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.
Over the course of 30+ years we’ve bought over a half-billion dollars of traditional media. We’ve bought for everyone from mom-and-pops to Fortune 100 companies; Mobile homes to funeral homes; BMW’s to Buicks; fast food to steakhouses. We are fluent with the typical media buying tools and licenses from Strata and SQAD to Nielsen and Scarborough.
Negotiation and placement. Ad production. Research. Strategic planning. Whatever your need, we’ve got the team to handle it.
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…
of your five neighbors, how many of them have the exact same age range, marital status, income level, net worth, number of children, ethnicity and education level… as your household? When you send direct mail to a ZIP code, block code… or even carrier route level, you are spraying and praying! Kristalytics helps you target to the household level.
Since we have the entire 253 million U.S. Consumer file in-house, we’re able to generate targeted mailing lists for all our campaigns. We also can match the respondents from any mail campaign back to the mail file far more accurately than typical tools.
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.
A 100% impression-share strategy forces our campaigns to be profitable. Otherwise, they could not sustain the resulting high volume of clicks and costs.
We achieve profitability by starting our analytics at the impression and geography level. Analyzing millions of impressions that create thousands of clicks and hundreds of conversions requires very robust systems and software. Excel spreadsheet won’t cut it.
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.
We create a very large universe of emails associated with people most likely to buy… and then upload that file to AdWords, Display, Facebook, or our Mobile platform. The matches then become the marketing universe for all digital campaigns. This not only prevents a lot of unqualified traffic, but it also almost entirely stops bot fraud.
We remarket to people differently based on their on-site behavior and exclude those that have already signed up or become customers. Then, we use a time-based persuasion sequence to nurture the prospects over time by changing the messaging in the remarketing ads over time and, potentially, the landing pages as well. This allows us to identify the emotional trigger that is going to close the sale for every individual prospect.
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.
The more you know about individuals on your email list, the better you can communicate with them, and the more likely they’ll open and read your message. Previously, an email address was a generic, faceless piece of data. We turn that data into a real person you can talk to.
We have the resources to match an email address to a physical street address up to 65% of the time. And the longer someone has been on your list, the better that percentage gets. Why is this important? When you convert an email to a physical street address, there is an enormous amount of targeted information we can learn even if you never intend to use postal mail.
Mobile device advertising allows us to utilize our predictive models to hyper-target individuals by device and by geography. Here are some examples:
- Send out ads for a new medical device to attendees of an industry trade show held in a convention center.
- Collect the device ID of a ski resort visitor in order to implement a year-round nurturing campaign once they’ve returned home.
- A store may have many competitors within a shopping mall. You could target smartphone users from your target audience who enter a tight radius of your store… OR a competitor’s.
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.
Sometimes advertising is the problem—a store is not well known in the area. Or, it could be a marketing problem—a store has different psychographic targets around its location than is usual, so the creative message requires adjustment. Perhaps the problem is cannibalization—a store finds itself with much of the target psychographic within its trade area closer to another store. There are, of course, any number of other possibilities, but these are examples of what we typically find.