Author: Tiphaine Goisbeault

https://embed-ssl.wistia.com/deliveries/e01834f7f763f4a45a8c88d6a8da5e26.mp4

Research is a large name covering many diverse fields. Today I can provide you with a POV on the marketing and ads research that I developed for Google: our mission is to generate actionable insights that put the user in their context at the heart of business decisions.

As our users’ behaviors have evolved over the past years and have become more complex, innovating is definitely key to developing actionable insights.

You can create innovation at every step while respecting user privacy and vendors safety: it can be about opening new areas of thought, challenging well-known marketing frameworks (like rethinking the purchasing behaviors – aka the Messy Middle – through behavioral science), testing new methodologies to improve our operational marketing (like the virtual shopping capabilities created by EyeSee). But how do we do this? It is important to create a space for innovation by partnering and brainstorming with many diverse research companies, from startups building experimentations to large and well-known research companies established in many countries. It is an interactive process as we can also share best practices or new methodologies with our vendors or research guidelines created by our researchers (like sharing UX requirements for mobile questionnaires) to improve research quality.

https://embed-ssl.wistia.com/deliveries/1f1a967b799c6be4ff25592802591b88.mp4

In France, we were glad to test the virtual in-store shopping test capabilities provided by EyeSee – which proved to be especially useful when retailers closed shops. This allowed us to conduct marketing tests on Chromebook as initially planned with no interruption.

In the summer, we experienced a slightly normal period when the [COVID-19] situation improved. We re-started to use traditional methodologies like quantitative and qualitative interviews – if safety conditions (like social-distancing) were respected for both vendors and users.

The new challenge was: how to detect the long-term trends and shopping behaviors that will remain after COVID-19?

We started to implement long-term trackers and identified opportunities to use the time series necessary to develop predictive models. We are still experimenting, but we should see the result in a couple of months!