It all begins with Research & Analytics

Know your trade area, know your customer base, and leverage the differences.

Research and analytics drive every marketing and site selection strategy we develop. Insights at the level we need for developing the highest producing advertising campaigns and choosing the best retail locations can only come from having the entire U.S. Consumer File of 235 million individuals in-house. This allows us to query, filter, and summarize the landscape for any trade area and compare it to any customer base. Integrating big data analytics into actionable strategy is our departure point for Traditional Media, Digital Media and Real Estate Site  Selection. Or, put another way: Know your trade area, know your customer base, and leverage the differences.

These channels often generate many targeted leads with a much larger universe than can cost-effectively be reached through online channels. We understand Reach, Frequency, Effective Reach, GRP’s, GIMPS, TRP’s, CPM’s, CPP’s, DEC, and a host of other acronyms that most folks don’t want to see cluttering any sentence.

Once we’ve identified your best customers, we can find the rest of them… at a household level. Why is this important? Here’s a quick test: Walk outside your front door. Look at the house across the street from you. Then, the one on the left of it, and the one on the right. Now, look at your neighbor to the left, and then right. If you did this correctly, you just looked at five different addresses. Now ask yourself this…

Paid Search: Our goal is 100% impression-share for all profitable exact-match key words within the metros that comprise a client’s trade area. This means that the ad is shown 100% of the time for an exact match key word within a given geographic target. By contrast, most local campaigns struggle to achieve a 25% impression-share, meaning that they are not seen 75% of the time when someone searches for that key word. Even national campaigns often struggle to get above a 50% impression-share.

Display Ads & Remarketing: By limiting the universe to only people who are assigned high scores by our predictive model, our display campaigns are typically more efficient than those that rely on any external or third-party data to drive targeting.

Email: According to Microsoft, 97% of all email traffic is spam. And that number is growing. Many marketers make the mistake of thinking email is free, so it doesn’t matter what you send out to people. But list fatigue, fake email addresses and the good ‘ol delete button are pitted against you in the fight for your customer’s attention.

Mobile device advertising allows us to utilize our predictive models to hyper-target individuals by device and by geography. Here are some examples:

Why does one existing location soar while a second “identical” location struggles? Both may show similar population density and demographics, but we dig deeper, considering competitive issues, drive time, daytime population, residential population, market cannibalization and more. More importantly, we can leverage these differences to offer a clear, actionable advertising and marketing path to maximize profitability for both stores. This goes way beyond a typical approach of, for example, targeting age 25-54 adults with children and $75K income.