those people to let them know the button was there--no ask attached--and they all went back to give."
2:41: Steve: "It's supposed to be a feedback LOOP. If you open the loop, you need to close the loop." Even if that's only a thank you for the feedback, but try your best to act on what they give you in some way.
2:42: Steve: "What I want to close on is, what about the things we may have not had to deal with yet, thinking out a few years, that we might need to prepare for today." Andrew: "Privacy and security are two big ones. I think we as a sector need to focus on that far more. It's possible that a lot of us have been hacked and some point and may not even know it. We have to be thinking about it as: as stewards of data, entrusted with lots of personally identifiable data, that we need to be treating that with the respect it deserves."
With that, the session concludes. Thanks for reading! Stay tuned for the afternoon session post coming soon.
2:44: ____: "When I think about data in the future, I think about how donors will behave in the future, which leads to how our interactions change with our donors and how our programs need to change and how we can use that data to shape that donor experience into something better."
1:33 p.m.: The panel begins with a question. "What if bandwidth or funding wasn't a problem for your organization?" More importantly, what can we leave behind for the next generation of change makers? At Blackbaud we want to make you more effective and efficient in your daily practices.
1:35: "We now support the entire social good community." Not just non-profits, but corporations, individuals, education, and foundations. Enter, the Blackbaud Institute. Blackbaud Institute will help bridge the gap between these different mediums for social good with industry insights and big data. Visit blackbaudinstitute.com
1:36: With that, let the panel begin! Steve MacLaughlin takes the stage with Andrew Means and Jen Schultz.
1:37: You usually think of data as the most abundant element in the social good sector. Everything we interact with has data. To what degree is the non-profit sector taking advantage of all of this?
1:38: Let's take a step back to April 5, 1905. Washington, D.C. On the second page of the Washington Post, a story titled "Money is the Best Talker." about a dinner held in D.C. with Charles Sumner Ward and Lyman Love Pierce. They planned to raise the last $80,000 of a $300,000 goal in 27 days. They were the first fundraisers to hire a publicist, utilized engagement tactics like countdown clocks, crowdfunding, and more. Sounds a lot like modern fundraising, doesn't it? These same men would go on to do truly innovative things in the non-profit space between 1905-1923, including crowdfunding, with NO TECH. Impressive!
1:41: What is big data? "Using predictive analytics to extract as much value form large data sets as possible."
1:42: Let's fast forward to the present. June 14, 2016. 373.25 billion dollars given to non-profits last year according to Giving USA, the highest ever recorded. But, percentage of income given to charity has been stuck at 2% for 40 years. Houston....we have a problem.
1:43: Steve talked to a large spread of organizations, old and new, large and small, to figure out what it was they did to be successful using data. He learned:
- Data health was essential. If you start with bad data, it only gets worse form there. It's worth investing in.
- Data needs champions. In successful orgs, there were champions at all levels of the organization, not just one data person who realizes it's important.
- Data is not a foreign object. It's an integral part of an organization's daily operations.
- Data requires the right culture. Your culture has to evolve to place importance and value on the data. "It's not about the volume of the data. It's about the VALUE of the data."
1:47: Andrew Means now takes the stage, and dives into an intro into data science. What happens when you search for "data" on Google images? Tunnels, masses of 1's and 0's. Almost 80% of it is...blue.
But what IS data? Data itself isn't valuable, it's only valuable because of what you can do with it. To turn it into something valuable you need tools and methods.
1:49: "Data is when experience, thoughts, movement, places, interactions, and more are captured and stored."
1:50: "Sometimes I go for runs along the lake front. If I don't bring my phone, fitbit, or other device--there is no data on this run. It happened, and now it is gone. But if I do bring my phone, or other device....there's a ton of data. Length, heart rate, elevation change, route. Did you know there is more computing power on your iPhone than on a space shuttle? There's a dramatic increase in computing power, and a dramatic decrease in cost of storage. Meaning we're beginning to track more and more information.
