Handling local marketing at scale

While this may sound simple, the chain in question has more than 700 locations in the U.S. alone. Managing such a strategy manually at that scale would have been impossible. By using automation, they were ready to respond quickly to signs of local demand, whenever and wherever they emerged.

This example also serves to show how insights generated through local marketing activity can be valuable to the wider business. Using search as a barometer for demand helped the chain understand which locations could be opened profitably and how to staff them accordingly.

No one data source, no matter how strong, can provide all the answers. Key for marketers is to combine the information they have with other sources to generate better insights. Making smart use of their customer data — about pricing, loyalty, and seasonality, for example — can help provide a much deeper understanding of what consumers are looking for. With a broader set of data points, machine learning solutions can better identify insights in consumer patterns.

Maintain user privacy while delivering helpful experiences

At Google, we’re also exploring a range of other approaches to improve user privacy, while ensuring publishers can earn what they need to fund great content, and advertisers can reach the right people for their products. For example, we support the use of advertiser and publisher first-party data (based on direct interactions with customers they have relationships with) to deliver more relevant and helpful experiences — as long as users have transparency and control over the use of that data.

Adding even one additional source of information in this way can provide a clearer lens to observe consumer patterns. Due to the pandemic, driving in the U.S. fell by almost 40% in April. One leading insurer incorporated aggregated and anonymized traffic data from public sources, such as state governments, to identify mobility trends. How, where, and when traffic volume returns to its previous level can be seen, to some degree, in the number of searches for car insurance, but that would leave the insurer trying to catch potential customers at the very last minute of their journey. With a more rounded and balanced data set, the team can see earlier where traffic is returning and respond with more comprehensive marketing plans.