I work for Transurban, one of the world's leading toll road operators, and part of my job is to help the business do data-led storytelling.

The challenge with telling stories using data is that, when you have a great deal of data, not only are there lots of stories you can tell, there a lots of ways in which you can tell each of those stories.

Here is an example of how one of the stories I was telling evolved over time.

2020, when the world changed

Screenshot of a timeline showing lockdown events from March to July 2020.

This story starts in March 2020 when the COVID-19 pandemic hit and cities around the world went into lockdown. For a business like ours that only makes money when people travel on our toll roads, this was a concern.

We could tell from the drop in traffic on our roads how much things had changed, but looking at only toll road data doesn’t tell you the whole story. We didn’t know what was happening on non-toll roads and on public transport, for example. And we didn’t know how concerned people were about travelling both now and in the near future.

So we asked.

We worked with Nature, a market research agency, to conduct an online survey of a thousand random people across each of the cities in which we operate (Melbourne, Sydney, Brisbane, Washington DC, and Montreal).

Screenshot of a report cover. The report is titled ‘Industry Report - Urban Mobility Trends from COVID-19’ and was published in August 2020 by Transurban.

Evolution 1: Which story are you really trying to tell?

In our survey we asked people about their use of various modes of transport and how much they were using each mode before and during the pandemic, as well as how much they expected to use those modes once pandemic restrictions were lifted.

We could have just shared these results straight-up in our report, but that wouldn’t have been good storytelling. That’s because when you present the numbers in this way, you end up telling two stories:

Which transport modes people are using more than others
How people’s use of those transport modes has changed over time

You can see what I mean when you look at the chart I’ve mocked-up below. What jumps out most is the relative bar-size difference across modes: mainly how much taller the car/motorcycle bars are compared to everything else. What gets lost here is the difference within each mode itself, such as the change in bus usage.

A clustered bar chart showing the pre-pandemic, current, and post-restrictions (expected) use of the following transport modes: car/motorcycle, train/subway, tram/light rail, bus, bicycle.

The more important story at the time, in the context of pandemic restrictions, was the change in usage of each mode of transport, and so that’s what our chart needed to focus on.

After experimenting with a few different chart types, the charts below are what we put in the report. These charts let you tell a story that goes like this: “In Melbourne there was an 84% drop in train usage. And even after restrictions are lifted, people expect their use of trains will be 19% lower than pre-pandemic levels.”

Screenshot of a series of bar charts for Melbourne, Sydney, Brisbane, GWA, and Montreal collectively titled: ‘How daily users of transport expect their use will change (% change)’. Figure 11 shows this change for train/subway and figure 12 shows this for bus.

Lesson 1: First figure out which story you’re trying to tell, then use a chart that tells this specific story best.

Evolution 2: Simplifying your story

The next storytelling evolution happened just six months later in our second report. We realised people didn’t care about the detailed, mode-by-mode numbers, so we grouped the all transport modes into ‘public transport’ and ‘private vehicles’.

Also, by this time lots of other organisations had shared their own road traffic and public transport usage data, so we didn’t have to tell the whole story anymore. Instead we simplified our story to focus just on people’s expectations of future transport mode usage, as shown in the charts below.

Screenshot of a series of bar charts for Melbourne, Sydney, and Brisbane collectively titled: ‘How daily transport users expect use will change post-pandemic’. The charts are grouped under the public transport and private vehicles subheadings.

Lesson 2: Avoid the temptation of sharing all the data you’ve collected; instead find the simplest version of the story that will get your point across.

Evolution 3: Telling a more nuanced story as the situation develops

A further six months later, when we collected the data for our third report, the situation was different again: despite various configurations of partial and full lockdowns, people still needed to move around and we had the numbers to show how they were accomplishing that.

Basically, we realised that the daily transport mode use numbers weren’t telling the whole story. People had drastically cut down on their daily travel, yes, but their ‘few times a week’ travel had started to pick up. So we began sharing both the daily and few-times-a-week data in our report, with the chart covering the latter shown below.

Screenshot of a series of bar charts for Melbourne, Sydney, and Brisbane. The charts are grouped under the public transport and private vehicles subheadings. Descriptive text below the charts explains that these charts are for regular (ie ‘few times a week’) use of transport modes.

Lesson 3: When you can no longer tell your story with a single metric, figure out which additional metrics you can add to tell a more nuanced version of your story.

Evolution 4: Telling additional stories when the focus changes

When it came to round four of our report, lockdowns had started to lift and the big new story was about ‘return to work’: businesses wanted all their employees to come back to the office, but employees didn’t want to return to the pre-pandemic daily grind.

So we collected and shared data about people’s ability and uptake of flexible work and whether or not they’d be willing to change employers if their flexible working needs weren’t met. (This was in addition to all our earlier transport mode use charts, of course.)

Two complex pie charts, one for Australia and one for North America, under the collective title: ‘Figure 8: Willingness to change employers if flexible working preferences were not catered for’.

Lesson 4: When a situation evolves, collect additional data to tell more aspects of your story.

Evolution 5: Painting a more complete picture using multiple data points

By the time our fifth report rolled around we needed to tell a more complete story of how and why people were moving around our cities.