1:52: We're wearing data capture data devices on our bodies, they're posted around our cities, they're in our planes and cars. Data capture is everywhere.
1:53: Data science is an emerging field involving a combination of a few differing things: Hacking Skills, Substantive Expertise, Math and Stats Knowledge. The convergence of all of these things...is data science.
1:54: How is data science changing the non-profit sector? It's changing the kind of interventions we can offer. It changes the way we can solve our world's problems, and increases the speed at which we can solve them. Example, one organization used satellite imagery to help analyze (using machine learning) to determine what villages in Africa were the most in poverty and used that to help figure out where to distribute funds to be most effective.
1:57: High schools who use data to determine students at risk of dropping out, can actually decrease drop-out rate.
1:58: We as non-profits exist to create change. To change something in the world today to make a better tomorrow. But, how do nonprofits compete with each other? Is it over quality of the impact? No, not right now. We actually compete via stories. "But the problem with a lot of our stories is that they are not true." Andrew goes on to qualify this by showing a McDonald's ad with a beautiful Big Mac. "This is not what we actually expect when we go to McDonald's.
2:00: The problem is that donors rarely have insight into the quality of the "product." They have very little insight into who is benefiting from their giving and how much. They get these "stories" and have to trust that this is an accurate picture of their contribution which it may not be. But, with Big Data we can change these things, and give donors an honest and accurate portrayal of the difference that they are making.
2:02: Jen now takes the stage. "I'll tell you the truth. I flunked 3rd grade long division." You don't have to be frightened by the terms, words or process of collecting and analyzing big data. You can do it too. She talks about Project HOPE. Providing knowledge, tools, and support to local healthcare practitioners in underserved communities to improve community health.
2:02: Jen needs a few things to be successful with data:
- Measure and Monitor
- Context
- Donor Experience
- Mission Outcomes and Impact (to tell our story)
2:06: Create context through benchmarking. 3 years ago we were at the bottom of the barrel compared to similar orgs. But now I can use that benchmarking data to inform board members and encourage them to invest in the right places.
2:08: Created a new lexicon based on the words: Win, Keep and Lift. WIN new donors, KEEP the donors through retention efforts, and then LIFT them up to greater giving and more engagement.
2:09: For her org, Jen created big posters featuring key metrics and placed them outside the breakroom as a start to educating and changing culture within the organization. All employees had to walk by and look at the posters on the way to get coffee so they were more likely to take a look at this imporanted data. This actually helped secure some modest investments in acquisition.
2:11: "Now that we're getting new donors we have to make sure they don't flow out the other side. We dug into the donor experience, and discovered that donors were rating their service/experience poorly due to limited hours and inability to connect to someone at the org." She responded by using this data to promote working with a third party to extend support hours and add e-mail support.
2:13: "We also analyzed other touch points with the donors. We thought our "Thank You" e-mail was fabulous! It was beautiful, had big images and calls to action....but it was not a good experience. We knew that because we implemented donor feedback surveys at various touch points throughout the donation experience. We ask for feedback everywhere, on every communication, including every e-mail. That's a lot of data. It helps us identify issues and help resolve them quickly."
2:15: "One of the biggest points of feedback was that our e-mails were too long and did not contain the amount of donation. Thanks to this feedback, we were able to shorten the e-mail, include new donation info, and since then have received 0 complaints."
2:17: We also use data to tell a story to our donors. To make the data tangible and understandable we have to tell a story.
2:18: Did it work? Yes. Using data to help build company culture, increase acquisition and donations, and improve retention resulted in improved metrics across the board. Becuase....
1:19: What get's measured get's done. Don't let big data intimidate you, use it to improve your organization, again by...
