Here’s the thing: The scale used on each axis must have equal intervals. It’s an easy mistake to make.
Excel automatically spaces your intervals and labels equidistant from one another but it is assuming that your intervals actually are equidistant. In this graph, that’s not the case. We are missing the months of March, April, July, and August, when either no one was enrolled in the study or we have some missing data. But we can’t just gloss over those months. It isn’t truthful and it distorts the data display.
To make the graph
display correctly, you have to adjust your table in your spreadsheet so that
you have space in the table for those missing months, even if there’s no data
there. Excel will add the months to the axis. You then have a choice about how
to handle the gap months. Should you just skip over them and connect the points
in the line? Should you report them as zeros? Your call, but make that decision
in the Select Data window.
your graph and click Select Data. In that window, look for the button in the
lower left that says Hidden and Empty Cells and click it. In that new window,
you’ll get options for how to handle the missing data.
When I have data collected at irregular intervals, its one of the only times I think its appropriate to use place markers along the line. Usually markers are a little too much extra noise but in this case, they serve a clear purpose to identify where data is actually reported, while still maintaining an equidistant axis.
So, hey, heads up: The Chart Starter Series is probably not for you. If you know my work well, you are probably already a dataviz whiz. The Chart Starter Series is for your colleague. You know the one. The one who keeps asking you to make their graphs.
This series of 10 tutorials is perfect for people who know they need to learn how to make great data visualization but are barely familiar with Excel. I’ve curated the 10 lessons that I implement most frequently in the companies I consult.
Not ready for interactive dashboards? That’s ok! Take a strong yet baby step in the right direction with the Chart Starter Series of tutorials.
Macros and coding are really cool but you don’t need to know anything about those to make high-impact data visualizations that will help you tell clear, compelling data stories. Come learn the basics in a software you already own.
The Chart Starter Series is a selected set of tutorials where we will hold your hand and guide you through effective graph making. You’ll feel like such a pro when you are done.
Individual access to the Chart Starter Series is a one-time payment of $299. You can watch, rewind, practice, and watch again for as long as it takes you to master the skills. We’ll provide you with templates, worksheets, video guidance, a discussion forum, and more.
Every day, the world fills with more and more data visualization that is so complex and intricate, it could be art that hangs on your wall. Cool! Not what your boss wants to see in those decision-making meetings! Join us to learn the skills that will make you look as awesome as you are.
And when you are ready to take a bigger leap into more complex graphing in Excel, when you are ready to explore Tableau or R, we will be here with the Evergreen Data Visualization Academy. And your entry fee into the Chart Starter Series will be discounted from a regular Academy membership.
Read more on the differences between the Chart Starter Series and the Academy or contact me with questions.
I’m guessing that 90% of the people who search on “Harvey
Balls” and end up on this blog post are not here for the same reason I’m here.
I’m here to talk to you about qualitative data. And this one can be a little
Harvey Balls are an unfortunately named set of specific, graduated icons that help convey qualitative judgments. You may be familiar with a version of these if you are an avid reader of Consumer Reports. They use a set of icons – little, coded circles – to depict how a washing machine performed across several variables, like price, efficiency, noise, and quality.
We can adopt a set of Harvey Balls (oh boy) in our
qualitative reporting to help our audiences get a quick visual assessment of
where things stand.
They are most often used in a table format to show whether an item met certain criterion. These little visuals can give your audience a visual status update across a list of themes or other measures, without totally overloading their cognitive processing power.
Compared to something like a gauge chart, they take up much less room on the page. Personally, I find I often suffer from space constraints when visualizing qualitative data, so in some cases a more efficient data-ink ratio is useful. If I just want to visualize maybe 1-3 themes or items, then maybe using something like a gauge chart that takes up more visual space works best. When you have a longer list of items you need to display, you need a visual option like Harvey Balls that is more compact. Harvey Balls can easily be incorporated into tables to make your data come to life a bit.
