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How to fail at storytelling

By Samantha Grant

I will admit that I’m a data nerd. Even so, I might skim through an evaluation journal and read only the articles that are relevant to my work. But recently, I got the summer 2016 issue of New Directions for Evaluation and read it from cover to cover. I was completely sucked in. I have never before read an entire issue.

What made this issue so interesting? It was all about stories. The journal highlighted seven North American evaluation pioneers who shared their evaluation roots and personal insights into the field. They did this by sharing personal stories. These stories were fascinating, and they touched on some deep issues like domestic violence, religious beliefs and power sharing.

My take-away: storytelling is powerful. I think most youth workers know that, but so often we miss the opportunities to gather and tell meaningful stories. According to Jenny Nabben, “Emotionally engaging stories affect more areas of the brain than rational, data-driven messages – meaning that they are far more likely to resonate.”

I think we've all had the experience of reading or hearing a really great story that sticks in our minds. It’s hard to articulate how to get the right story. “Right” is hard to define for each situation and audience, but I can give you my thoughts on how we get stories totally wrong. Here are four ways that I think we mess up storytelling.

We make the story about our organization. Sure, your organization matters. You’re telling stories to demonstrate the work that you do, but when you make the story all about you and your organization, you lose the reader. The story should tell about a person or people you serve -- that’s what draws people in.

We want the story to be perfect. Rehearsed storytelling can lead to a final product that reads like it’s being told by a robot. Robots do not hold people’s interest. A few ums or ahhs can be edited out -- or you can keep them in to maintain the authenticity of the story. As listeners, we filter these out all the time.

We have the story but nothing to back it up. Here’s where the evaluator in me can go crazy. You might have one shining case study that you share in your annual report, but if the rest of your data don’t support that single account, your story becomes an outlier. Make sure your data support the story you’re telling, and vice versa.

For bonus points, you can strengthen a story by bringing in evaluation or community data to support the message. In doing so, you appeal to the individual that is drawn in by a story but also earn the respect of a data-loving critic like me.

We try to appeal to emotions and end up victimizing our customers. Poor helpless young person is saved by your after school program. Sound familiar? Look for it in the stories you hear (and maybe in your organization) and chances are you will see evidence of a victim story. Brandi Olson, a data consultant with a skill for helping organizations talk about impact, recommends that for greatest impact you share a hero’s story instead. You want your main character to be a hero, not a victim who is saved by your organization. That gets people listening.

Taking some time to think about the story you want to tell and what not to do can put you on the path to storytelling greatness. I’d love to hear more about the powerful stories that you have told in your organization. What stories have you used that have grabbed your audience? How do you tell stories in your program?

-- Samantha Grant, evaluation director

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Comments

  1. I appreciate that you emphasize the importance of making an emotional connection with a story AND using data to back it all up. Too often, organizations feel like they have to choose between the story or the data. But when you're telling the right kind of story, the data can help to create a connection with the audience by building trust.

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  2. Hi Brandi. Thanks for your comment! As an evaluator, I'm always looking at the data first and then trying to find the story that strengthens that data point. I know others do it the other way around too, but I think you can fight off the critics by knowing the data first.

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