Lockdowns had been lifted and transport mode usage had started to stabilise. However, active transport (walking, cycling, etc) had become more popular over the pandemic, so we started including those numbers in our charts as well.

A series of bar charts showing daily and regular usage for public transport, private vehicles, and active transport for Melbourne under the title: ‘Figure 2: Transport mode choice now vs expected use in 12 months’.

Meanwhile the flexible work discussion had shifted to how many days people were working from home versus the office, so we started collecting and sharing those numbers.

A bar chart titled: ‘Figure 4: Average number of days people travel to their workplace or place or study (or travel around for their job/study)’.

And instead of talking only about general travel around our cities, we expanded our research to talk specifically about travel for work. Commuters comprise a large proportion of people who move around our cities every day, so it made sense to dig further into this segment’s travel patterns.

A series of bar charts under the collective title: ‘Figure 8: Main mode of transport used to commute to (or travel around for) work/study before and since the COVID-19 pandemic’. There are charts for Melbourne, Sydney, Brisbane, Greater Washington Area, and Montreal.

Lesson 5: Once the main metric you are tracking has stabilised, look at the additional metrics you could report on to tell a more complete story.

Evolution 6: Switching from crisis mode to business as usual

By 2023 travel patterns had started to stabilise, so we switched our research cadence from half-yearly to annual.

And because things had started to get back to “normal”, we moved to reporting transport mode usage year-on-year instead of comparing everything to pre-pandemic levels.

We added more nuance to our ‘days working from the office’ story by showing how this varies by occupation.

A bar chart titled: ‘Figure 4: Average number of days people travel to their workplace or place (or travel around for their job) by occupation’.

We talked about how much people expected their office-working days to change in the coming twelve months.

A stacked bar chart titled: ‘Figure 5: Expected change in the number of days people travel to their workplace (or travel around for their job) over the next 12 months’.

And we showed why people expected to increase the number of days they travelled to their workplace in the near future.

A series of bar charts for Australia, Greater Washington Area, and Montreal titled: ‘Figure 7: Reasons people expect to increase the number of days they travel to their workplace’.

Lesson 6: Once the obvious driver of change has been accounted for or eliminated (pandemic lockdowns, in our case), figure out the other reasons why people are doing what they’re doing.

Evolution 7: Changing focus when the time is right

It’s now 2024 and travel patterns have properly stabilised, and so the discussion about transport mode choice and working-from-home is pretty much over.

The new travel and commuting patterns that have been established are unlikely to change as quickly and drastically as they did during the pandemic.

So instead of transport mode usage being our headline story, this year we’re talking more about congestion and what people are doing to avoid peak-hour travel.

A stacked bar chart titled: ‘Figure 22: Frequency of rush hour avoidance’.

We’ve also moved our focus to a bunch of other urban mobility topics, such as road safety, sustainability, and road user charging. Road safety and sustainability even got their own mini-reports starting in 2022.

The biggest topic these days, however, is people’s concern about the rising cost of living and their ability to manage their expenses. We started sharing data about this two years ago and we now devote a whole section in our report to it.

A series of bar charts for 2024, 2023, and 2022 titled: ‘Figure 9: Concern about household expenses - Australia’.

Lesson 7: Know when it’s time to move on from your once-primary metric to other metrics that are now more important and immediate.

Sharing this data with stakeholders

The storytelling evolution I’ve been talking about didn’t just happen with what we were saying, but also with how we were saying it.

You’ll have noticed from the screenshots above that the charts in our reports evolved over the years, and that’s one part of it.

The other part is how we’ve constantly tweaked and updated the Transurban insights hub, which is where all this data is shared online.

So don’t forget that evolution happens in all aspects of storytelling.

Screenshot of a website titled ‘Insights hub’ with the subheading “Explore our data”.

Overall lessons in story evolution

Aside from the individual story-evolution lessons I’ve shared along the way, I’ve learned two overall lessons from doing seven rounds of this research now.

First, keep your research focus as narrow as possible. Conducting research like this is expensive and time consuming, so only ask the questions that will help tell your story.

My team does this by creating a draft outline or structure of our report before we finalise the list of questions we’re going to ask in our survey. This is where most of the story evolution happens. If we realise there is a gap in our story, we add questions to fill that gap. If a question we asked last time didn’t give us useful data, we tweak it to get to the nuance that will be valuable. If we realise we’re asking questions in the survey that don’t help tell our story, we remove them from the questionnaire.

Second, engage early with your stakeholders and then keep them updated of your progress. If you want to tell a comprehensive story, you need diversity of thought in your planning process. People will suggest angles to your story that wouldn’t have occurred to you, so make sure you capture everyone’s unique perspectives.

My team does this by sharing our initial report outline and draft questionnaire with key stakeholders. We then meet with groups of those stakeholders to get their individual and collective feedback. This is where the rest of the story evolution happens. Our survey questionnaires end up being much more thorough and much more nuanced after we’ve gone through this comprehensive internal engagement process.

Screenshot of a website showing two boxes, one titled ‘Urban mobility trends reports’ and other titled ‘Insights reports’. The boxes have links to reports from August 2020 to August 2024.

