How businesses can turn Excel data into free PR
Turning data into content like blogs, reports and press releases can be one of the most cost-effective ways of generating valuable media coverage. Mike Marquiss explains.
Every startup can benefit from free publicity and media coverage. It can shine a light on the big issues in your sector, and where you can make a difference. It can ensure your product is seen by more potential customers immediately, or improve SEO rankings so that more customers visit your site in the future. If funding is your top priority, appearing in a prestigious title like Forbes can impress investors and give your brand more credibility.
Unfortunately, for smaller businesses, you are competing for column inches with big brands with big marketing budgets and dedicated PR teams. It’s hard enough just to get a reporter to read your email, let alone write a story. You may need more inventive ways to get noticed. Turning data into content like blogs, reports and press releases can be one of the most cost-effective ways of generating valuable media coverage. There’s no monopoly on who can use data. Good data is good data. It’s usually completely free.
For example, The State of Australian Startup Funding Report analysed thousands of deals to reveal that only 4% of startup funding in Australia went to female-founded startups in 2023. Likewise, Melbourne-based air-conditioning startup, Conry Tech, used publicly available ABS data to prove that the Australian manufacturing sector shrunk more than any other industry over the last 15 years. Both reports generated valuable coverage and shone a light on hugely important topics.
Below is a guide to the kind of data-driven stories reporters want, potential data sources, and a step-by-step tutorial on how to turn your Excel spreadsheets into headlines.
What journalists want
Your data-driven stories need to help journalists answer the biggest questions about the people, places, and industries they cover. If you can give journalists the right data, with the right presentation — and the right story – it will get attention. Your content always needs to be:
Timely: Journalists want to receive stories relevant to the times we live in and the news of the day. It’s no good using data from 2016 or trying to get a reporter to cover a subject that is no longer in the news cycle.
Opinionated: Your data should be used to tell a story with a definite opinion and narrative. It’s no good sitting on the fence.
Trusted: You need to provide well-researched, trustworthy information that will hold up to scrutiny. If your data is flimsy, so is your story.
Interesting: Your data or report needs to offer a unique or counter-intuitive perspective on a subject or shine a light on an entirely new one. If you think of news as a 24/7 dialogue between reporters and readers, you need to find ways to further the conversation, change its direction, or start a new one entirely.
Where to find data
A lot of startups have customer or user data that can be used to tell a compelling story (provided you do not disclose sensitive or personal information). There are also plenty of other sources available. The Australian census is a remarkable public dataset. You can also search for keywords relevant to your industry on the Australian Bureau of Statistics website. Or sign up to their Table Builder service for access to figures like the Businesses in Australia data set (BLADE) or National Health Survey results.
Consider reaching out to connections in your industry and your customers, partners, or suppliers who might have relevant datasets. You can also look at the reports/data published by your relevant industry body. If you are patient, try issuing FOI requests to relevant public sector bodies like the DCCEEW for access to previously unseen data sets and documents. Also look at sites like the Reddit – r/dataisbeautiful sub or the Pudding.cool website for inspiration.
If you really don’t know where to start, Google the subject you want to explore, and see who’s issued statistics and reports on it in the past. This may help you to do something similar, or even recreate and adapt their methods for your own purposes.
Scrub
Once you have a data set you want to use, the first step is to give it a good scrub. The data needs to be usable and reliable. This means removing duplicates, errors and anything that will throw your results off later. Filter the column so you can easily identify different variations such as 0/nil, or ‘format cells’ for the entire column and ensure that all variations of figures and dates (eg $1000/1000/ $1000.00) are consistent.
You may also want to format your spreadsheet so that the fonts, alignment, borders, colours, are consistent. A clean spreadsheet is a lot easier to work with.
Identify
At this point, you can start to look for anomalies and trends – write down the big who, what, why, when, how questions that your dataset may be able to answer. Try to identify the averages and the outliers. Your average figures can be a proxy for the average customer/company/professional in your industry, while the outliers can add colour to the story.
Having average and outlier figures allows you to create two very different kinds of stories depending on your desired tone. For example, either “Australians spent an average of $50 on diet drinks last year” or “Australians spend up to $600 a year on soft drinks”.
Visualise
This trial-and-error stage is all about experimentation. Play around with the data and different types of charts until you start to see some trends emerge.
If you’re in Excel, filter all your columns and sort by descending/ascending order. Try selecting and deselecting different options in your filters to see if any trends and outliers emerge. Use the conditional formatting option to colour code some of your columns from red to green so you can quickly identify the standout high/low figures. Use formulas to create new columns that compare one column or data set with another. You want to see if one trend influences another, if there’s correlation, reverse correlation, or causation. An obvious example for many companies would be location data. If you can filter your data by location, you may be able to compare each state and tell a story about which region is best/fastest growing/most at risk for your industry and why.
Play around with date, location, and any other variables you have. Plot your data over time, see how your figures go up or down, and how they change as a percentage (not just raw numbers). The best way to do this is to create line graphs and bar charts that will allow you to visualise trends in only a few clicks. Select the data you want to visualise and select ‘insert’ > ‘recommended charts’, or try experimenting with pivot tables if you are an Excel pro. You can also plug your data in into a tool like Datawrapper.de if you want to turn geographic data into great-looking, data-rich maps.
Here are four graphs I made in a matter of minutes using various Australian climate data sources.
Pitfalls
There are some major pitfalls you will want to avoid. This may be obvious, but sample size and quality are vital. A survey of 50 customers is not a large enough figure to draw many meaningful conclusions, you certainly couldn’t call this a representative sample for your industry.
Also be aware that if you start looking for patterns, you will find them, and some will be red herrings. Take note of line goes up/down trends – if your startup and userbase are growing, some lines will go up or down as a result. For instance, a cybersecurity company may log more cybersecurity incidents over time as it grows – this data would not conclusively prove that cyber threats have increased. It’s important to isolate and analyse the data so your company trends don’t influence your findings.
Also be careful of filtering data too much. If you over-filter the data or compare every possible variable, you may reduce the sample size inadvertently or see trends where there are none. You can also be tricked into confusing correlation with causation. For example, for an extended period in the 2000s, goals scored by Aaron Ramsey were always followed by a major celebrity death. It’s a complete coincidence, but this can happen with your data if you start trying to link random and disconnected data points.
Write
Get out of Excel and try to tell a short story using the insights you have gleaned from your data. What does it tell you? What are the most interesting results? What surprised you most? Do this in bullet points, not paragraphs.
Before you write anything longer, show your bullet points to someone who doesn’t already know the dataset. Make sure it is understandable and interesting to someone not involved in the process.
If you have an interesting story, then you can write something longer – a report, a press release, or even just a blog. This is what you need to share with the reporters who cover your sector. This can be done by your company directly or through a trusted PR agency/consultant.
In the right hands, your free company data can be transformed into compelling stories, eye-catching headlines, and meaningful press coverage that can help you grow the business. If you don’t take advantage, it can be a huge missed opportunity.
Mike Marquiss is the founder of Decoded Comms.
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