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HomeBlogMarketing Analytics3 Proven Ways to Get Great Results from Not-So-Great Data

3 Proven Ways to Get Great Results from Not-So-Great Data

Research many graphs.Has your house ever been such a mess that you had no idea where to even begin to clean?

From piled up dishes to rogue dust bunnies, a messy living space can be overwhelming enough to make you scrap the idea of cleaning altogether and plop down in front of the (dusty) TV instead.

For many business owners, it’s the same story when it comes to their data.

One hundred percent of our clients – not 99%, one hundred percent – come to us with fragmented data that’s often incomplete. Their data, like yours, doesn’t usually exist in one location, but multiple locations across different servers, departments, and even cities.

There are often holes in the data, missing values, and other oversights that make business owners hesitant to begin a data-driven project. After all, how can you possibly use data to increase profits and decrease your marketing spend when the data, like your house, is a big ol’ mess?

I’m going to let you in on a little marketing secret that most analytics experts don’t want you to know:

  • You don’t have to have perfect, “pristine” data in order to get great results from that data.
  • Your data doesn’t have to live in one place to be useful in your decision-making process.
  • Disorganized data can actually help you uncover missing information that turns out to be crucial to achieving your marketing goals.

In order to make the most of the data you already have, no matter how disorganized it may be, simply follow this three step process:

1. Dive In (to the data)

The great news about imperfect data is that it’s already there, ready and waiting to be uncovered, analyzed, and integrated into your marketing strategy.

Even better news is that you don’t have to uncover or analyze anything yourself! An added benefit we provide our clients is the organization, analyzation, and “tidying up” of imperfect data across multiple locations.

As we work, we begin to uncover areas of missing data as well as subpar data collection processes. This gives you a crystal-clear picture of how better data collection tools and data storage options will save you time and money (not to mention help you target customers who are ready to buy).

By diving into disorganized data, you’ll be able to pinpoint missing information crucial to your marketing success while revamping your data collection, organization, and storage options to align with your marketing goals.

2. Ask The Right Questions

Once you’ve committed to taking a good, hard look at your data, no matter its current state, it’s time to start asking the right questions.

Common questions we get from our clients include:

  • Are we sending email and direct mail to the right people (AKA the people who will show up and buy something)?
  • Why aren’t people renewing their subscription to our loyalty program?
  • How can we find targeted leads for an acquisition campaign?

Asking these types of questions provides a framework from which you can begin to approach your data more strategically. Only when you ask the right question can you begin to use data to find the answer.

3. Identify Key Assets

In order to find answers to each of the questions listed above, ask yourself this:

“What data am I missing that will help me find the answers to these questions?”

  • Are you asking for email addresses?
  • Are you capturing coupon codes at your Point Of Sale?
  • Do you ever ask a non-renewing loyalty member WHY they left?

By identifying missing data, you’ll be able to make a plan for obtaining only those key assets that are most useful to you in your marketing.

You’ll also be able to see where you’re wasting time obtaining data points that reveal nothing about customer behavior.

By taking an objective look at your data (no matter how disorganized), asking the right questions, and identifying the missing data points you need to reach your goals, you’ll be able to use the data you already have to predict customer behavior, reduce your marketing spend, and increase your revenue.

What’s more, you’ll be able to streamline your data collection and storage processes for increased efficiency in the future – which is sort of like having a house that cleans itself.

Food For Thought: Orecchiette with Pork Sugo 

The first thing you’ll want to do once your house is clean is entertain some guests.

Whether those are metaphorical guests in the form of new customer data, or literal houseguests attending a cocktail party, there’s always room for pork!

Its winter in Chicago now though still a bit warmer than usual but when the sun starts going down at 4:30 in the afternoon and the temp gets below 32 degrees during the day my food brain starts drifting toward making warm one pot meals. I haven’t made this one since 2008 but it popped into my mind recently and now I remember why I kept the recipe. Made it again recently and was so happy!

Orecchiette is that little ear shaped pasta and it is combined with a rich pork sugo (Italian for gravy) that you make by slow cooking for a couple hours – completely unattended! It will make your brains fall out! Enjoy!

What could be simpler, or more delicious?

Believe it or not, it’s just as simple to figure out which customers to market to using predictive analytics. You don’t have to understand those words and you don’t have to have a “clean house” in order to get started. Find out more by calling (312) 463-1050 or contacting us online.

Bonnie Massa is Founder and President of Chicago-based Massa & Company, Inc. She works with companies and nonprofits to make the best use of their information about customers, partners, donors and sponsors. With more than 40 years of experience in marketing and predictive analytics, Bonnie is passionate about helping clients make informed, data-driven decisions to increase the value of their customer base. She strongly believes that making pasta and ice cream from scratch are worth the effort, and she spends much of her free time testing and re-testing that theory.

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