3 Location Requests For Enhanced Campaigns

The shift to Enhanced Campaigns is important to every advertiser, but critically important to Enterprise programs, particularly those with meaningful brick and mortar footprints.

The reality of Enhanced Campaigns today doesn’t create much urgency to switch over. We don’t get any more controls, and in fact, we lose some. However, the near-term future of EC is very exciting, particularly with respect to improved location targeting.

Chain business operations from retailers, to package delivery firms, to insurance companies with local agents will see the true benefits of the new structure; hopefully, soon.

Enterprise Programs Need More Data

Google has a few thorny issues to address to unlock the full potential of advanced geo-targeting using the Enhanced Campaign architecture. They have to reveal enough information to platform providers and technology shops like RKG so we can bid smartly.

At the same time, they have to respect the privacy agreements with users, particularly users of mobile devices. Users may give Google permission to know their precise location, but don’t really give it permission to share that information with others.

Enterprise advertisers also don’t really need to know a user’s longitude, latitude and altitude, either. What we do need is for the information Google shares with us to match the bidding and targeting controls we have through Enhanced Campaigns. When advertisers can carefully measure the performance variations associated with each factor, we can react to them wisely.

Google can provide us with fabulous controls without revealing too much about users by passing us two (ideally, three or more) pieces of information through valuetrack parameters (or via API tied to a click id that matches the GCLID passed):

1. Postal code. Whether through postal code, city, state or country, this information combined with census data can be used effectively to understand how regions and types of regions impact performance.

We can currently map IP addresses to zip codes, but that information is pretty far from ideally accurate; and, the mapping of IPS to zips is a pain. Google has click-level city, state data available through the API, but the click id there isn’t the GCLID passed in the redirect; so, we can’t yet connect it to the conversion and post-conversion information we have. We’ve asked them to address that.

The power of being able to model data from different regions looking at all types of attributes of that region, and then be able to implement bid adjustments to easily act on that modeled data is exciting.

Carving up programs for geo-targeting is a painful process and we end up not being able to get very granular in our approach to this because of campaign limitations. The more a company spends in paid search, the more money is at stake in this level of granularity.

2. Relative location. For a brick and mortar business, we don’t need to know exact location, as long as we know where someone is in relationship to us. Knowing business addresses, Google could give us the distance a user is from our nearest store/office without revealing the person’s precise location.

This would provide crucial information for understanding how proximity influences advertising value.

3. Context of location. Is the user stationary, walking, or riding? Is the user at “home,” “office,” or “other”? Again, Google could give advertisers cues to the user’s intent that would likely impact ad value, and therefore, allow us to target smartly. Passing each context in a separate valuetrack parameter would be awesome.

Benefits Of Laser Targeting

Conspiracy theorists might suggest that Google really doesn’t want advertisers to have all this information or to bid smartly, at all. I’ve heard folks suggest that this is really nothing more than a mechanism to make it more difficult for advertisers to target consumers. I don’t buy it because that would actually hurt Google in the long run.

Google should not fear laser targeting. Folks in Mountainview might think, “if advertisers can really target the best users, they’ll spend less on other users and that might hurt our revenue.” Not so. “Cherry picking” actually benefits Google and advertisers and users, for that matter. There are several reasons for this:

• Right-sizing the competition. To the extent that there are “good” targets and “bad” targets, advertisers will spend less to attract the latter, but more to attract the former. Differential bidding works in both directions and the net effect is a win for Google.
• My cherry is sometimes your lemon. Online pure-plays will pay more for those likely to convert online; brick and mortar businesses might pay more for users who do not convert online. Allowing each business to target their own cherries will lead to identification of more cherries.
• More bang for the buck. Smarter bidding leads to more efficient resource allocation; more efficient resource allocation leads to more bang for the advertising buck; more bang for the buck leads to more bucks. Advertisers that spend to efficiency objectives will spend more; those that spend to a budget will enjoy better ROI, which leads to a larger budget.

More efficiency helps advertisers get more from the channel cost effectively. This happens because users get advertising that is more relevant and useful to them, and it has the net impact of Google making more money — a three way win.

Cost-Effectiveness Of Laser Targeting

The notion of one bid plus a string of modifiers is not the ideal, but it is what we have to work with. The construct assumes that the contextual cues are independent of one another, which likely isn’t the case.

An advertiser might want to bid more for smart phone users within a mile of a brick and mortar location but not after the stores are closed. But, damping the bids after 5PM for smartphones also damps the bids for stationary tablets and desktops in that zone, which may not be what you want.

The ideal system would allow advertisers to pass Google a discrete bid for each combination of contextual cues, but that gets to be a great many discrete bids in a big hurry and would shatter the stated goal of simplicity.

Understanding the location, the relative position, and the user’s context will help advertisers, with the largest, most sophisticated enterprise firms benefiting the most. The benefits will flow to users in more targeted, and therefore, more useful ads; to Google, in more revenue; and to the advertisers who can take advantage in growing their program, in profitability. Moreover, Google can share all three without breaching trust with its users or sharing personally identifiable information.

Who’ll join the call for more data?