Tag Archives: Tableau

Analytics for a (Good) Purpose

I imagine that anyone reading my posts can tell that I love doing analytics. I mean real, hands-on, getting your cuticles data-dirty analytics. But if I have a complaint about the analytics part of what I do, it’s that so often it’s for purposes that just aren’t gripping. There’s nothing wrong with selling more insurance, getting people to view higher-value ads, or cutting a few seconds off the time it takes to complete a process. Making commerce better is a perfectly good thing to do. Commerce matters to all of us. But if there’s nothing wrong with improving commerce, neither is it food for the soul. I’ve been re-reading Tobias Wolff’s wonderful novel “Old School”, and in it, one of the professors says something like this – “Essays? We could live without essays. The world would be a little poorer – like a world without chess – but stories…stories we can’t live without.”

That’s why I’ve always loved the rare occasions when we get to turn an analytics eye on a problem that means something more. Part of my team at EY got that chance a little more than a week back when we hosted an “Analytics Hackathon” for the Earthwatch Institute.

You can check out Earthwatch here at Earthwatch.org – it’s a very cool organization. I love everything about what they do and the way they approach it. I love the science part, which is fascinating. The nature part, which is just something I happen to enjoy – my daughters will attest that I am “crazy hiker guy”. And I love the approach, that assumes we are at our best when we do good not from ideology, which is often cold and artificial, but from passion. Even more, that worthwhile commitment comes from passion tempered by knowledge. We all realize that knowledge without passion achieves little. But passion without knowledge more often does harm than good in our complex society. Building that rare combination of passion for and knowledge of the natural world strikes me as what Earthwatch is all about, and I can’t think of a more rewarding mission.

So Earthwatch provided us six years of data on their expeditioners (folks who volunteer to take field trips to support their scientific endeavors), their donors, and the intersection of the two, and let us have at it for day. They asked three big questions: what can you tell us about donors and donor patterns, how do donors and expeditioners intersect, and are there things we should know to improve the marketing of expeditions to attract volunteers?

Earthwatch Image 1Great questions all, but a lot to ask of a five-hour day.

We pre-loaded their data into Tableau, and after a brief context-setting presentation from the Earthwatch folks, we split up into groups with each group drawing a single question. Each group produced a full-on dashboard and spent some time answering the questions.

One of the great challenges for many non-profits is the split between what you do and those who pay. In the traditional enterprise, good products and service make your customers happy and willing to pay. At Earthwatch, as with many a non-profit, their mission doesn’t directly serve their donors (those who pay). So the challenge (and the opportunity) is how to connect donors to the mission.

The mechanism for doing that at Earthwatch is the expedition. Hands on participation in an Earthwatch expedition is by far the best spur to giving they have. So one of our groups focused specifically on the relationship between expeditions and giving – and what they found was fascinating and unexpected. But it’s also fair to ask what other factors might drive giving – are there demographic, lifestage, or proclivities that can be used to direct social advertising, inform partnerships or target messaging?

Unfortunately, like many an enterprise (and not just non-profits), Earthwatch hasn’t done the greatest job building out their knowledge of their customers – in this case their donors. With only age, gender and zip code to work with (and that data obviously spotty with null values dominating each demographic category), the options for look-alike or advanced targeting are fairly minimal.

However, even with such thin gruel, there are findings to be had and analysis to be done. If you graph Earthwatch’s expeditioners by age, you get a big horseshoe-like graph. Lots of teenagers. Lots of seniors. Not much in-between. That’s no surprise and probably not changeable. Graph donors, and the left-hand side of the horse-shoe (the teenagers) go away. That’s no surprise either. You can’t squeeze much water from a rock. What is surprising is that the middle part of the graph doesn’t fill-in. Aren’t the parents of those teens natural donors? Your children’s connection to an activity ought to be a powerful motivator to giving. I think there’s potentially a missed strategic opportunity here.