Let’s say you work for a market research agency and you were contracted by a company to do some research on new online business products. You conduct a series of phone interviews with potential customers and are presenting the results to your client. Here is a great visual of the findings to use during your presentation:
I say this in almost every qualitative post, but the downside to visualizing our qualitative data in this manner is we lose the juicy, meaningful words that populate the Harvey Balls. Therefore, when you can, I suggest adding in some of the raw data to your visual:
Harvey Balls are an easily digestible way to show things like management response to recommendations (agree/partly agree/disagree), the degree to which policies were implemented (fully/somewhat/not a lot/not at all), or levels of quality (high/medium/low). Even meteorologists use them. Harvey Balls have so many applications.
If you are wondering how to make these Harvey Balls, let us
save you from a troublesome google search and tell you how super easy these are
to make in Excel. To get the Harvey Balls,
click in the empty cell in your table and go to the Insert tab. Then find Symbol. Under the font, choose
“Segoe UI Symbol”, and under subject find “Geometric Shapes.” There you will find the Harvey Ball options.
You can simply hit the insert button here.
If you need more options than ¼, ½, ¾, and 1 then you could
also make small multiple pie charts that insert and align them in the table. Or
make up your own set of symbols, like the way Consumer Reports has, just be
sure to tell people somewhere what your symbols mean.
You are probably dying to know why these icons have the name they do. Like many graphs with odd names (hello, Sankey) they were invented by a white dude. Named Harvey. So consider a phrase that has probably never crossed your mind: Try out some Harvey Balls.
This post is an excerpt from my latest book, Effective Data Visualization, 2nd edition, which contains the largest collection of ways to visualize qualitative data in its Chapter 8. Join us in the Evergreen Data Visualization Academy or our Graph Guides program to get hands-on help with video tutorials on how to make effective data visualization, even with qualitative data.
Data visualization workshops are an investment into your growth as a leader in your field.
They should, if they are good, produce immediate returns on your investment which should show up as significantly increased use of your work, attention from existing and potential customers and partners, and more revenue. My workshop clients have seen all of these impacts and more.
You should be investing in professional development in data visualization and design, no question. So let me give you some insight as to what you should be looking for when shopping around for a high-impact data visualization workshop.
The workshop should be based in research. The more that data visualization becomes a popular topic, the more we see subjective opinions about what makes data visualization good, bad, or ugly. Too often, those opinions are positioned as The Truth. Luckily, the more that data visualization becomes a popular topic, the more we see research conducted on it. You want a workshop that is based in the research. Your presenter should have a readily available reference list of research articles that have informed the workshop’s recommendations. Presenters with doctorate degrees in areas related to data visualization and reporting are the surest bet.
At the same time, the company should be known for making the workshop fun. Who wants to spend a day listening to someone walk through study after study? Ick. The presenter should be smart AND relatable. Even funny. Moreover, the workshop should be highly interactive. Get on the phone with a potential presenter and ask what kinds of activities they’ll incorporate. A phone call will also give you a good idea about the presenter’s disposition and ability to make things fun.
The company should have a strong, credible reputation. You should know their name by word-of-mouth. Feel free to ask for references from past clients.
The workshop should include some redesigns of your work. It should be customized to meet your needs. While the workshop should show examples from a broad range of industries, you should see yourself in the slides regularly.
The presenter should be familiar with your industry or at least willing to study in advance of the workshop. You want someone who can speak to your specific circumstances, data scenarios, metrics, and reporting methods.
The presenter should be demonstrably culturally competent. I followed a presenter last fall who repeatedly demeaned the women in the room. Needless to say, he was not getting invited back nor recommended to anyone else.
The company should have published some snippets of their ideas and philosophy, where you can get a pretty good idea of the things they’ll cover in a workshop. This doesn’t have to be in a book, per se. Look for blogs or even tweets that provide evidence of their style and thinking.
Relatedly, the company should have produced many examples of their own data visualizations, in print or online, so that you can see they are walking the talk. That will tell you that the workshop is likely to include their own work, which indicates they’ll have richer insight and more empathy. You do not want someone whose content is just a critique of others’ data visualizations.
