How does a traditional bricks-and-mortar retailer transform itself into an omni-channel business with strong digital and data science capabilities?In this episode of Leaders of Analytics we learn from Bunnings General Manager, Data and Analytics, Genevieve Elliott, how the company is transforming its operations using data and analytics.As Australia and New Zealand’s largest retailer of home improvement products, Bunnings is a highly complex organisation with a large physical footprint, a wide product range and an elaborate supply chain.Bunnings is almost 130 years old and has undergone tremendous growth over the last three decades. The company’s well-known strategy of “lowest price, widest range and best customer experience” is increasingly being driven by the company’s growing data and analytics capability.In this episode we discuss:Genevieve’s career journey and how she ended up in data and analyticsHow Bunnings uses data to create operational efficiencies, improve customer experience and optimise pricingHow the team prioritises projects and engages with the organisationHow the Data & Analytics team is driving a data-driven culture through the companyGenevieve’s advice to other analytics leaders wanting to drive strategically important results for their organisation, and much more.Genevieve Elliott on LinkedIn: https://www.linkedin.com/in/genevieve-elliott/

Is your company good at customer success and retention? Chances are that you could be better.For most businesses with a recurring revenue model, customer churn is a very costly affair. Whenever a customer leaves, you lose out on recurring revenue, forgo the opportunity of expansion (cross sell) revenue and have to pay for another round of acquisition costs to cover the loss.In my personal experience, customer retention is both art and science. Machine learning and other data science techniques can be used to identify customers who are likely to churn, but it is equally important to craft meaningful and delightful interactions throughout the customer lifecycle.So, what’s required to become a lean, mean retention machine?In this episode of Leaders of Analytics, I speak to Sami Kaipa to learn the best practices of data-driven customer retention.  Sami is an experienced technology executive, serial entrepreneur and start-up advisor. He is co-founder of Tingono, an AI-driven customer retention platform.Listen to this episode as we discuss:Sami's journey as an entrepreneur and corporate technology executiveThe core elements of customer success and retention that every business should masterA deep dive into the concepts of customer retention, expansion and NRRThe economics of customer retention and expansionHow data science and machine learning can help with retention, and much more.Connect with Sami on LinkedIn: https://www.linkedin.com/in/samkaipa/Tingono's blog: https://www.tingono.com/blog

Do you really need a data-driven culture? Maybe not.According to Bill Schmarzo, the CEO’s mandate is to become value-driven, not data-driven. For analytics teams that means one thing: no one cares about your data, they want results!In this episode of Leaders of Analytics, Bill and I explore the economics of data & analytics and how to drive powerful decisions with data. Decisions that turn into business value.Bill is the author of four text books and one comic book on generating value with analytics. He is a long-serving business executive, adjunct professor, university educator and global influencer in the sphere of big data, digital transformation and data & analytics leadership.In this episode of Leaders of Analytics, we discuss:Why Bill has split his career between corporate leadership and educationWhat value engineering is and how it pertains to data and analyticsHow to determine the economic value of data and analyticsWhy data management the single most important business discipline in the 21st century, and much more.Bill's website: https://deanofbigdata.com/Bill on LinkedIn: https://www.linkedin.com/in/schmarzo/Bill on Twitter: https://twitter.com/schmarzo 

Great analytics teams understand that they are responsible for two things concurrently: production and consumption.Most analytics teams master the production part well. After all, that’s why they exist, to produce analytics.However, analytics only matter if someone consumes them and makes valuable decisions as a result. “Decision + value” is what we’re after.To be able to make valuable decisions from analytics, consumers must be data and analytics literate, and that often comes down to education and culture creation.So, how do you build analytics literacy in your organisation?In this episode of Leaders of Analytics, Ben Jarvis, Head of Scaled Customer Services and Operations AUNZ at Google, answers this question and many more related to building a strong analytics culture.Listen to learn:How Ben went from practicing law to becoming a senior analytics leader and operational GMHow to coach and mentor technical and non-technical stakeholders on data and analytics literacyHow do traditional businesses that aren’t born out of the internet era can transform into data-driven and analytics-literate organisations, and much more.Connect with Ben on LinkedIn: https://www.linkedin.com/in/ben-stuart-jarvis/

If you’re anything like me, you have a love/hate relationship with marketing. Marketing can be delightful, obnoxious or somewhere in-between, depending on content and context.Most of us remember an ad from our youth that has given us a life-long emotional connection to a brand or product. Most of us also remember that obnoxious sales call or email campaign that made us swear never to buy from the offending company again.In this episode of Leaders of Analytics, you will learn from Ikechi Okoronkwo why data-driven marketers have a leg-up when it comes to designing and executing impactful campaigns that hit the right audiences and create delight.Ikechi is Executive Director, Managing Partner and Head of Business Intelligence & Analytics at Mindshare. Mindshare is a global media and marketing agency, and part of global marketing powerhouse GroupM.Listen to this episode to learn:What Ikechi sees as the biggest opportunities in data-driven marketingWhat kinds of analytics to invest in to optimise the impact of your marketing effortsWhat kinds of data is needed to take advantage of these opportunities, and how to collect itHow Ikechi and colleagues use data and analytics to distinguish between rational and emotional reactions to advertisingHow to drive a culture of experimentation and measurement among colleagues and stakeholders who are more creatively than analytically minded, and much more.Ikechi on LinkedIn: https://www.linkedin.com/in/ikechi-okoronkwo-0318579/

Business leaders are changing.Today, it’s not enough to be a strategic thinker and good people leader to be successful in the corporate world. Why?Modern business leaders are customer-centric and understand how to create a personalised customer experience using customer data.Modern business leaders are data-driven and understand how to make decisions based on probabilistic outcomes, not just gut feel.Modern business leaders understand what it takes to develop and deploy artificial intelligence in their organisation.So, how do we educate our future business leaders to be analytics literate, technically capable and able design and use AI effectively and responsibly?I recently spoke to Professor Hind Benbya to answer this question and many more relating to educating our future business leaders.Hind is the Head of the Department of Information Systems & Business Analytics at Deakin University, where she leads the strategic direction of the department as well as academic aspects of teaching, research and industry engagement.In this episode of Leaders of Analytics, you will learn:The critical must-learn skills for students wanting to shape the future of business with data and analyticsThe role of data, analytics and AI in business 10 years from now and how today’s business leaders must prepareHow we bring today’s business leaders and executives up to speed with data and analyticsHow analytics leaders can drive their organisations to become truly data-driven, and much more. Hind on LinkedIn: https://www.linkedin.com/in/hindbenbya/Hind's research and publications: https://scholar.google.com/citations?user=KNAW0xsAAAAJ&hl=enDeakin's Department of Information Systems & Business Analytics: https://www.deakin.edu.au/business/department-of-information-systems-and-business-analytics

Most of us take for granted that food is always available to us when we need it. Our local supermarkets have shelves stacked with produce from all corners of the world. Rarely do we stop to think that the items in our shopping carts have been on a long journey involving months of work by many people.How does all this food get produced in the first place, reliably, consistently and to a high standard? How do we combine and utilise scarce resources to feed billions of people around the world every day?I recently caught up with Serg Masis to answer these questions and understand how data science is used to optimise food production around the world.Serg is a Climate & Agronomic Data Scientist at global agriculture company Syngenta and author of the book ‘Interpretable Machine Learning with Python’.In this episode of Leaders of Analytics, we discuss:The biggest challenges facing our global food system and how data science can help solve theseHow data science is used to help the environmentWhy Serg wrote the book ‘Interpretable Machine Learning with Python’ and why we should read itHow to make models more interpretable, and much more.Connect with Serg:Serg's website: https://www.serg.ai/#about-meSerg on LinkedIn: https://www.linkedin.com/in/smasis/Serg's books from Packt: https://www.packtpub.com/authors/serg-masis

Professional sports have undergone a true data revolution over the last two decades.Today, all major sports teams, regardless of sports code, use analytics and data science to drive team performance, optimise game outcomes and scout young talent.Why has analytics become so popular in professional sports and how does it help drive a competitive edge?To answer these questions and many more relating to the sports analytics, I recently spoke to Ari Kaplan.Ari has spent more than three decades using analytics to measure and understand human ability, scout future superstars and win professional sports titles.He is known as “The Real Moneyball Guy” because of his work in baseball and his involvement in making the Hollywood classic Moneyball.Today, Ari is Global AI Evangelist at DataRobot.Listen to this episode of Leaders of Analytics to learn:How Ari became “the Real Moneyball Guy”The analytics the Chicago Cubs used to break a 108-year drought by winning the World Series in 2016The evolution of analytics and data science in sportsWhat the business world can learn from sports in terms of using analytics to gain a competitive edgeWhere sports analytics is going in the future, and much more.

It’s no secret that data and analytics can be used to create a competitive advantage for almost any modern business.In fact, the customer data you capture in the course of doing business is one of the strongest differentiators between you and the competition.So, how do we build an organisation that is capable of both producing and consuming truly differentiating data products?It’s not enough to just have a great analytics team that is capable of producing high quality work. We also need an organisation that is able to consume this output, however advanced it might be.Back by popular demand, analytics executive and author of ‘Building Analytics Teams’ John Thompson is returning to Leaders of Analytics to talk about the future of analytics leadership.In this episode, we discuss:Where analytics teams should sit in the organisational structureThe typical mistakes businesses make when designing analytics teams and embedding them in the organisationHow we plant the seed of advanced analytics and build a data-driven cultureHow we select and prioritise the right data and analytics projects to work onThe main purpose and remit of a Chief Data & Analytics OfficerWhat the perfect data-driven organisation looks like, and much more.John on LinkedIn: https://www.linkedin.com/in/johnkthompson/John's book 'Building Analytics Teams': https://www.packtpub.com/product/building-analytics-teams/9781800203167Defensive vs. offensive data & analytics: https://hbr.org/2017/05/whats-your-data-strategy

Every company, regardless of size, is dealing with a barrage of data. In any typical organisation, there is more information on hand than we know how to use or manage.While every team in the organisation is screaming for analytics professionals to turn data into insight, a strong data and analytics tech stack is foundational to being able to make sense of it all.The need for a robust and efficient data and analytics tech stack has created a sprawling industry for new technology solutions that sell the promise of seamless integration and faster insights.Today, there are a plethora of data and analytics platforms available, most with very high valuations attached to them.But do we really need all these tools to make us super-powered data users?To answer this question and many more related to the data and analytics tech stack, I recently spoke to Benn Stancil.Benn is the co-founder and Chief Analytics Officer at Mode. Mode is a modern analytics and BI solution that combines SQL, Python, R and visual analysis to answer questions for its users.In this episode of Leaders of Analytics, you will learn:What the perfect analytics tech stack looks like and why.Programmatic automation of the analytics workflow.What will cutting-edge analytics tech be able to do 5-10 years from now.Why Been thinks the Chief Analytics Officer role should be redefined, and much more.Connect with BennBenn on LinkedIn: https://www.linkedin.com/in/benn-stancil/Benn on Twitter: https://twitter.com/bennstancilBenn's (brilliant) Substack blog: https://benn.substack.com/