- Measure and Monitor
- Context
- Donor Experience
- Mission Outcomes and Impact (to tell our story)
2:21: Jen answers: You have to have that internal champion first and foremost, but having an external partner to work on it with you helps too. Sometimes having that outside perspective helps. Andrew chimes in: Sometimes having that culture in place first is better than starting with tech. If you don't have the culture, the tools won't be well utilized. Steve: You have to build the "Coalition of the Willing" inside your org.
2:22: Sometimes it takes some digging to find your internal champions.
2:23: "Andrew can you talk about having this explorational mindset, and how there might be some trial and error when looking for insights in your data?" Andrew: Deputize everyone to be a scientist. We can do experiments and test at a much more rapid pace, but to do this you have to be willing to just go in and say "I'm going to go where the data will lead me." even if you don't really like where it takes you or the answer it provides.
2:25: Steve: "Jen you talked about making conscious decisions about things you wanted to own outright vs things you want to partner on. How did you approach those decisions?" Jen: "It requires partners to do all the work." A lot of times you can rely on internal people, but sometimes there isn't enough bandwidth or there isn't someone with the expertise you require, so you have to look outside. Ask "What's the question?" first, and then look for the person who can best answer it.
2:27: Steve: "Andrew, what are some key traits of people that are particularly successful with using data?" Andrew: Data science is a team sport. You're not going to find all of the aspects of a data scientist in one person all all the time. Sometimes a really excellent data scientist doesn't understand the constraints under which we operate. So, I find someone who has worked like that before. A person that understands those constraints is usually successful. Also, someone with communication skills. Being able to convey the story that the data tells is equally as important as interpreting it.
2:29: Learning the jargon is sometimes a barrier. Things like "mid-level giving" or "lapsed donors" have flexible definitions within the industry. It's important to make sure these things are clear to better determine desired outcomes and how the data should be interpreted. Andrew: Something that I hope to see in our sector soon is "data sharing" and having a more unified set of parameters to determine how we compare to each other. It's difficult with differing missions, goals, and benchmarks.
2:34: Steve: What are the kind of reactions you get internally when you use benchmarking data? "It's really easy to think you're the only organization--that if your organization is suffering others must be too. Like during a recession." But the benchmarking data helped to show that this was not the case, that there was something more impacting the program than the recession. Sometimes there's just more information that anyone can process, so picking the exact pieces of data to tell the right story are important. And sometimes you're going to need help, and bring in an outside resource to identify what's important and what data to keep tracking to discover trends and insights.
2:36: Steve: What kind of data should you keep? And what kind should you let go? Jen: "We're actually seeing that with the donor feedback we're receiving. Rather than tracking all donors, we may only look at someone who has responded to a survey and only track their behavior to hone in on trends with people who have given us the feedback we acted on."
2:38: You've got to ask yourself, "What is the value of the data we're trying to collect? What can it answer for us?" Filter out the data that is not serving a purpose for your org.
2:39: If we're collecting data from people, we need to give it back to them in a way that's valuable. Jen: " I have a story that ties into that. During the earthquake in Nepal we saw a lot of requests for the ability to donate via Paypal. We took it back to the team and were able to give them a Paypal button. I then e-mailed all of those people to let them know the button was there--no ask attached--and they all went back to give."
2:41: Steve: "It's supposed to be a feedback LOOP. If you open the loop, you need to close the loop." Even if that's only a thank you for the feedback, but try your best to act on what they give you in some way.
2:42: Steve: "What I want to close on is, what about the things we may have not had to deal with yet, thinking out a few years, that we might need to prepare for today." Andrew: "Privacy and security are two big ones. I think we as a sector need to focus on that far more. It's possible that a lot of us have been hacked and some point and may not even know it. We have to be thinking about it as: as stewards of data, entrusted with lots of personally identifiable data, that we need to be treating that with the respect it deserves."
With that, the session concludes. Thanks for reading! Stay tuned for the afternoon session post coming soon.
2:44: ____: "When I think about data in the future, I think about how donors will behave in the future, which leads to how our interactions change with our donors and how our programs need to change and how we can use that data to shape that donor experience into something better."
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