Earlier this month at the Future of Storytelling Festival at Staten Island, experts in the field led thought-provoking sessions around what storytelling is, where it’s going, and how it can be most impactful.
Renowned film director and producer Steven Soderbergh took the stage to speak on his new project with HBO in one of the highlights of the Festival. While discussing stylistic devices that allow you to play with both time and perception, Soderbergh shared a number of insightful ideas, which resonate strongly with the ideals behind the Digital Insight Management (DIM) methodology developed by Eric T. Peterson. DIM practices, describes Peterson, can help you “establish ownership, drive action and create accountability for the use of analytics across the business.”
So building on the ideas of Soderbergh, with regards to creating meaningful stories that can spur a reaction, here are 5 tips to improve data-driven Storytelling within organizations in order to incite action:
No matter how concise, explicit or authoritative you believe your data-based story is, don’t discount the fact that human language is open to interpretation and that each individual may project their own ideas, expertise, experience and hopes on your narrative. Use this as an opportunity. If a colleague recommends an unanticipated action based on your insight, explore that idea with them to optimize recommendations and draw on the full value of collaboration.
Always question your own perspective when narrating metric-based performance outcomes and making recommendations based on these. Our implicit bias will likely try to rear its head. Whenever accountability comes into play, and you find yourself depicting a situation where external factors led to your failure to meet a goal, ask yourself whether you’re demonstrating attribution bias. Ask yourself: am I telling an impartial story about what occurred or am I sharing a story that puts my performance in the best light? Attribution bias, does after all, lead even the most logical among us to attempt to place the blame for our own poor performance on external circumstances, while gladly accusing our colleagues of intrinsic weakness when they achieve similar results.
It is very easy to exhibit bias, especially in scenarios where there will be some kind of consequence for sub-optimal performance. We must, however, openly explore how we are performing, and also how successful we are in learning from historical outcomes. Where we do not, we cannot expect to see significant improvements in future results.
Imagine a scenario where you are sharing data-based stories with your colleagues at the end of each week to inform them about what happened in the previous week, how your team performed, what they did well, and what they can try to improve on next week.
In the scenario above there is great proximity to your portrayal of performance. If you were to revisit that same week a year later, and undertake the same analysis, you might find that the story you tell is a completely different one. Not only will you have the benefit of distance from that week which may cool any biases, but you will also (hopefully) have a whole wealth of new knowledge and expertise that enables you to improve on your previous story. You’ll likely have had many experiences in the meantime, you might have a new perspective on the market, and may also have witnessed numerous external success stories that influence the way in which you analyse the outcome of that specific week.
Therefore, it’s always useful to consider the moment at which you are creating your story, how the telling of it may change over time, and to revisit historical insights to attempt to glean further value from them as your perception of those events change.
Although it might be tempting to choose reporting timeframes that shine a light on positive outcomes and dim poorer results, be aware that in doing so you may be over or under-inflating the impact of an action on the KPI or KSI you are analyzing. In the same manner, try to avoid focusing too narrowly on short-term results as they may either fail to properly convey the full value of an initiative, or incorrectly attribute results.
For example, if you run an advertising campaign on social media and only measure the increase in purchases from click-throughs during the period of the campaign, you may fail to recognize purchases made a week, a month, or even a year after the campaign that were influenced by your campaign provoking a response, connection to, or reinforcing your brand with a consumer. Additionally, you may be falsely attributing purchases to your campaign where a purchase decision existed prior to the viewing of the campaign.
As obvious as it might sound, sharing less information that provokes more action is much more valuable than sharing a long-winded story that doesn’t incite any change
Sometimes we forget this though. For example, when we want to make it clear we are experts in a topic we might try a little too hard to justify our ideas, or we may simply forget to put ourselves in the shoes of our audience. In order to make our data-driven Storytelling actionable, make it concise, to the point and focused on the most influential factors and best recommendations for impactful adjustments.
How do you make sure you’re telling actionable stories? I’d love to hear further ideas.
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