What’s the Difference? Data Enrichment vs. Data Cleaning, and How CDPs Help

What’s the Difference? Data Enrichment vs. Data Cleaning, and How CDPs Help

Ever wonder why a campaign fizzled or a particular offer unexpectedly sent sales through the roof? It might be your data; if it’s not clean or timely, it could be costing you money and customers or causing you to miss some of your best opportunities. And if you’re not relying on accurate, real-time customer data to inform your marketing strategy, you may be missing important behaviors, needs, and demand shifts in significant segments of your audience. That’s why data cleaning and data enrichment are so important, and why AI-driven technology such as customer data platforms (CDPs) are commonly used to do a lot of the dirty work. CDPs help achieve accurate, complete customer profiles you can actually use to boost profit and ROI with the marketing technology you already have.

Data Enrichment vs. Data Cleaning

Reliable customer data helps you dig into the perceptions your customers have about your brand and make informed decisions about your marketing strategy. Many marketers now rely on customer data platforms (CDPs) to handle data cleaning and enrichment, as well as to deliver insights about customer demographics, psychographics, behaviors, and preferences. As an added advantage, they also provide metrics about what your customers see, what they click, when they click, and where they go next. With all this information, you can more easily optimize your approach to better engage your customers along their path to purchase.

There’s no shortage of customer data to dig into. But if you’re like most large sales and marketing departments, you have DMPs, a DSP, a CRM, social media managers, advertising platforms, second- and third-party data to add to your own first-party data, loyalty program information, and more.

And with so much data coming in from so many directions, things can get messy unless much of the data cleaning and data enrichment are handled automatically, in real-time. (It does you no good to know that someone was in the market for your product a month ago, especially if they already bought, say, a toaster. They probably won’t be in the market for a new one until today’s children are driving.) Here are some behind-the-scenes tasks that used to require a lot of human intervention and now can be handled by automated platforms.

What Is Data Cleaning or Data Cleansing?

Data cleaning—or data cleansing—refers to the process of ensuring your data is trustworthy, consistent, and correct. In other words, it’s the process of sifting through the large quantity of data at your disposal to find high-quality, usable information about your customers’ behaviors and motivations.

The difference between data cleaning and data enrichment is that data cleaning involves resolving discrepancies and updating or discarding old or inaccurate data, while data enrichment, which we’ll discuss more later, refers to augmenting one dataset with data from different sources, for a more complete profile.

So, for example, you might start a data project by cleaning up your first-party data to discard old data that you don’t trust anymore, data with missing fields, and all the pranksters who signed up as “Mickey Mouse.” Then, you might buy third-party data to combine with what you have, adding new fields and probably new entries as well.

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The process of data cleaning gives you an opportunity to identify any corrupt, duplicate, or inaccurate records from your customer data platform—which can be a little overwhelming unless you automate a lot of it. Here’s a good summary of some of the problems businesses typically face in data cleaning—as well as an interesting discussion on the data quality lifecycle—and how to address them, by marketing data expert Victoria Wilson of Data8.

How CDPs Help You Manage the Data Quality Lifecycle

Building a protocol and using reliable software to handle data cleaning and manage the data quality lifecycle is imperative to your success. A robust customer data platform can help to collect, sift, and unify information from multiple sources into “golden profiles” of each customer, helping you streamline your data-cleaning process so you can use the data you already have, quickly, to convert customers who are still shopping or mulling a decision. Plus, constantly incoming data is constantly associated with existing profiles, for real-time targeting and segmentation, avoiding some of the problems encountered in the data quality lifecycle. Here’s how.

How to Cleanse Your Marketing Data

To truly understand your customers’ behavior and which marketing tactics are working, you need to cleanse your data. This requires merging duplicate data, purging old leads, and cleaning up metatags and outdated organization. Here’s how data cleaning programs work:

  • Merge duplicates.

    Identifying duplicate information and consolidating it into customer profiles is a crucial step in analyzing customer data. CDPs do much of this work automatically using machine learning, and have the capacity to consolidate trillions of transactions in continuous data updates to each customer profile, even if you only have millions.

  • Purge old leads.

