This week on “Masters of Metrics”, our host Emma Lo Russo talks to Virginia Wheway, Vice President of Data and Analytics for Koala (formerly at BHP and Boeing) about her career deploying data projects for many organizations.
In this episode, we deep dive into how to build analytics capability into the DNA of your organization to drive customer acquisition and better direct your marketing dollars.
Virginia is an expert in analytics and data mining. In September 2020, she joined Koala, an Australian startup mattress and furniture company with an amazing growth story and some super creative, ballsy campaigns to its name.
Remember the ads with people launching themselves onto mattresses and the wine glass manages to stay upright? That’s Koala. You’re going to love this interview.
Virginia was a data scientist before data science became sexy. She is passionate, articulate and in this episode shares some great practical and real ways people can apply data, build a team of data analysts, influence organizations and kick off successful journeys in data and analytics.
How analysis of big data helps businesses (then, and now)
When Virginia began her career in statistics and mathematics, she was often involved in TQM, or Total Quality Management. Data sets were small, and storing and processing data was expensive. “So you had to make the most of not a lot of data!”
But in the 1990s, data mining became popular (what we would now call data analytics). “People knew it was cool and that it could do something, but … I don’t think society really got the true value out of it in that phase.” Virginia soon joined an advanced data project at Boeing, pioneering how to use black box flight recorder data to predict when an aircraft will need maintenance – in real time.
“Boeing was the first place I worked that really made money out of their data… by getting insights no one else could, and then selling those insights back to airlines.” This was an entirely new way to use data to generate profits and benefit organizations at the same time.
With data, if you really want to get value from it, you need to invest and be patient. “Companies that will really succeed in data are the ones who are willing to invest in the horrible, grungy untangling of it.” It’s like eating your veggies before having your dessert!
Every organization’s data strategy is different, and often it grows organically over time. There is no magic template. But if leaders are consistent and persistent, if they’re discerning and deliberate about their talent and hiring, and if they can advocate for their data and for their data teams, success will follow.
How to get started with data analysis and interpretation
At Koala, Virginia is balancing two speeds of data science. On one hand, Koala needs to build a strong data foundation, with the right platform, team and strategy. On the other hand, the business still needs to know the conversion rates of customers buying sofas in Japan on Sunday night, and they need to know it now!
It’s a challenge to satisfy both speeds, but Virginia has a lot of practice setting things up from scratch, and relishes the experience.
When she is starting fresh with new data at a new organization, these are the questions Virginia spends time asking the organization’s stakeholders and employees. The questions are great advice for any business owners who want to invest more in data science and analytics!
- Why do you think you need data analysts and a data team?
- Is your organization ready for a data team? Will they have work to do?
- What keeps you awake at night?
- What do you already know about your data?
- What are you really trying to ask your data? What do you really want to know?
- What decisions do you want to or need to make using data?
With the answers to these questions, you may be better placed to understand whether you need a data visualization expert or a web analytics engineer, for example. Asking questions and being curious is one of the pillars of being a data analyst or data scientist.
What are the most important business metrics?
There is no magic metric to business success. But Virginia says something simple for a business owner to set up and watch is cumulative sums. If you’re looking at trendlines over time, it can take a while to show if something is trending downward. With cumulative change in revenue, cumulative users of your site, or even cumulative change in conversion rate over a period of time, you’re much more likely to see if you’re starting to lose ground on that metric.
And it’s easy to communicate to others that something is happening and you need to do something about it!
Virginia also admitted that when she got started at Koala, she needed to learn a lot of the core concepts of marketing data and analytics in particular, like the marketing funnel! Every industry has its own fundamentals to learn, even when it comes to something as essential as numbers and data.
For business owners, this translates into learning what works for you and your business and your data and metrics. Once you have your own data and metrics to analyze, you’ll be way more confident in making decisions for your business.
Rapid fire questions!
- Guilty pleasure? Cheese Doritos. Or sneaking a spoon into a tub of ice-cream when no one else is home.
- Inspirations? Rollie shoes and Hunter Candles.
- What age would you pick to be for the rest of your life? Her 30s: “I killed it in my 30s.” More sensible, successful, financially independent and adventurous than her 20s, had also finished her PhD and met her husband.
Our picks for the best takeaways
On succeeding in data: “Companies that will really succeed in data are the ones who are willing to invest in the horrible, grungy untangling of it.”
On getting results from data: “We have to eat our veggies for a while before we can have dessert. We have to invest in data and work on it before we can create those cool analytics or case studies you read about in magazines.”
On the popularity of AI and ML: “I think data professionals have a responsibility to keep people honest. We have a really big education role on what [AI and ML] actually are, and what is possible given your data maturity and your in-house capability.”
On being the first data leader at a company: “It takes energy, persistence, consistency: you have to keep showing up and showing up. And sometimes you just need to find that nugget that’s going to flick the switch… get the first spark going.”
On how to get started as a data analyst: “Get very good at being questioning, and being curious. No matter how clever you are, or how skilled you are, always be curious.”