Machine learning and predictive analytics need to meld seamlessly with core app functionality. The technology needs to “just work,” without steep learning curves or frustrating dead ends. So I’d expect any company who experiments with machine learning for local search to start with a simple set of problems and hone the user experience.
The series demonstrates the real complexity of cross-platform digital marketing and the importance of a data-driven strategy in identifying meaningful objectives and tracking performance. This commentary explains how Brand Battles are constructed and how their subject areas fits into the bigger picture of local marketing for national brands.
The first phase of mobile software relied on us to express a desire and thereby to enable a service. Our actions initiated services that capitalized on the phone’s ability to maximize proximity. Now we’re entering a second phase where, for many of us, connectivity and location awareness will be active for longer stretches of our days.
Around the holidays, consumers tend to spend a lot more time on multiple devices, altering standard shopping habits and behaviors. This means brands and businesses need to ensure they are accurately and competitively represented in search, social, and mobile channels, and that social engagement and advertising efforts are properly targeted to the right consumers at the right times.
Local search takes place across services that are proprietary and dedicated, even if indirectly, toward earning revenue for the companies that run them. But that doesn’t preclude us from thinking of local search as a kind of public utility whose objective is to provide accurate and consistent information. That means treating local listings primarily as a public good, not a business.
The evidence is in. Reviews on social media have a material impact on the capital investments made by nationwide brands. The key is strength in numbers: A national brand will be more likely to have the critical mass of reviews required in order to move beyond anecdotal evidence and glean statistically significant results.