Keep in mind that the shorter the workshop, the less useful. I sometimes have folks who ask me for a 60-minute workshop. We’ll barely be able to scratch the surface and your audience will just want more. Give space for learning.
All skilled data visualization workshop providers are booked months in advance. Anyone worth their salt would be. Be sure to start conversations with potential workshoppers at least 6 months before you want the workshop to take place.
At the end of the day, you should feel heard and understood and well-educated. That’s how a data visualization workshop – heck, ANY workshop – will have the most return on your investment because your team will be more likely to take up the lessons provided throughout the day.
This graph type goes by a lot of names: isotype chart, pictograph, or pictogram. Whichever way, it allows us to use symbols rather than stick with the squares that make up the waffle chart. And it is especially well suited to representing small counts of things that can otherwise be distorted when we convert them to percentages. In this example, I’ll show you how to display a set of simple counts.
In this example, we are plotting the number of servings of fruits and
vegetables consumed per day by some families in our simulated program, as well
as the national average. Could just be a bar chart, right? Or even a lollipop
or dot plot. But maybe we need to be a little more precise, since the counts
are so small, so we will replace the bar with an icon of an apple, one icon per
Notice that the servings consumed are in the column labeled Red. The goal is 5 servings per day so the remainder is listed in the next column, called Gray. You’ll also need to procure an apple icon and have a red and gray version saved somewhere on your computer.
Now grab the data in Excel and insert a stacked bar chart.
Right-click on the bars that represent your red data and select Format Data
Series. In the Fill section (for me, this is inside the icon that looks like a
paint bucket), select the radio button by Picture Fill.
Then select the File button and go locate that red apple icon you saved earlier. Then click the Stack and Scale with button.
I never knew what that button did until one of my readers shared the secret with me. So cool!
We want those gray apple icons to look like the remainder. So follow the same
procedure as above to fill the other bars with a picture of the gray apple.
Now, the graph is probably looking a bit distorted with some seriously
squished and unappetizing apples. We will adjust a few things here to make this
First of all, Excel likes to give you more than you need (I hear you nodding
your head in agreement), so the x axis is currently running to 6 but it needs
to stop a 5. To fix this, right click on one of the numbers in your x axis.
Choose Format Axis. Change the Maximum bound to 5 instead of 6. Well… this
probably added to the distortion. But it’ll be easier to deal with the overall
chart area now.
In fact, let’s delete that axis (just click on it and hit the Delete key on
your keyboard) and delete any gridlines.
If you click on one of the apples you’ll see that it is sitting within your
original stacked bar area and that area is skinny. Thin. Stretching your
apples. Let’s make it thicker. Right-click on any bar and select Format Data
Series. You’ll see a menu called Gap Width. This is referring to the width of
the gap between your stacked bars/apples. Set it to 0 and the apples will
become more proportionate.
The apples are still a bit stretched, so resize the overall chart area until
the icons look proportionate.
You could be done at this point but you may want to reorder the categories
on your axis so that Average is at the top and Family 4 is at the bottom.
Right-click on the y axis and select Format Axis. Check the box that says
Categories in Reverse Order.
You’ll notice there’s still one line left, even though we deleted the gridlines.
That’s actually the y axis line, so while in that same menu messing with the
order of the categories, go into the paint bucket icon and, in the Line menu,
select No Line.
Now, delete the legend and add a title. Boom!
This process will be fine if you are working in whole numbers. But let’s say Family 3 ate 4.25 servings of fruits and vegetables. This method is not gonna work. Another one of my readers sent me the trick to make partial amounts still work.
In the spreadsheet, fill the Gray column with all 5’s. Then, instead of a stacked bar chart, insert a clustered bar chart. Follow the same steps to pop the icons into the bar. Then right-click on the red apples and select Format Data Series. Then click the button for Secondary Axis. The red apples – even the partial pieces – will show up in front of the gray ones. Cool trick!