    You work hard generating new leads to fill your pipeline, but those leads start to decay over time. During your data cleaning process, set up your marketing solutions to use thresholds of engagement to determine the leads you need to act on. Set up triggers to re-engage if their data meets the characteristics of people who are likely to buy, just slow to make a decision, or put them on an automatic, low-maintenance touch program that costs almost nothing but might occasionally reel in new customers.

  • Clean up metatags and update your customer touches.

    Best practices for metatags have changed over time. And depending on how long your site’s been operating, some of your pages or blog posts might not have optimized metatags at all. Use current metatag best practices to clean up old tags or write new ones for those less-than-optimized pages.

What Is Data Enrichment?

With a clean slate, you can begin to fortify the new data coming through your CDP to make it more reliable and more usable. Data enrichment involves refining raw data, generating insight from data, and combining existing data sources. Here’s a closer look at data enrichment.

  • Refining raw data to make it actionable.

    Without accurate customer profiles, all of your marketing tactics are essentially a shot in the dark. CDPs distill incoming customer data into usable customer profiles. Then, they let you use these profiles to orchestrate campaigns on multiple platforms, driving your marketing strategy forward.

  • Generating insight from data.

    Although it can be easy to assume you know which steps your customers take along their path to purchase, you always want to validate your assumptions with data. For example, new CDP users are often stunned at the huge variety of customer journeys their customers take, and some of these newly discovered insights ultimately prove extremely profitable, improving customer experience, loyalty, and ultimately, customer lifetime value (CLV). Shiseido, for example, realized that sending the same email to everyone in its loyalty program produced poor results. But sending highly targeted offers and emails based on what the customer just bought was extremely productive, dramatically boosting sales and helping Shiseido to anticipate changes in customer preferences due to their ages and current fashion needs and tastes. Based on what you learn, you can alter your marketing strategy to better suit your customers’ needs.

  • Combining data sources.

    Every touchpoint across the customer journey serves a unique purpose. But measuring each touchpoint individually doesn’t paint the full picture of your customer’s behavior. By combining data sources, you can gain a unified view of each customer and develop a greater understanding of each step along their journey.

How to Get Started with Data Enrichment

Any martech you use for data enrichment needs to handle the following functions gracefully, without a lot of extra work from your own marketers, IT staff, or data scientists. The best CDPs do. And whether you buy or build your own, you’ll need these critical CDP functions. For a deep dive on handling some of the data enrichment features on the Treasure Data CDP, check out this overview on attribute enrichment, behavior enrichment, and the content affinity engine.

  1. Consolidate data on a single platform.

    To refine raw data, combine sources, and generate new insight, you need a robust CDP. The Arm Treasure Customer Data Platform helps you capture your customers’ interactions across devices, online and in-store, to deliver CLV-boosting premium customer experiences at scale.

  2. Add offline data. Data isn’t just web-based.

    The modern customer journey is omnichannel—which means it takes place everywhere. In addition to web-based metrics like browsing data and web analytics, be sure to analyze offline data sources, too, like in-store purchase history. Again, the best CDPs automatically handle different types of data feeds from hundreds of different platforms.

  3. Add third-party data.

    Different from first- and second-party data, third-party data is information collected by an entity that does not have a direct relationship with the customer. This purchased data often comes from other marketing platforms, such as a DMP provider, and it can help paint a fuller picture of your customer’s journey to purchase—especially when combined with other data sources. The key is selecting technology that automatically associates this third-party data with the first-party and second-party data you already have, correctly combining all of the data into complete customer profiles—producing thousands or millions of complete, actionable customer profiles for targeting, segmentation, and adding to CLV.

You’ll find more information on CDP technology and uses at the Customer Data Platform Institute (CDPI).

Squeaky Clean Data

CDP technology makes it easier than ever to automatically update your data, mine it for insights, and execute campaigns. You already collect so much data—so don’t let data cleaning issues or the multitude of different data enrichment sources stop you from using it to increase engagement, purchases, and CLV. Ready to dive into your data? Request a demo of our customer data platform to get started.

Micki Collart
Micki Collart
Micki is a Senior Product Marketing Manager at Treasure Data where she focuses on helping the marketing community learn about customer data platforms, their evolving capabilities and the unique approach to customer experience that Treasure Data empowers.
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