There were two other points that emerged from simple graphs of donations by age and donation amount by age. Earthwatch gets lots of donations from seniors. But there’s a big spike right at sixty. And there’s a pretty significant spike in donation amount right around forty. Think about that. Forty and sixty are big inflection points. They are times when almost all of us step outside the lines for at least a short while and think about the shape and nature of our life. That’s a good time to think about an Earthwatch expedition or a donation, right? This is a case where there’s no need to target a broad demographic. The combination of some key interest variables and a big birthday might be enough. It’s at least worth testing. Targeted marketers know the importance of magic moments, and the finer-grained you can make them, the more efficient you can be. For a non-profit like Earthwatch with tiny marketing dollars, the tighter you can draw the boundaries around a magic-moment, the more likely you are to be able to use it effectively.

Thinking about that donor curve also makes plain how important both patience and a long-term strategy are to a non-profit like Earthwatch (and maybe to a lot of for-profits as well). Earthwatch has been around for a long time. That means some of their early expeditioners are retirees now. If you can keep track of people for twenty, thirty or forty years, you have an opportunity to re-ignite those connections. When they have teenagers themselves, they are the right audience to target for expeditions and donations.

This long term view seems hard. But it’s exactly what great schools and universities do. They know their 25 year old graduates aren’t giving them money. But if they can create mechanism to stay in touch till those graduates hit forty, fifty and sixty, that is worth a lot. Social media is, of course, a great way to do this. And facilitating social media connections with volunteers ought to be a long-term strategic goal for any non-profit that engages with young people.

And what about all those folks who took expeditions back in the 80’s and 90’s? Track them down on LinkedIn and Facebook – that’s what interns are for – and send them something to get them back in the fold!

In my recent posts, I’ve been arguing that analytics is under-used in strategy. Mostly, this type of analytics isn’t advanced modelling or big data stuff. It’s macroeconomics not microeconomics. Just looking at the shape of the donor and expeditioner curves can help inform strategic thinking.

From a more tactical standpoint, we also looked at the relationship between their new membership program and repeat giving. Earthwatch has bounced back and forth a bit on membership, but they currently are focused on it. We found that members tended to be smaller donors (their biggest donors weren’t always members). More interesting, however, was the impact of membership on donation pattern and stability. We tracked donors who gave in ’14 before the membership program and then became members in ’15. Did they give less or more? We didn’t have the time or the tools to do this analysis properly, but it looked as if membership, on average, tended to slightly depress average donation but increase frequency of giving resulting in a net positive. As I said, we didn’t have time to really prove this, but analytically, there’s a couple of key points here. If you’re a non-profit trying to assess the impact of something like membership, you need to make sure you break the problem down into analyzable segments. That means creating cohorts of previous donors and tracking their behavior (including whether their behavior tends to improve or deteriorate over time), tracking the impact on new donors and efforts, and, in most cases, using hold-outs and control groups to make sure you’re not fooling yourself about the numbers.

Going back to the shapes of curves, the team that looked into the relationship between giving and expeditions found something truly interesting. They linked the two tables (donors/expeditioners) to isolate just the population that had gone on an expedition and donated money. Then they created a calculated variable that tracked the difference between the donation date and the expedition date and laid it out on a chart (ain’t Tableau wonderful).

Earthwatch Image 2What they found was kind of a shock. I would have expected a curve kind of like a camel’s hump after the expeditions. Not much giving ahead of time, a short latency period after the expedition, then a sharp hump followed by a quick decline and a long slow descent as the halo from the trip gradually dispersed. Much of that is exactly what they found. There isn’t much of a latency period but the there is a sharp hump followed by the quick decline and slow descent. The shocker was on the other side of the curve. It turns out that lots of expeditioners (not the teens but the adults) are quite likely to give BEFORE they travel. The team tackling this called it a “Packing Boost” (this is one of those things that makes me proud – not only did they find something interesting but they did the extra work to attach a business useful name to the phenomenon – that’s good consulting). The pre-trip donation amounts were quite a bit smaller on average, but the number of donations was almost symmetrical.