Whether you call it a pictograph, pictogram, isotype chart, or icon array it’s an efficient way of communicating small datasets. It might take a few minutes of your time to construct something like this but the time investment is worth it. These graph types are easy to read.
These pictogram instructions are one of the new chart types + step-by-step instructions in the second edition of Effective Data Visualization, where you can find even more options for visualizing quantitative OR qualitative data. This book has been the #1 New Release on Amazon’s Communications, Business Communication, AND Media Studies lists, just in the first couple of weeks since release.
Some might claim they look at a blank PowerPoint slide and see a source of hope and possibility, similar to how artists supposedly see inspiration in a blank canvas. I call bullshit on this. Artists did not just walk up to a blank canvas and envision their end product. They sketched.
Dali drew. Sketching was how historically famous artists
studied their subjects to understand them at a deeper level and think through
the mechanics of how they’d actually produce their artwork when it came to
When Van Gogh started his artistic journey, his work was all
very dark and dim, representing his subject matter of peasant life. Then he
went to Paris one Spring and got inspired by color and figured he needed to
incorporate this into his work. So he practiced. He made quick paintings that
were experiments in complementary colors so he could hone his craft.
Of course now his studies and sketches are in museums and
most of our won’t make it that far but we need to follow the same process
because it works.
Historians recently uncovered what they believe to be Da Vinci’s practice sketch of the Mona Lisa.
Sketching doesn’t *just* help him figure out how her chin
should be positioned, it’s also far less expensive. Paint and canvas are
pricey. So artists sketch because it lets them quickly iterate – meaning try,
fail, rethink, try again – in a less risky space so when it comes time to pick
up the paintbrush or crack open your dashboard software, you have a plan that
can be implemented efficiently.
Sketching saves you time & money. And keeps you from
making costly errors.
This is a classic case of where the software’s shiny buttons messed everything up. What began as a rectangular stacked area graph was modified using the Mask tool in Illustrator, where a designer (probably unintentionally) added geographic associations with racial groups that are laughably untrue. My favorite is the suggestion that all Asians moved to Canada in 2010. This is a mistake caused in part by software that would have likely been sorted out before appearing on national television if it had been sketched first.
You get better
insights when you first sketch because sketching facilitates reflection on the
topic at hand. Sketching is where you work out the logical errors you
didn’t know you had in your thinking. Because our software programs will let us
do anything. But they aren’t human, so they can’t do the thinking that’s
actually required to tell stories with data.
Research shows that sketching frees up our brain space so
that we can think. Rather than trying to background process all the buttons and
menus staring us down when we open software, the minimal distraction of pencil
and paper helps our working memory processing so we can let our brains do their
Let’s say you are charged with developing a dashboard to track key performance indicators related to a customer satisfaction survey. The most disappointing time sink of your life would occur by opening a dashboard software and trying to mess around to see what you get.
Because even if you are a master at your software, you’ll still spend weeks to months getting the draft dashboard made and then you’ll show the draft to your boss who will say, let me show this to the team and she’ll come back a week later asking for a zillion changes. Because people don’t know what they really want until they see something to react to. And you would have wasted weeks of your time.
Instead, sketch a mock up, using a predesigned template from my new book, in an afternoon. Multiple copies of three different dashboard templates are available in the sketchbook.
Use the graph paper – we also have dot grid paper – to sketch the visualizations so you are as accurate as possible, as realistic to the potential data as possible, when showing sketches to end users. We don’t want incorrect proportions to distract folks from judgments about whether the dashboard would be useful.
This is how you save yourself so much time and money. This is where sketching helps us be more data-driven because it keeps our end users – our audience – at the forefront of our development process.
The Data Visualization Sketchbook contains templates for a complete project reporting package. I’ve included a project profile page to help you document decisions related to fonts, colors, graphic elements, and what deliverables are due when. The sketchbook includes square and dot grid paper. Which is also an excuse to get out of the office for a bit.
We have three different handout helpers, depending on how many points you need to make. Like infographics but with more class.