We’ve come a long way

It’s a rare opportunity to be able to conduct and then share this type of longitudinal research publicly with your stakeholders. And this is doubly rare in the corporate communications space.

I’m very grateful to have had this opportunity. It’s been hard work, but it’s also been the most fun I’ve had with research, data analytics, and data-led storytelling.

Here’s to many more years of findings insights and then using them to inform, educate, and make good business decisions.

The 2021 Melbourne Business Analytics Conference kicked off today. This is a four-day online conference with talks running from 10am-2pm, Monday to Thursday.

Screenshot of a web browser window showing a virtual conference layout, with boxes for speaker video, slides, Q&A, presenter information, and event resources.

42% female speakers

I’ve attended all four #MBSAnalytics conferences since they launched in 2017 [1] and have been constantly impressed with the gender diversity that Melbourne Business School have achieved with their speakers and panellists. This year, for example, 42% of speakers and panellists are women.

Screenshot of a column chart titled ‘% female speakers at #MBSAnalytics conference’. There are four columns on the chart, one each for the years 2017, 2018, 2019, and 2021. The percentage of female speakers and panellists shows above these columns i… — Screenshot of a column chart titled ‘% female speakers at #MBSAnalytics conference’. There are four columns on the chart, one each for the years 2017, 2018, 2019, and 2021. The percentage of female speakers and panellists shows above these columns is: 31%, 39%, 51%, and 42% respectively.

Not a manel in sight

Not just that, but at all four conferences there has never been a single manel (ie all-male panel of speakers and experts) – which I think is hugely impressive.

Screenshot of a webpage showing a grid of head shots. These are some of the speakers at the 2021 Melbourne Business Analytics conference. Half the speakers on the screen are female and two of the eight speakers shown are people of colour.

This is big deal

Tracking and reporting on the proportion of female speakers and panellists is important because (a) that’s not often tracked and (b) a high proportion is rarely achieved at conferences in this field. In fact, none of the business or analytics conferences I’ve attended in the last decade (?) have had more than a third of speakers who aren’t male.

The highest I’ve seen elsewhere was at MeasureCamp Melbourne in 2018 where 31% of the speakers were women. That took a bit of effort too, since the year before that number had been zero!

Photo of two hand drawn graphs on lined paper under the heading ‘Measure Camp Melbourne 24 Feb 2018’. The first chart shows 34% of attendees are women, the second shows 31% of speakers are women. That attendee percentage is an “estimate based on wel… — Photo of two hand drawn graphs on lined paper under the heading ‘Measure Camp Melbourne 24 Feb 2018’. The first chart shows 34% of attendees are women, the second shows 31% of speakers are women. That attendee percentage is an “estimate based on welcome session attendance”.

And it compares well with the industry

This high proportion of female speakers and panellists is particularly great because:

~29% of full time computer science graduates are women and
~35% of 2020 Melbourne Business School graduates were women.

Also, a couple of years ago I did a Professional Certificate in Business Analytics from Melbourne Business School. As part of that I took two subjects that had 17% and 30% female students respectively.

I know these numbers aren’t definitive, but it’s awesome that the proportion of female speakers at the #MBSAnalytics conference is at least higher than the proportion of women typically graduating into this field of work and study.

So kudos to the folks from Melbourne Business School who make this happen every year. This is already my favourite conference and seeing those stats makes it even better.

—

Footnote

[1] Your maths isn’t wrong. They’ve had four conferences in five years because they had to cancel their 2020 event because of the COVID-19 pandemic.

FYI

I’ve tweeted about these numbers each year I’ve attended this conference: 2017, 2018, 2019, and 2020.

Proportion of women and non-binary speakers at MBAC 2024

Evolving our data-led storytelling

2020, when the world changed

Evolution 1: Which story are you really trying to tell?

Evolution 2: Simplifying your story

Evolution 3: Telling a more nuanced story as the situation develops

Evolution 4: Telling additional stories when the focus changes

Evolution 5: Painting a more complete picture using multiple data points

Evolution 6: Switching from crisis mode to business as usual

Evolution 7: Changing focus when the time is right

Sharing this data with stakeholders

Overall lessons in story evolution

We’ve come a long way

Melbourne Business Analytics Conference 2022

Benefit of registering early

Proportion of women on the stage

Gender diversity at Melbourne Business Analytics Conference 2021

42% female speakers

Not a manel in sight

This is big deal

And it compares well with the industry

Footnote

FYI

2020, when the world changed

Evolution 1: Which story are you really trying to tell?

Evolution 2: Simplifying your story

Evolution 3: Telling a more nuanced story as the situation develops

Evolution 4: Telling additional stories when the focus changes

Evolution 5: Painting a more complete picture using multiple data points

Evolution 6: Switching from crisis mode to business as usual

Evolution 7: Changing focus when the time is right

Sharing this data with stakeholders

Overall lessons in story evolution

We’ve come a long way

Benefit of registering early

Proportion of women on the stage

42% female speakers

Not a manel in sight

This is big deal

And it compares well with the industry

Footnote

FYI

Insanity Works

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