I would never have expected that.

Apparently, when people are getting ready for an expedition they are also in the mood to make a donation. I can see that, but not only was it a surprise to me, it wasn’t received wisdom at Earthwatch either. Their donation solicitations are not at all focused on the pre-trip period.

That’s potentially a huge win and an easily testable addition to their solicitation marketing program.

The third team looked at the behavior of expeditioners. Their initial analysis focused on when people book an expedition versus the type of expedition. It turns out that there are some pretty distinct types of trip. Expeditions to Africa are usually booked a long time in advance. Expeditions in the US and places like Costa Rica are more typically booked 2-3 months in advance. There are seasonal impacts as well, with most expeditions getting booked in the spring (to take place over summer).

Actionable? You bet it is. If you’re programming the hero section of the website (which happens to have a rotating set of expeditions), knowing the time-horizons for each type of trip can help you get your web marketing right. There’s also a planning element to this. If your Africa expedition isn’t largely staffed six months out, you’re in trouble. But that trip to Costa Rica still has plenty of runway.

Finally, that team looked at the impact of discounts on cancellation behavior and which expeditions were most cancelled (important from a planning perspective). They, too, ran out of time and had some tool limitations but initial analysis seems to suggest that people are less likely to cancel trips when they’ve gotten a discount. Even more suggestive, it didn’t look like the amount of the discount was hugely significant. This might indicate that some discounting is economically beneficial – even it drives no initial lift.  It’s also possible that it’s no more than an artifact of self-selection, since the discounts may be offered to customer segments that are inherently less likely to cancel (previous expeditioners, for example). It’s an unexpected and potentially important finding but like any exploratory finding, it needs testing and controls to see if it’s real.


I’m pretty sure our five hours of time won’t change the world. Still, we had a lot of fun doing work we genuinely enjoy for an organization that truly matters. And there’s a chance we helped out a little. That’s good enough for me.

Are there some big takeaways about analytics from our one-day Hackathon? Most of them are things we all should know.

Earthwatch helped make the process more productive by coming to the table with three real and fairly concrete problems. We don’t always get as much from clients that are investing a lot of money. Knowing the questions you want to answer is the single most important step in any analysis.

Like a lot of organizations, Earthwatch hasn’t invested as much in data collection and data quality as is ideal. Limitations on the data place real boundaries on what you can do – not only with analysis but with the fruits of that analysis in targeting and personalization.

Being open to the unexpected is critical (and sometimes that’s easier for an outside consultant without a lot of preconceptions around the business). The team that started by focusing on the impact to donations after taking an expedition ended up talking much more about the impact to donations of planning for an expedition. It wasn’t that their initial hypothesis was wrong. People do donate after going on an expedition. But they had the imagination and sense to see that a more interesting hypothesis emerged from the data.

Tableau is a great tool for visualization and data exploration, but it can’t do everything. Problems like the cohort analysis of membership or the impact of cancellation really required statistical analysis tools with more horsepower and more data manipulation capabilities. Still, the ability to quickly explore a data set across many dimensions is wonderful and the utility of that ease in the right hands is hard to overestimate.

Finally, the biggest part of any analysis is the imagination to map the data to the business problem or opportunity. Strategic insights aren’t usually driven by fancy analysis. They are more often sparked by simple views and cuts of the data (line graphs or bar charts) that make obvious some fundamental fact about the business. Sometimes data can spark new insights; sometimes it’s just a confirmation (or refutation) of strategic thoughts or business intuitions that are already on the table. Either way, it makes for a better strategy and more confident decisions.


Finally, one last plug for Earthwatch. What they do is important and, often, very cool (check out that Barrier Reef diving expedition). Like our Hackathon, there’s nothing wrong and everything right with having fun doing something worthwhile. So even if you’re not coming up on forty or sixty, take a look!