And three different dashboard designs, dependent on how many metrics you need to report.
You’ll find a slide guide for your PowerPoints and a report structure to focus the look and feel of your longer written documents.
Plus, plenty of blank pages for you to sketch out your own thoughts if these inspire you to go in a different direction.
Give yourself the gift of generous, analog, thinking space. First, sketch so that your final product development process is fast, efficient, and accurate. Grab The Data Visualization Sketchbook on Amazon or directly from my publishers, SAGE.
Diagrams come in many different flavors – mind maps, concept maps, flow charts, and so on – but at the core, they are all visualizations of how themes relate to one another.
ubiquitous as diagrams are, they have some significant shortcomings.
tend to be most useful as a mental organizing activity for the people who make
the diagram. Outsiders coming in fresh have a much harder time seeing how the
elements of the diagram fit together and make sense. I think that is generally
because diagrams lack enough narrative to explain what is going on. We often
use diagrams without explaining them. Or, more precisely, we use diagrams
without connecting the diagram pieces to their associated parts of our
We learned this lesson the hard way. We had made a diagram illustrating
how a new initiative was set up and what short, medium, and long-term outcomes
the initiative was expected to produce. We showed the diagram to the top exec
in a planned meeting to pitch my potential involvement in the project. We had
to slowly walk him through the diagram, even though it made complete sense to
me. When we finished, he said, “This is nice but what interests me is what
happens in the arrows between the boxes in this diagram.” Dang. What a good
If done the right way, they can take a complex process, flow,
or concept and visually tell the story in a way that just cannot be done with
Let’s say we are working with a team to develop some
standards of practice around a goal. If put into words alone, here is how it
Through a collaborative process our team and leadership have developed 3 core standards: safety, leadership, innovation, and teamwork. Under the teamwork standard, there are some goal areas that are broad ways we can work tougher to measure teamwork. The first is collaboration. Collaboration is central to working together so there are 3 new initiatives being launched that help promote collaboration. Each initiative is intentionally scaffolded to work towards building a collaborative culture. We have contracted with a consultant to host a series of quarterly work group sessions to implement the initiatives. The second goal area is diversity. Diversity in staff identity, culture, background, and experience can build a strong team. To support our diversity goal, we need to first get a better grasp on the current state of diversity in our workplace. Therefore, we are starting an internal research committee to examine opportunities for growth in diversity within our team.
Ok we are going to stop. We only got to explain two goal areas
under one of the three core standards in just over 250 words. Whew. You can imagine if we kept going, we would
write an entire brief on this. Imagine a 5-page brief explaining all the core
standards, accompanying goal areas, and all the measures connected to the
goals. At the end of the brief, we doubt anyone will remember all the
particular goal areas linked to each standard.
To review, you would need to carefully read through the text. This is a
great example of when a diagram will come in handy.
Often times, a diagram cannot necessarily replace all the
text explaining the complexity of the data, but it helps condense all that
complex language into something bite-sized that people can visually link up to
the words. The trick is making sure you help the reader link the diagram to the
explanatory words. Let’s walk through an example.
We mentioned how diagrams are often misused because they are built in such a way that doesn’t help support the information being presented.
This may not be the worst diagram in the world, but it is
not great. Let’s talk about what isn’t working.
First of all, the color and font are Microsoft default colors. We should
never use the defaults because we’re not boring default people and neither are
our ideas. Why does every shape have a border, fill, and shadow? The measure
boxes look misaligned and all the lines make it feels cluttered. Let’s juxtapose
this with a better formatted diagram, with the same information being visualized.
This example engages us in the information in new ways. The
intentional use of icons and colors helps readers make sense of how the parts
of the diagram are related and better aligns the goal and measures with the
corresponding standard. The measure box designs make it so much easier to see
the overlap between the goals.
