Marketers know that capturing – and using – consumer data is key to successful marketing. And in this era of digital transformation, data is even more important to achieving and maintaining the leading edge among competitors. Assembling a strong team of data analysts is a crucial part of digital transformation.
According to a study commissioned by Google in March of this year, gains in revenue, profit and market share were seen most in companies with a strong, successful analytics team. However, another study found that only 9% of businesses are effectively using insights and technology as they navigate their digital transformation.
How can your organization improve its data usage and effectiveness? Google offers these three core principles to help build a successful data team and strategy.
- Talent > tools. There are always shiny new toys in the digital marketing realm. But when it comes to investing in analytics, people should be prioritized over tools. Human capital is more flexible than software and tools. It’s important to remember that people are the solution to improved analytics and the performance of tools can be limited with the wrong people behind them. Google developed and recommends the 10/90 rule for success in web analytics: Invest 10% of your analytics budget in tools and 90% in people.
- Grow and nurture a culture of curiosity. Encouraging an environment where questions are asked and conventions are challenged is a part of a test-and-learn mindset where experimentation can help to identify successful strategies and optimize what works. Celebrating not only successes but failures can improve performance by reviewing what didn’t work and learning from mistakes.
- Collaboration with the C-suite. It goes without saying that the C-suite must be committed to providing and promoting the highest level of analytics capabilities. Organization leaders should collaborate with data analysts to understand the data behind the decisions they’re making. Leaders who know when to collect data, present business challenges to their data analysts and look to quantify goals and objectives will be the best at providing an environment that is friendly to analytics.
Is your organization making the most effective use of its data? If not, how can you improve?