In Progress DA Starter Guide

Summer’s Recommended Data Analytics Starter Guide
–free course to figure out how to install R on your computer
–SWIRL package to get started
–Datacamp for basic/intermediate
——find a good tidyverse lesson
Becoming a Data Scientist
Heap Analytics
Roger Pengs’ podcast…
find data events near you!

9 Social Media Metrics to Monitor

I attended a webinar today through AMA on social media metrics to monitor, since we’re always trying to figure out what to cull from the giant data resource that is Facebook Ads and Google Analytics. Some notes:

  • you must understand the WHY behind the WHAT
    • what = sharing, net sentiment, passion, over time, vs. competition
    • why = understand consumers, opinions, emotions, behaviors, themes

They propose 9 metrics:

  1. Mentions
    • # of mentions (total and by channel)
    • whether they are positive or negative (just had a vendor call today with a BI tool that has built their own algorithm for determining what the tone of the mention was!)
    • how passionate the mentions are
  2. Engagement
    • where and when are consumers TALKING TO and ABOUT your organization in social media? –> why? because you want to respond
    • different types of engagement:
      1. owned = people responding to your posts;
      2. partnered = artists + clients talking about you;
      3. earned = people talking about you independent of any posts you’ve made (the most important channel)
  3. Sentiment
    • what your consumers like and dislike about you. allows you to concur or commiserate with their opinions. if people love you, you want to know why, and vice versa
    •  lots to measure/pay attention to here: volume of opinions, what’s the attitude? who is driving the conversation (are they an influencer? where are they posting?), how strongly do they feel? how big is it in relation to other conversations/topics?
    • love some of the methods they suggest re: measuring sentiment:
    • wordclouds
  4. Brand Passion
    • how people describe you, and the energy behind the words they’re using. your organization is “ok” or “good” instead of “awesome”
    • learn what excites your customers most and leverage those insights to increase their loyalty (and find the right pricepoint)
  5. Detractors
    • spot detractors early on. mitigate issues proactively by engaging with detractors
  6. Influencers
    • who are your biggest fans? they deserve to be recognized.
    • how passionate are they? which channels are they on? how influential are they (how many followers do they have?)
  7. Content
    • not all content will resonate with your audience — which is why you have to have multiple approaches to content; which is why you need to segment audiences and provide content specific to each segment
    • are informational articles, funny memes, instagram stories important to your audience? which content drives the most engagement? and where?
    • segment, segment, segment
    • which content is doing best, how to market it to the segments it’s doing best with
  8. Channel
    • which channel is making the most impact.
    • also look at quality of traffic — if visitors “bounce” quickly or don’t convert or aren’t as relevant to you, that channel may not be worthwhile
  9. Share of Voice
    1. are you being mentioned more than your competitors?
    2. it’s not enough how your brand is doing, you’ve got to know where you stand against other brands in your industry. monitoring has to include mentions of your top competitors so you understand where you fall in the hierarchy
    3. look at what people are saying about other organizations — those same opinions might be actionable by your organization. is a museum next door being blasted for no gender-neutral bathrooms or rude guards? make sure you evaluate your bathrooms and guards to make sure if that same person came to you, they wouldn’t have the same problem — e.g.


For Loops vs. While Loops

I’m taking the Intermediate R class in Datacamp right now, and it feels like while loops and for loops do pretty much the same thing, it’s just that their syntax is a little different.

So I looked around a bit on Google to see if anyone could explain the difference and I like this example:

  • While loop is used in situations where we do not know how many times loop needs to be excuted beforehand.
  • For loop is used where we already know about the number of times loop needs to be excuted. Typically for a index used in iteration.

I feel like programming teachers always glance over really crucial information like this. It can be hard to take the step outside of the practice examples given and determine how to apply the program to your own real data and questions. Thank goodness for other curious programmers on Quora, Reddit, and other public forums.

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:

  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:

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:

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… 🙂

Blog at

Up ↑