The average person is exposed to thousands of ads each day. Naturally, then, you probably assume that marketing and advertising is simply the act – or art – of telling anyone and everyone about your product or service. In reality, it doesn’t quite work like that.

The most successful marketing campaigns hit their marks by leveraging the power of data to fine-tune who sees their ads. The very best of these marketing campaigns use data modeling to make it happen. Read on to learn more about what data modeling is in marketing – and how you can harness it to improve your reach.

What is Data Modeling in Marketing?

Data modeling has a somewhat different definition in marketing than in other fields. Whereas other sectors may define data modeling as the visualization and presentation of data, data modeling in marketing refers to extrapolating one data set to another.

It means using trends in one dataset to predict the likelihood of trends in another dataset.

This process becomes essential when figuring out whom to target with your campaigns. Sure, you could go the blanket approach and promote yourself to everyone you can reach.

But reaching someone doesn’t always mean getting them to pay attention – and it sure doesn’t always mean using your money well. Data modeling increases the likelihood that the audience you reach will pay attention – and, more importantly, have a pre-disposition to buy what you’re selling

How Does Data Modeling Work?

It might be easiest to explain data modeling when compared to the typical lead list to one generated via modeling. A lead list generated without data modeling likely comprises an overwhelming number of contacts in hopes that some of them engage with you.

In reality, only a fraction of the people on such lists are interested in responding or buying. Plus, since there’s nothing particularly special or challenging about assembling this list, it’s likely sold and resold to your competitors several times.

Data modeling, on the other hand, is specific to your product or service –

and in its more advanced forms, it can even be specified to a certain kind of advertisement! It takes a dataset directly relevant to your company and extrapolates it to another dataset.

For example, let’s say you’d like to conduct a direct mail campaign advertising solar panels only to homeowners who earn a certain income, that are also in your area – and also are frustrated with high energy prices.

In that case, you’d identify trends in your customer base’s age, location, income, and hundreds of other key data points. This data includes behavioral patterns, past purchases of certain products, and even seemingly off-the-wall attributes such as if your customers are likely to be coin collectors or pet owners. You’d then identify all the prospects in your area whose data aligns with these trends.

Match Customer Data

Notably, only a portion of prospects will best match your existing customer base in comparison to the available data in your service area. In general, 80% of the person who meets specific criteria such as age or ZIP code will have absolutely no interest in the product you’re selling.

In the example above, even if you were to target homeowners in a given area, the amount of electricity used by one homeowner vs another can vary wildly. Picking one address over another and eliminating the addresses that have no use for your product is critically important when it comes to spending your marketing dollars wisely.

This also leads to improved sales closing rates, as your sales representatives will spend a lot less time talking to people who are not a good fit for the products they are selling.

Less Clutter, Better Sales

You may think that shrinking available prospect lists with data modeling would result in fewer leads. Although this is theoretically possible, the fact is that the vast majority of closable sales are within the top 20% of most likely matches in a given area. While the remainder are among the remaining 80% of prospects where response rates tend to drop by 60% or more.

It’s generally not worth chasing those prospects as your cost-of-acquisition exceeds the amount of revenue you can generate from them. 

Essentially, you roughly get the same amount of leads and sales but only send out 1/5 of the advertisements you would have had to without modeling. You will also end up fielding fewer “dead end” leads which improves your sales agents’ morale and makes the leads themselves more valuable. 

Imagine getting roughly the same amount of leads, spending much less than you are spending today, and having a higher closing ratio.

It may seem hard for the grizzled marketing veteran to wrap their head around, but if you think about it as eliminating waste from chasing after prospects who have no interest in your product and only beaming your message to highly interested people it starts to make a lot more sense.

What Kinds of Companies Benefit from Data Modeling?

In theory, any company can benefit from data modeling. That said, modeling is especially useful in direct mail marketing since, unlike online ads, there is an intense amount of waste when going after prospects who aren’t likely buyers.

Postage alone eats up 2/3 of your cost, and when you factor in production, materials, and the time it takes to turn around a mail drop into a response, the thought of all that wasted time and money is enough to keep your average finance department up all night.

And if you’re even remotely environmentally conscious (as we are at DK Solutions), anything you can do to reduce the amount of paper wasted is a net benefit.

It follows, then, that the types of companies that most often use direct mail marketing most benefit from data modeling. 

These types of companies often include final expense insurance, hearing aid dispensaries, Medicare supplement insurance, and various senior needs companies. Other companies, especially those in the home services such as solar, home remodeling, and tv/internet providers are increasingly turning to this form of advertising as well.

Standalone agents or dealers who sell these products benefit from data modeling in the same ways as do Fortune 500 companies. And agents and companies alike benefit from having a direct mail marketing partner capable of data modeling, lead generation, and end-to-end campaign execution.

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