5 Easy Steps to Turn Third-Party Marketing Data Into Great First-Party Data

Cleaning data

While third-party data can add tremendous value for any company, most buyers of this data don’t take the necessary steps to get the most out of it, thus leading to a low return on investment, discontent with the data quality, and churning through different providers year after year. This article will explore five easy steps to turn third-party data into valuable first-party data.

Just so we are on the same page, let’s define first-party vs. third-party data.

First Party Data: This is data from your marketing, sales, support, and product databases. This data has your business context.

Third Party Data: This is data you can acquire from a third-party, typically a data provider. This can include, people, company, intent, device, and any other data that may be of value to your sales and marketing efforts.

In the last few years, there has been an explosion in the number of marketing and sales data providers. The variety runs the spectrum of:

List/database providers
Web crawler / “real-time” search
Predictive analytics
Account-based “whatever”

The last two categories of providers usually don’t build their own data from scratch, but instead, acquire data from the first two categories of data providers and add values to it such as correlation and segmentation.

The typical consumption model looks like a combination of the following:

  • Sales reps can buy a lead from inside their CRM tool ad-hoc
  • Marketing team buys a list that fits a profile and loads the data
  • A predictive or ABM solution that suggests leads and companies that look promising based on profiling or activity
  • Third-party data are stored as custom data fields in their sales or marketing automation platform
  • The sales team may look at these custom data fields if the primary ones don’t work
  • Marketing automation platforms essentially ignores these custom fields
  • Multiple sets of data are accumulated creating discrepancies
  • Data is not refreshed or updated for a long period of time, if ever

This approach is not exactly the formula for success, let alone the best way to spend your precious marketing budget. While data quality does vary among providers, how you consume third-party data has more impact on the overall success you will have with the data. To get the maximum value from your third-party data budget, follow these five simples steps.

1.   Clean Before Enrich

If you’re disappointed with your data provider’s match rate, there are two root causes:

The provider does not have that data record

The provider has that data record, but its matching algorithm cannot find the record based on the data you provided

The root cause is frequently a matching issue rather than not having the data. I’ve worked with many data providers and have seen matching algorithm performance ranging from excellent (>80%) to atrocious (<30%). One thing I have found is, if you pre-clean your data before sending it to the third-party for enrichment, you can improve your match rate by 100% to 300%. Simple pre-cleaning should involve:

Removing bad data (e.g., “N/A,” “retired,” “not provided,” “555-1212”)
Standardizing data (e.g., “United States,” “USA,” “U.S.,” “United States of America”)
Filling in blanks (e.g., “California, no country,” “San Francisco, California, no ZIP code”)
Resolving inconsistencies (e.g., “California, Germany,” “city = California”)

2.  Optimize Data Format and Standard

Given data providers live and breath data, you probably figure that you should be able to send them data in any format and standard and they should be able to consume it. Unfortunately, that isn’t the reality. Data providers’ ability to deal with dirty source data varies greatly. Discuss with your data provider how best to deliver your data to maximize match rate. Make sure you cover these three areas:

Format: For example, should you send first name and last name as separate data fields or as one name field? If you send it as one name field, should you include the middle name or middle initial? Don’t think this should matter? It does if want a high match rate.

Standard: Is Puerto Rico a state or a country in your database? How about Scotland? Should “Greater London” be the county, the province, or the urban area? Is it “South Korea,” “Republic of Korea,” or “Korea, Republic of”? Find out which data standards your provider can support and deliver your data in a supported standard.

More or Less: Some matching algorithms do better the more data you can provide as input. Other matching algorithms get confused the more data you send. The best approach is to get guidance from your data provider about which combinations of input data produces the best results.

3.  Standardize and Segment Post Enrichment

If you implement the first two recommendations, you will have a much higher match rate. Now that you have gotten back all this great data, more work is needed to maximize its value.

Standardize: Your data provider may not send data back to you in the exact format or standard that matches your data standard. Just like you would benefit from transforming your output data to match the data provider’s supported standards, you need to convert the data you receive back into your standard. If this data must go into multiple systems, then you may have to transform the third-party data into multiple standards and formats.

Segment: Even the best-fit third-party data you can buy is still commodity data. If you can buy it, then your competitors can buy it as well. Raw data from a third-party provider needs to be segmented to support your data standards and go-to-market requirements. Examples include using job title to derive job function, job level, and buyer persona. Another common example is turning annual revenue and employee count into company size. The “standard” data from your provider is minimally useful until you segment it and make it your own.

4.  Reconcile Immediately

Data is not like whiskey. It doesn’t get better with age. In fact, it starts to stink after a few months. Storing third-party data in custom data fields without reconciliation is like buying and storing white wine to drink five years later. It just doesn’t make sense. The only data set that matters to your operations is your first-party primary data set in your CRM platform. Rationalize, reconcile, and enrich your primary data with the third-party data as soon as you acquire it. Automate the business logic of how to enrich your primary data with any third-party data. Automate the decision of what to keep, throw away, merge, and overwrite. This business logic should take into consideration time, the source of data, and the business processes supported by the data. No business logic is ever going to be 100% correct, but reconciling new third-party data in an automated manner with a consistent logic will always outperform a simple buy-and-forget strategy.

5.  Buy With Purpose

Third-party data is not cheap, especially high-quality data. I have yet to meet a marketing team with an unlimited data budget, thus, it pays to build and automate a data enrichment strategy that consists of:

What data to buy
From which vendors to buy it from
When and how frequently to buy or refresh

It is rarely worthwhile to dump out your entire marketing database every six months to get it refreshed. Doing so will cost you a fortune and will have a low impact on the business. Put together a strategy so you can get just the data you need for the correct part of your database at the right time. For example, with a small budget, the best place to consider starting at is by adding enrichment as part of a list loading process and limiting records that are lacking a few critical data fields like job title, phone number, email, and company name. A data recovery strategy may focus on only records that have not been touched for more than six months within target accounts.

A good data enrichment strategy is one with a clearly stated purpose. Which business processes is it trying to improve (e.g. attribution)? Which stakeholder’s life is it supposed to make better (e.g., inside sales not having to wade through operators and phone trees)? How will the ROI be measured (e.g., email bounce rate or lead routing accuracy)?

Make Your Third-Party Data Investment Worthwhile

An alternative option is to select some of these following strategies, which may work for some investors, but definitely, don’t work for marketers looking to leverage third-party data effectively and economically:

Buy and forget
Spray and pray
Buy the market

Conclusion

By following the five simple recommendations above, any marketer can instantly improve the return on investment of their data acquisition budget and gain a competitive advantage in a world where access to data has become a commodity.

Also Read: Four Keys to Unlocking the Power of Predictive Sales

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