There is just a touch of some simple design that make this
come to life a bit more. Do not be intimidated by this! If you know how to insert a shape and textbox
in PowerPoint, you have all the skills you need to make a nice diagram. A
diagram is just a bunch of lines, circles, rectangles, and icons that are *very
Aside from the step of cleaning up the diagram, what makes
it useful to others who were not involved in making it is to repeat sections
from the diagram in the margins of corresponding sections throughout the report.
This step visually connects the diagram to the explanatory text, helping the reader
see the big picture. Afterwards, they are much more likely to see the original
diagram as a helpful tool.
With some quick fixes, the diagrams you build to visually support your qualitative text and concepts will be better than ever by moving away from Microsoft defaults and adding in intentional color, icons, alignment, and narrative.
When Jenny Lyons and I were pulling together the revised chapter on Qualitative Visualization in Effective Data Visualization, 2nd edition, we ended up ditching this section on dendrograms. In the list of most-likely-to-use qualitative visuals, this one is probably not in anyone’s top ten. But we still wanted to share this visual with you because, well, the world of qualitative viz could always use more options.
Dendrograms are not THE most common qualitative visual because they require a data generated through a hierarchical cluster analysis. Cluster analysis can be a useful tool in analyzing qualitative data. By clustering groups of participants with similar qualitative codes, you can better understand your findings. According to Henry & team, this analysis can help “reveal things like participant motive and the reasons behind counterintuitive findings.”
Check out Henry’s article to learn more about the analysis. Here, let’s just focus on describing a dendrogram that could display those hierarchical cluster analysis findings. They can be a little confusing at first, especially since the x-axis has 100% closest to the y-axis when we aren’t used to seeing it that way. Walk through this example with us.
In a dendrogram, there are clades which indicate each branch, leaves which are the terminal end of each clade, and outliers. Outliers are clades with only one leaf.
The width of the branch indicates how similar the items/clusters are from each other. The y-axis are all the findings in the data. Then, the x-axis is the percent of agreement between participants. The closer a clade is to 100% on the x-axis, the higher the level of agreement between participants. Usually people decide to cut the tree in order to group clusters together. There are many factors that determine where to cut the tree but let’s say in this example we cut the tree at 80%. This dendrogram highlights that 80% of participants agree with clusters A/B and D/E/F.
One downside to dendrograms is that they can get complicated. They may be best suited for when you have a small number of findings. As usual, use color and effective titles to make your message come to life. In this example, let’s say we are examining participants responses given in interviews about their reasons for attending leadership training on sexual assault prevention. You can see the two clusters that come to life at 80% agreement: 1) a cluster at 92% agreement on community impact and 2) another cluster at 80% on personal benefits.
Software programs that run hierarchical cluster analyses also have the capability to display the data in a dendrogram, but it probably won’t look that appealing. So import the image into PowerPoint. Then, add colored lines, dots, and a title to help make the visual easier to understand for your audience. This will help make a complicated visual like a dendrogram readable and impactful!
Every time I show this trick to even veteran Excel ninjas, their heads explode.
So you have probably heard me preach the gospel of small multiples once or twice before. Breaking a clutter-y graph into a lot of smaller graphs that show one piece of data at a time can make interpretation a whole lot easier for your audiences. Cause it’s nice to actually be able to see things.
This sort of visual is challenging, what with the disconnected legend and some data values that overlap. Perfect candidate for small multiples – a series of small charts.
I’ll break the data out into 5 graphs, one line per graph. It could be 15 minutes of tweaks to each graph to work out Excel’s defaults and make all 5 look wonderful. You can probably handle 15 minutes, but what if this graph had 20 lines in it? Sounds tedious. Here’s the quick way to generate small multiples.
Make the first graph with just one trend line in it. Make it beautiful. Make it perfect. Then copy it and paste a duplicate right next door. When clicked into the duplicated graph, you should see that the data it is graphing are highlighted in blue back in the spreadsheet. See it?
Just drag that blue to new data and the graph will update.
I’m not even kidding. Try it.
Put your mouse on the edge of the blue space. Your cursor will turn into a double-headed arrow cross thing. Then click and drag the blue to the next line down. POW. You’re done.
