Over the Counter Data

How do you make your data easy to understand?

OTC Data Standards, written by Jenny Grant Rankin. These are best practices. Free on her website: https://overthecounterdata.com/otcd/

  1. Title your graph
  2. Footer/annotation, they are useful. Make sure they’re not overwhelming, but it helps with understanding the data. Running commentary on what to look at at the data.
  3. Supplemental Documentation. Include a reference sheet. Description of the data display, explains its purpose (what are some questions the report will answer), focus (who is the intended audience), warning (what do many of these people misunderstand?). Walk people through how to use the data display.
  4. Help Lessons/Help System – 1)understanding the data, 2)technical help. Written lessons halve the training time needed.

This is all a lot of extra work, but Rankin’s research shows that it improves the accurate reading of the data by 10-50% across the board.


Gathering Data on What Your Audience Really Wants

Another KYOB post filled with goodies: https://www.colleendilen.com/2017/06/21/audience-insights-organizations-overlook-important-clues/

Main takeaways:

  • don’t dismiss a negative Yelp review just because “it’s just one guy”. Beware the squeaky wheel, but don’t discount it either. Use it as a clue to dig in deeper to potential areas for improvement
  • Always be looking for things that you don’t understand or don’t know about your audience — and gather that data. “If you don’t have your eyes peeled for things that you don’t understand (or, worse, if you are relying on data or audience feedback solely to affirm past decisions), then you may be collecting data for data’s sake. What’s the point of that?”
  • Go back and look at your online reviews: Yelp, Facebook, TripAdvisor, Google, CharityNavigator, etc. There may be gems in there you’re currently ignoring

Bullet Graphs

Love this idea for a clean comparison over time chart — the grey is last year’s water consumption, the black is this year’s.


Check out this blog post from storytelling with data for more information: http://www.storytellingwithdata.com/blog/2017/5/25/the-bullet-graph.

Segmentation Guide

Another resource on segmentation I found enlightening:

Why segment?

If you want to persuade someone to do something (like attending an event), the more you know about them, the better your chances of success. You can make sure, for example, that you tell them about something that interests them, or that you use their favoured means of communication, or even that you don’t put them off by telling them about something that they’re definitely not interested in.

Segmentation helps make sense of these variations so that you can devise strategies to engage particular audiences based on the appropriate behaviours and characteristics which they share. It is in effect a recipe for reaching wider and different audiences, more often and more cost effectively.


I think I knew a lot of this intuitively, but it’s very helpful for me to read specific examples and have the reasons we segment — what to be done with those segments! — clearly outlined.

You should consider the type of cultural offer and promotional messaging that is most appropriate, based on the behaviours of what a particular segment is likely to respond to. For example, do they need to be won over with discounted offers to more traditional events, or are they more likely to be influenced with exclusive premium priced packages to contemporary cutting edge events? Communications can then be planned based on knowledge of where they live, e.g. door to door leaflet distribution, local newspaper advertising, buy-in to customised mailing lists, or even to help you decide on the tone and image of the messaging.

And I like this little gem at the end:

You may need to use bits of your data at different times to inform your segmentation strategy to help achieve specific audience development objectives. This will also require constant topping up of the information you have about audiences in order to build upon that knowledge over time.

This is particularly relevant to me, since I just had a conversation with the agency that conducted our market research and created our segments for us. I pointed out that there were some surprises when we started applying the algorithm to our audience — deeply engaged members + donors who were placed in the “Not a Target” segment. Their response was that there might be some tweaking yet to do with the algorithm, since they were working off of survey data, and we have a wealth of other information and data in our system in regards to ticket-buying, membership, and donation behavior over time. Part of my job in the coming months will be to be on the lookout for trends and outliers in our segmented pool of audience members — are there other data points we should add to the algorithm? There never seems to be one “set it and forget it” method for doing anything around here… 🙂

Survey Best Practices


Image from http://www.mymarketresearchmethods.com/survey-design-best-practices/

Love this series of questions and considerations for survey-building. Among my favorites:

  • “After I write a question, I like to imagine that I instantly have the raw data in my hands, then I ask myself: “Ok, now what?  What can I do with this information?  Does this give me the information I need?”  This simple exercise will help you to construct questions that really matter and will yield actionable data; you will avoid filling your survey with a bunch of “nice to know,” but non-essential questions.”
  • “Nothing is more frustrating than a complex survey question.  Questions can be complex by being too long, having too many choices, by using confusing language, by forcing respondents to rank too many items, do arithmetic, or by trying to measure too much in one question.”
  • “use objective metrics like “once a week,” “more than once a week,” etc.”
  • If you are asking multiple choice questions like the one below, it is a good idea to randomize the choice options so that the same option isn’t always listed first.
  • “I recommend putting your demographic questions (age, gender, etc.) at the end of the survey.  Just like you ease a respondent into the questionnaire with easy questions, you want to ease them out as well.”

Data Democratization

This is one of the most beautiful projects I’ve ever seen:


I’ve been thinking a lot about this today, as I started the day off with a meeting with a department that runs events that have historically not kept accurate or easy-to-find attendance numbers. Our chat encompassed the variety of data sources available, employees not being given a clear “Why collect this data” reason, and a question of whether to store this data in 2 different systems (the current practice) or keep it in one. The meeting came about because my Marketing department has long had misunderstandings about why these issues existed. I wanted to hear the story and landscape from their perspective. It was a great conversation.

I don’t think we’ll ever completely get away from siloes, multiple data sources, and the need for institutional knowledge to fully understand how and where to find data points. So building out a data infrastructure like this sounds like the most intuitive, democratic, and thorough way to get everyone on the same page re: our organization’s data.

But how to build out a custom infrastructure so complex and user friendly at a nonprofit arts org…..

View story at Medium.com

View story at Medium.com

Research on Arts Market Segmenting

I’m doing some ground work on our audience segments this month — we had a research firm do market research for us, and identified 2 key segments of the Bay Area general population that resonate most closely with our mission, vision, and overall message.

Now that we have these segments, and a way to sort our constituents into them (a survey they fill out, an algorithm generates the result, the result is an Attribute in our CRM….), there’s no real plan to move forward with this tool!

So I’m putting a meeting of the minds together to identify the following:

  • what is the behavior of people who are in the audience segments in our system now? what tickets have they bought? have they ever been a member? contributed to our annual fund?
  • how do we use this information — the messages that appeal to them (segment) + the types of performances and messages that they respond to (ticket and contribution history) to plan marketing efforts:
    • segmented emails
    • targeted marketing with Google Adwords and Facebook
    • mailings (part of the audience segment includes demographics, which includes location….)
    • key partnerships with organizations that have employees and constituents likely to fall into the segments
    • etc.
  • comprehensive plan to keep surveying and identifying constituents so we can sort them and further refine our behavioral model

I found a great resource from the National Arts Marketing Project to help guide my thinking, since I’m new to the Marketing world at large and the Segmentation world more specifically.


This resource is actually incredible and I encourage a deep-dive. Here are my takeaways:

  • Marketing segments attract the right people with the right message through the right media at the right time with the right product at the right price. It creates highly satisfied audiences that keep coming back for more
  • Use segments to make the most of your marketing resources — figure out how each segment responds to which messaging/channel/programming
  • demographics are only a starting point. they describe the “what” makes up a person, not the “how” (critical thinker) “why” (compassionate, optimistic) of a person. for this, you need to add psychographics to your segmentation definition
  • pinpoint the psychographics by identifying your audience’s common needs

And since I’m working with the privilege of having the segments identified for me (based on market research we had conducted on our behalf 2 years ago), I’m particularly interested/grateful for the fact that the article recommends the following next steps, once you’ve defined what characteristics make up your segments:

  • find ways to assign your people to each segment (we do so by asking survey questions and sending the answers through an algorithm given to us by the market research group)
    • but we still need to find the best way to continually collect answers to these questions, and to streamline that algorithm work
    • i’m also interested in analyzing the behavior of the segments we have to see if we can :further: segment/predict behavior based on past behavior
  • Do people in this segment share any particular personality traits? Are they more or less likely than others to be happy? Optimistic? Introspective? Self-confident?
  • How might their personality traits affect your marketing message, or the way you try to reach them?
  • How are people in this segment the same, and how are they different from other audience segments in your population? How can you allow for these similarities and differences in your marketing?
  • Find as many facts as you can find, then flesh out your profile based on experience and an understanding of how the facts fit together.

Tips for analysis of segmented groups:

  • Play the If, Then game — for each fact (i.e. Creative Changemakers prefer the film program to any other program we offer), ask yourself “If this is true, what else is likely?” (i.e. we should lead with film info in emails to them; we should build membership packages with film benefits and market to them; if we got rid of our film program, we would lose support from our base)
  • What do you know about the makeup of the larger demographic? For example, if your segment tends to live in the Mission neighborhood, what do you know about the overall wealth, family makeup, education level, etc. of that neighborhood?
  • Continually ask the “why” behind a fact. (i.e. say Upbeat Engaged tend to be members at the Pay What You Can level, ask why that level appealed to them, why don’t they want the benefits/or can’t afford the other levels, what draws them to that level, how did they find out about it, and what options are there to change it)

The article ends with a fun use-case example to puzzle through, and several worksheets to help you define and survey potential audience segments. I printed out several of those worksheets and started filling them out for the segments that I’m working with now…


National Center for Arts Research

Watched this webinar today and got some interesting ideas about how to measure arts orgs based on how large research agencies are currently doing so:

It described how to read the data that is located in Data Arts in dashboard form for each organization. NCAR wants to understand arts org health by looking at the whole arts ecosystem.

For every org, they’ve located where it is and take into account community characteristics: who lives nearby, how many other arts orgs nearby? what other businesses, population, density, etc.

Their process:

For each of their large buckets of measurement (e.g. earned revenue, expenses, program activity, staffing, community engagement, etc.), they start with a question (e.g.”How many people attend per dollar spent on marketing?” = response to marketing). Then they go a step further:

  1. What was performance? (all the averages of all orgs by sector, size, and geography)
  2. What explains average performance? (all the driving forces – org age, is it culturally specific? how many programmatic offerings does it have per year? how much new work? community characteristics, availability of public arts funding, etc.etc.)
  3. What drives high performance? (all the Key Intangible Performance Indicators aka KIPI – the mojo that sets a high-performing orgs apart. e.g. good decision making, artistic and managerial expertise, reputation, brand, how well it relates to its community). Examples:
    • “We surveyed audiences and quality was the number one reason they come. It’s the same reason we can hire the artists we want.”
    • “We’ve realized how to leverage our brand to create more consistent revenue streams.”
    • “It’s about getting your mojo working.”



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