Winning BIG with Data

May 15, 2024

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In the following article, Avinash Kaushik explores the use of data analytics, identifying key metrics and strategies to make the most of your data and win big.

In a (business) world where data flows freely like a gushing river, it’s heartbreaking that a vast majority of decision-making continues to be based on faith.

A complex set of realities contribute to this. Most reports and dashboards remain glorified data pukes. Most senior leaders fail to articulate, and execute, clear business strategies (making it impossible to identify what success – or priorities – look like). The torrid pace of new data generation shows no sign of slowing down and we’re constantly trying to catch up.

Still, delivering both customer delight at scale and convert it into long-term profit remains crucial. I recommend the following simple adjustments to win BIG with data.

1. Outcomes, not activity.

Most teams are obsessed with activity metrics. How many impressions/reach/page views/store visits did we get? All this data is easy to find, and it’s liberally splayed all over our dashboards.


Pathway with modern concrete arches and a landscape at the end


The problem is that on the brightest day, none of this activity is any indication of whether your customers are happier or if you’re making money. Worse, these metrics create incentives for your employees to behave badly.

Let the activity metrics be used for minor diagnostic purposes. Switch your focus to an obsession with outcome metrics: Customer task completion rate. Economic value. Revenue. Lifetime value. Loyalty. And more.

The end result will be less data (YES!), sharper execution focus, and better incentives for your employees.

2. Insights, not data.

Data is simply what happened. Even the most amazing outcomes data is solely what and not why.


Black and white photo of a woman's profile, doomed in on her eye


It’s ok as an executive to be curious about what happened. What you desperately need to know is why did it happen? And what in the name of Thor should we do? Stop asking for data. Start asking for insights.

Go one step further, tell your analysts/organizations that you only want text-based output in English, Swedish, Hindi – text that says in plain terms here are the top five reasons for the business performance of our outcome metrics, and here are the top five actions the data recommends taking.

The impact on your organization will be instantaneous and magnificent.

3. Experiments, not opinion.

Years ago, at a company I worked for, a term was coined the term HiPPO. The highest paid person’s opinion.


Little kid holding up their hand in front of their face


It was coined from the observation that in any given meeting, data was frequently ignored in favor of the opinion of the highest paid person in the room. Sad face.

Opinions are good, they can open new possibilities or fill in gaps in our thinking. Yet, opinions are unexplainable expressions of faith. I believe X. It’s good you believe X. Perhaps you have lots of experience. Take those opinions and, if you feel they will have a HUGE impact on the business, make sure they are executed as experiments.

The HiPPO can have an opinion. When you execute it as an experiment, you 1. lower risk to the business, 2. create an opportunity to learn, and 3. build an ideal democracy. Collectively, experiments build a resilient data-influence culture.

4. People, not tools.

Twenty years ago, I’d created this rule for analytics: If you have $100 to be successful with data, invest $10 in tools and $90 in big brains!


Two people in front of a blue wall look at a laptop


Every passing year since, the 10/90 rule has proven itself over and over again. World-class analytics tools are free (ex: Google Analytics), world-class visualization solutions are free (ex:, and world-class analytics hacks are free (ex: search analytics on Github).

What you really, really, need are smart people to make sense of all that data, to help you focus on outcomes, and to help you execute complex experiments. They remain extremely expensive. So pay for them. Don’t be the leader/ company that puts all its faith in tools, or worse yet, spends 3.5 years implementing a complex analytics solution that is out of date 1.5 years before completion of implementation.

People are the solution. (Yes, AI is coming. But not for a while.)

5. Use data before and during – not just after!

For the overachievers reading this book… Almost all use of data in your organization is likely after the money has already been spent (on Sales, Marketing, Service, HR). It looks backwards.


White circular staircase from below


The most sophisticated use of data is before you spend money (to ensure you’ve done everything humanly possible to ensure positive business outcomes for your company) and during the process of spending money (to ensure you take money from what’s not working and put it towards what is working).

At the point, the backwards looking analysis after the money’s gone is almost boring. You already know what happened: You. Won. Big!

Obviously, this is not easy, but it’s this living on the edge of analytics that builds a competitive advantage.

Carpe Diem.

About the author

Avinash Kaushik helps executive teams leverage innovative strategies to outsmart competitors. He’s the author of two bestselling books on digital analytics, and leads Global Strategic Analytics at Google. Acting on his passion to teach, Avinash has delivered keynotes at major global conferences and taught at Stanford, ULCA and Santa Clara University. His weekly newsletter, The Marketing Analytics Intersect, raises funds for Doctors Without Borders and The Marshall Project.

Avinash Kaushik

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