An insider’s guide to loving, not fearing, data enrichment

Plus 20 best practices to align your organization and get the most from each enrichment vendor.

Data enrichment has long been among the most important and valuable tools for sales and marketing teams. Yet, so much about enrichment remains shrouded in mystery and doubt. Why this is the case has much to do with the enrichment industry itself and its traditionally high financial and administrative cost, poor overall results, and infamous lack of transparency.

As a pioneer of multi-vendor data enrichment and partner to the industry’s best-in-class data providers, we’ve heard the horror stories and understand the apprehension. But, we’ve also seen the other side and have worked to help sales and marketers strip away the difficulty, fog, and cost from data sourcing and operationalization while delivering the measurable data improvements businesses like yours need.

If we’ve learned anything from our experience, it’s that the best way to remove the risk and uncertainty from enrichment is through the combination of knowledge and planning. In this article, we’ll help get you started with both and pass on some of the more valuable tips, tricks, and best practices we’ve developed through our work with enterprise businesses.

Mythbusters: Data enrichment edition

To kick things off, let’s quickly debunk two of the biggest misconceptions about data enrichment: one, that it’s only for B2B companies, and two, that it isn’t necessary if you already have a data capture strategy. To understand why these aren’t true, you need to consider what data enrichment can do and all the downstream activities and processes it affects.

At its core, data enrichment is about improving data quality. That means making your data cleaner as well as more accurate, recent, robust, and relevant. Data quality impacts everything from the customer experience and sales/marketing efficiency to analytics, prospect/account targeting, attribution modeling, lead-to-account matching, and enterprise territory management (ETM). These processes transcend business type and apply to both consumer and commercial use cases. Regardless of who you serve, think of all the things you could accomplish or the problems you could solve if you just had data that was consistently clean, contextually rich, and more relevant. With data you can trust and take timely action on, your go-to-market (GTM) team could act more quickly and make the right decisions.

Still skeptical? Let’s run through a quick data capture scenario to prove the point. Anyone reading this post has filled out at least one web form. We can all agree they’re a chore, with or without autofill or progressive profiling. Web forms, especially on the B2B side, are often asked to carry a heavy load, providing crucial data that informs both the individual and associated account records. Even under the best circumstances, do you trust your visitors to be 100% accurate about their company’s demographics, like employee size, industry, or even reporting office? Throw in the demographic chaos created by the recent remote-hybrid work reality, and that’s a lot of responsibility and trust to place in a single individual who is still, after all, just a random web visitor.

Why enrichment is your organization’s data quality safety net

Put simply, enrichment is your organization’s data quality safety net. It can:

• provide your GTM team with historical and contextual data to improve the customer experience in real-time;
• help sales to better understand company structure and hierarchy for optimal lead-to-account matching and ETM performance and efficiency;
• give the whole organization better, more accurate, and robust data for targeting, reporting, analytics, and decision-making.

Now that we’ve established why data enrichment is worth the investment, let’s look at some of the internal probing and planning you can do to get the most from it.

8 ways to align your organization on data enrichment

As a provider and consumer of data enrichment services, we’re always asking our customers to think outside of their own “box” when it comes to enrichment. While your marketing and sales technology may be fragmented and siloed, your customer data belongs to both. So, whatever you do to enrich, it should benefit as much of the organization as possible. With that in mind, we always circle back to the following best practices whenever thinking about or planning a new data enrichment project or program.

1. Determine your primary enrichment goal
   Is your goal to enrich existing accounts, expand your total addressable market (TAM), or something else? This gets to the heart of your enrichment strategy and will greatly impact your vendor search, as well as the data you send to and receive from them.
2. Limit the fields to only what you need
   Even the most mature data organizations have limits on the amount of information they can use. Many vendors return 300 or more fields, but the most anyone really needs is 15 to 20. Anything more is just clutter.
3. Compile a list of data and requirements necessary for forecasting and reporting
   Every vendor processes data differently. Take employee size. Some vendors may return a range, while others, the raw number or their estimate. Find out what each team requires in advance so you can tailor your search and/or integrations accordingly.
4. Give yourself extra time to plan and source if you’re buying historical data
   Some teams use enrichment to help track trends, which require backfilling specific fields. Over time, this can significantly increase the size of your database or lock you into a particular provider to maintain continuity.
5. Make a list of all the specialty data you’ll need
   Some teams need specific information that can only be provided by the U.S. Department of Labor or other niche source.
6. Decide when and how often you plan to enrich
   With data decaying at a yearly rate of 30% or greater, deciding when to update your records is becoming increasingly important. Do you intend to do it once on intake, in batches, or on a recurring real-time basis? Will enrichment take place before or after your lead-to-account matching? These questions are important for both vendor search and selection, as well as for configuring your data processes.
7. Create a budget
   Every project has a finite budget, but having a hard number in advance will shape not only your vendor search but also potentially which data gets enriched and how often.
8. Make sure you have the right infrastructure in place
   Whether importing one or 300 fields, you still need a way to ingest, process, and store the data. Cleaning, standardizing, and unifying are essential. Storage will be required if you intend to bring in more fields than you’re currently using.

Once your organization is aligned on the requirements and strategy, you can begin your enrichment vendor search and selection. If you’re like most businesses, you’re going to need more than one to meet all your needs and use cases. But, proceed with caution. Executing a true multi-vendor data enrichment strategy is not a DIY project.

9 best practices for sourcing multiple enrichment vendors

Selecting, integrating, and managing a single data enrichment provider is challenging enough by itself. Add in one, two, three or even more vendors and the difficulties multiply exponentially. Doing it yourself is not for the faint of heart unless you have limitless resources. Absent a dedicated platform, you’ll need hundreds of connectors, a stack of automated cleansing, standardization, and validation services, built-in reporting and analytics, and a team of in-house experts to help you set up, monitor, and optimize the results. If you are considering going it alone, you’ll need to know exactly what you’re getting into. Follow these best practices to understand exactly what each vendor is offering and where every dollar is going – and avoid costly surprises once the contracts are signed. If you decide to use a multi-vendor enrichment platform instead, the list can also serve as a checklist to help you evaluate the solutions and sources they provide.

1. Get a look at each vendor’s data dictionary
   Data enrichment spans multiple dimensions, and no single vendor specializes in them all. Ask to see their data dictionary, which gives you access to the fields covered and their formatting standards.
2. Only pay for the access level you need
   Many vendors offer tiers or levels of access. Avoid paying for multiple fields or hierarchical data you won’t use.
3. Skip vendors that don’t regularly update their data
   Some data, like individual profiles, go stale faster than others. Make sure the updates meet the industry standard for the specific data type and dimension.
4. Don’t buy data you can get for free
   Regardless of what their marketing says, most vendors use a mix of public and proprietary sources for their data. Find out where and how each vendor sources its data. Make sure you’re not paying for something that you can get for free or elsewhere from a better, more reliable source.
5. Avoid paying for calls that return empty or suspect matches
   Better vendors have thresholds or confidence levels that have to be met before a match is returned.
6. Don’t get charged twice for accessing the same data
   Many vendors allow you to access their data through both their UI and API. If they track each separately, make sure you’re not charged for accessing the same data twice.
7. Understand the billing cycle and how often you can access or update data
   Vendor credit billing varies greatly by use case and dimension. For example, validity vendors will typically charge you for each call or update, while others will allow you unlimited or a fixed number of updates over a specific period.
8. Don’t get double-charged for duplicates
   Even with a deduplication process in place, duplicates are inevitable. Partner with a vendor that will help you eliminate duplicates and not charge you twice for the error.
9. Be conservative when buying credits
   With most vendors returning a ~50% match rate, it’s easy to end up with a third or more unused credits at the end of the contract. The better strategy is to start with fewer credits and buy more as you need them. However, if you are stuck with unused credits, it’s good to know whether you can roll them over when you renew.

Saving the best for last: top 3 data enrichment best practices

Like anything with complexity, data enrichment requires experience and a fair bit of art and science to master. Having a plan and a solid grasp of what to look for is a good way to start. Vendors are neither your friend nor enemy but they can be your partners, especially if you have advanced use cases that require custom integration and configuration. Here are a few final best practices to consider to help you get the most from your multi-vendor data enrichment partnership–and any data enrichment project.

Start with cleansing and standardization to ensure data quality
Openprise was founded by a “mad” data scientist who cooked up a set of building blocks to establish and maintain high data quality standards organization-wide. Before any CRM (or system of record) data is updated, it is cleaned, standardized, enriched, segmented, and graded. This assembly line approach, which also can include routing and territory assignment, ensures not only maximum match rates but also timely distribution of the right data to the right people and teams.

Employ a waterfall approach to maximize match rate and budget

Different data vendors return different results. The waterfall approach uses a succession of vendors (moving from general to increasingly specialized) to fill in the maximum number of fields possible. Openprise pioneered the multi-vendor enrichment waterfall model, now becoming an industry standard, which delivers a 40% or greater match rate than a single vendor enrichment approach, without the need to buy and orchestrate third-party data from individual vendors.

Track and test everything to quantify and ensure success

With the commoditization of data enrichment, most vendors are focused on maximizing the contract, upselling, or both, and provide little in the way of metrics and transparency. As a result,  data enrichment has historically been difficult to quantify and thus defend. So, it’s up to you to track and analyze field match rate, completeness, and recency to keep your vendors honest and ensure you get the most from each enrichment dollar. In contrast, Openprise has instrumented several platform metrics and analytics to help customers understand and measure program performance and ROI.

Want to learn more about multi-vendor enrichment or Openprise’s approach? Download the guide to buyer-centric enrichment or visit our multi-vendor solution page today.

Recommended Resources