A Collaborative Culture of Experimentation

This article was written by Jennifer Belissent and originally appeared on the Snowflake Blog here: https://www.snowflake.com/blog/a-collaborative-culture-of-experimentation/


Recently, we recorded a panel session on Data Driven Engagement & Analytics In The Post Covid Era for the FUTR European Summit 2021. Panelists were asked what the future holds. Well, I’ve never claimed to be clairvoyant but I do have a tendency for optimism. The future holds great promise. That is to say, the future looks good for companies who can leverage their data to strengthen relationships with customers and deliver products and services that are relevant and timely for them. However, “to hold great promise” indicates a likelihood, not a done deal. What are the challenges?

First of all, customers are no longer who you think they are. Consumer habits have changed. They are buying more online, eating out less, cooking more, spending more time with family, only getting dressed from the waist up, and spending lots of time on video with friends and co-workers. Will they return to their old ways of doing things? Maybe some will but not all. Dressing from the bottom down will likely come back with the opening of offices. Yet according to McKinsey & Company, 73% of those who switched brands during the pandemic expect to continue their new buying habits.[1] Bottom line: Decision-makers can’t rely on their historical data to inform their decisions about how to serve their customers.

At the same time, the opportunity to discover and understand our customers has never been greater. There is a wealth of information from transactions and digital engagement. Yet most companies are still not fully taking advantage of it—at least not to the full extent. How much of their data do they use? Do they source external data? According to Forrester Research[2], fewer than 10% of companies are truly insights-driven. Why? What’s holding them back?

It’s not easy to do. It requires investment not only in data and technology but also in people and process. The question to the panel that really addressed this challenge was about how to balance analytics and creatives. The subtext was that analytics is not creative. I vehemently disagree. To build an analytics model requires creativity. There is an art to the science of data. But the real key is in combining analytics and creatives, and that comes from experimentation. We build models, test them, evaluate them, tweak them, and repeat. We can also use analytics to test creative elements—a product design, a marketing campaign, a business model, a type of customer engagement. With an increasingly digital buying process, we can extend experimentation across the customer lifecycle and personalize customer experiences.

What do we need to do that?

  1. Access to data across customer lifecycle—cross-company data
  2. Access to new insights about customer behavior—external data sources
  3. Ability to quickly test and iterate—tools to build models and application

That sounds like the Snowflake Data Cloud. But that’s not all you need.

4. A culture of collaboration and experimentation

Companies need to cultivate a culture that encourages innovation, collaboration, and experimentation. In a recent Hidden Brain podcast (Side note: if you don’t listen to Hidden Brain you have to start!), a psychologist discussed our need to challenge ourselves. One example really struck me: A teacher asked first-graders to draw four versions of a house. Then they were asked to critique each other, with explicit instructions to offer constructive suggestions and not personal insults. The children were taught to produce multiple drafts—their first was not the final—and taught to accept input from others and not take it personally. We need to learn to think and rethink, surrounding ourselves with a “challenge network” of people who can point to the fallacies in our work. We need to learn to disagree without being disagreeable.

The tools we need to enable this collaboration and innovation are available. In our world, that’s seamless access to data and robust, rapid platforms for experimentation. Now let’s work on the culture.