So easy! If this move deletes the formatting you did to your first graph, take one more step. Go back to your first graph and click Copy (in the Home tab). Then click in your duplicated graph and select the drop down arrow under Paste. Click Paste Special and then click Formats.
You’ll be knocking out small multiples so fast, you’ll have time to write me an email about how great that was.
I taught this technique to Evergreen Data Visualization Academy members during one of our monthly Office Hours calls. Sue had submitted a complicated line graph and wanted advice on how to display it better. I fixed her line graph, live, and showed everyone on the call how to do this. Comments in the chat box included “WHAAAAAAA?!” and “Do that again!”
That’s the kind of help you get in the Academy. In addition to dozens of tutorials on how to make high impact graphs, I show you all my little secrets that will give you your life back. You get my hands-on help with your tricky data visualizations.
Enrollment in the Academy is only possible during short windows, open just twice a year.
If you have been looking for online training to help you improve your data visualization skills, join us. We will make your life better.
I have learned all of these lessons the hard way. I now have two checklists I run through before I have a workshop. One handles logistics and it’ll only be useful if you also run workshops. The other helps me determine whether a potential client and I are a good fit and it’ll be useful for anyone who has autonomy over who they work with.
Stephanie’s Pre-Presentation Checklist
There are all kinds of things I have forgotten to ask about in advance that have resulted in some serious sadness.
Like, are you going to feed me lunch? Several times in 2018 I have shown up to a full day workshop and discovered they aren’t providing lunch. Ever stood in front of a group and poured all our energy into a group for 6 hours without food? Those $13 airport pistachios were saving me from fainting.
Can I use my own laptop? I have some clients, especially federal government, who can’t risk viruses on their network, so I can’t use my laptop with my custom fonts. Finding this out at the last minute SUCKS.
Where is the freaking workshop? I’m ready to get into my Lyft at 8:30 in the morning and I realize I don’t know where I’m going and the client isn’t answering emails that early. I need to collect address and importantly cell phone numbers ahead of time.
Now some people I know and love are overpreparers. They are the ones who wheel suitcases into a presentation with bags hanging off of both shoulders so they can handle anything that comes their way. Sheila Robinson, I’m talking about you. I am probably on the other end of that spectrum but I’m trying to prevent more fails in the future with a checklist, so here you go.
Who is feeding me?
Can I use my own laptop?
What’s your cell phone number?
When will we start and end (and eat)?
Who are the big personalities?
This question about the “big personalities” in the room is an important and sensitive one. It is SO HELPFUL to have a heads up if there’s going to be that one guy who asks a billion questions or that one gal who hates on everything. You won’t be able to change their behavior, but you’ll have a chance to make a plan for handling it.
Stephanie’s Client Screening Checklist
My mentor once passed along this advice on the criteria to consider when choosing projects: Fun, Lucrative, No Assholes
And these three criteria are written in all caps on the office whiteboard. But how do you determine these things when initially emailing and talking with a potential client? I am pretty brave but I still can’t exactly ask “Are you an asshole?”
Sometimes you’ll know right away, because they’ll send you an email asking for new work with an impossible deadline and talk to you in such a way that you know this ain’t gonna be good. Other times, you have to look for clues to figure out whether all 3 criteria are met. These questions have saved me so much headache:
Am I a commodity?
Are they contacting others?
If so, how will they judge quality?
Is this person nice?
Is this company good?
I’ll break those down a few of those for ya.
Am I a commodity? Meaning, are they looking for any vendor who can deliver a presentation workshop or are they looking for me? I’ll have less of an impact if they are looking for just anyone.
Are they contacting others? Am I bidding or is this sole source? If I am required to write a proposal, I think more carefully about whether it will be worth our time, given how many noncompetitive requests we get every day.
If so, how will they judge quality? What will be the evaluation criteria? I know for a fact that if the budget is their biggest decision point, we will never win. Quality isn’t cheap.
My aim here is to avoid putting myself in this situation: