Enriched marketing data conceals excellent potential for the strategic development of B2B businesses, enabling, among others:
- personalized marketing and sales
- targeted advertising
- customized messages and offers
- and all improved communication, engagement, and lead generation resulting from that.
Enriched data also ensures greater flexibility for go-to-market (GTM) teams through improved micro-segmentation. For instance, insights from firmographic attributes, such as quarterly increases in new hires, can identify fast-growing companies interested in onboarding solutions or collaborative work automation.
However, many business owners fail to capitalize on this potential due to haphazardly implemented practices and misalignment of data enrichment strategies with strategic business goals.
Let’s find out what common pitfalls you should avoid to excel in B2B data enrichment.
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What Hinders Data Enrichment Adoption for Most Companies
Even though many marketing teams have invested in B2B data enrichment, not all succeed. Several factors can impede or derail the efficiency of data enrichment efforts. Take note of these potential pitfalls to avoid wasting your marketing budget.
Lack of Data Coherence
First and foremost, it is vital to overcome data silos. Companies must ensure that customer, product, and sales data are well-organized and integrated. Coherent data management underpins the effective use of enriched data and fosters true marketing-sales alignment.
Lack of a Strategic Approach
Clear, defined goals and strategic planning are essential to make data enrichment worthwhile. Without specific objectives – be it increasing the number of MQLs/SQLs, improving retention rates, or enhancing customer satisfaction – you won’t achieve your core business goals.
Obsolete Data Collection Techniques
Relying on basic data points is no longer sufficient for excelling in targeted advertising and sales. Modern enrichment techniques involve data profiling and modeling, integration of third-party data sources, and continuous optimization of data collection methods.
Lack of External Data Expertise
Understanding which specific data points add value to your customer data and marketing strategy is crucial. When evaluating data samples, consider:
- Data Breadth (the number of attributes per record)
- Data Depth (the coverage of a certain attribute)
- Data Quality (the relevance, accuracy, and up-to-dateness of the data).
By addressing these common challenges, companies can enhance their data enrichment efforts and achieve better marketing outcomes.
4 Steps to Prepare for Enriching Your Customer Data
Step 1. Establish Shared Metrics and Tools for Marketers and Sales Teams
Eliminate the discord between Sales and Marketing datasets to create a holistic view of the buyer’s journey. This improved visibility and trackability will enable both teams to identify key metrics that influence overall revenue growth.
Start by sharing CRM data and providing access to marketing/sales automation platforms. However, remember to avoid information overload and focus on information people can apply within their established roles and routines. For example, marketing teams should focus on data that enhances audience targeting – data points supporting identity resolution and targeted outreach. Sales teams, meanwhile, should prioritize data that improves personalized outreach and reveals buying behavior.
Both teams should center their data-driven collaboration on the Ideal Customer Profile (ICP) and establish ongoing feedback to double down on high-potential leads.
Step 2. Set SMART Goals and Practice Goal Prioritization
SMART stands for Specific, Measurable, Achievable, Relevant, and Time-bound. This simple and practical framework helps you define and achieve Sales/Marketing goals within specific timeframes.
Choosing the right prioritization framework is mainly individual, but you can always fine-tune your current approach based on progress and achievements.
A Value/Effort Matrix is a straightforward prioritization tool, grouping goals within a 2×2 matrix bordered by two axes: value and effort. Value can be defined as business value (revenue gains) or audience value (awareness, engagement, loyalty).
The PIE framework expands on the Value/Effort Matrix by breaking value down into Potential, Importance, and Ease, offering further enhancement of your prioritization method.
Step 3. Rebuild Data Collection
Cutting-edge AI technologies have transformed the martech space, with immense predictive power and the ability to analyze behavioral patterns. Today’s social listening tools are a prime example of how machine learning platforms can anticipate customer behavior and sentiment. Owning this behavioral data is becoming the norm for forward-thinking, growth-oriented marketers.
Additionally, the looming phase-out of third-party cookies continues to impact digital marketers. Although Google delayed the cookie phase-out earlier this summer, it nudged the community to explore alternative data collection methods. Experts emphasize the need to streamline the collection of consented first-party data and join smart data alliances.
Step 4. Adopt External Data Purposefully
Data enrichment should add value to your existing customer datasets and drive conversions. Conduct a thorough audit of your datasets before scouting B2B data providers.
What Should Your Data Blueprint Include?
Let’s assume you have four specific data assets that fuel your marketing/sales activities:
- Prospect lists for initial engagement, containing general identifiers like name, company, industry, location, and work emails.
- Firmographic data points to qualify inbound leads. Attributes like headcount and revenue size or change are crucial for lead segmentation and prioritization.
- Technographic data, including tech stack, funding status, and social platform activity, is essential for personalized outreach. These data points help you address customer needs directly through email proposals and hyper-targeted ads.
- Direct contact details. Sales reps thrive on verified phone numbers and emails. Having both personal and work contacts for prospects is invaluable for matching list-based audiences with social media accounts – a key element of successful paid social campaigns.
These are the basic types of customer data go-to-market teams should gather. Investigate and document any missing fields and clarify the data coverage you need. This process prevents information overload and ensures you’re not swamped with irrelevant data that adds no business value or marketing impact.
Remember, irrelevant or unusable data will only inflate costs. Focus on data enrichment essentials, starting with your raw ICP, and refine it through testing and experimentation.
Dig into the Essentials of Third-Party Data
Now that you’ve mapped out the B2B data you need, carefully evaluate potential providers. Consider these essentials:
- How much data do they have on ICP-aligned audiences? How complete is their data?
Identify the specific data you need to enrich your CRM lists. For instance, if you plan to add ad network identifiers, ensure the data vendor includes these in their exports. Start with a smaller set of attributes and gradually extend it as your ICP becomes more defined.
- How accurate is their data?
Request data samples from vendors and inquire about their data collection and verification processes.
Always verify the accuracy of the provider’s contact data by cross-referencing it with a company already in your CRM database, where you’re certain the phone number or email is accurate.
- How fresh is their data?
Do they regularly validate contact details? Which partners supply emails/phone numbers?
- How much would it cost?
Determine the target data ROI you aim to achieve with new data sources. Check if there’s an option to customize your subscription plan to avoid paying for unnecessary features.
- What are the Terms of Use?
Opt for unrestricted use conditions. This flexibility allows you to export sourced data to other marketing automation platforms, merge it with first-party data sets, and share it internally and externally.
By considering these evaluation criteria, you can make a well-informed decision, ensuring a smooth and result-oriented experience with data enrichment integrations.
A pro tip: Most likely, you won’t find an ideal B2B data provider that covers all your needs with the required data breadth, accuracy, freshness, сost, and conditions. So prioritize these aspects and select which is more important for you. And don’t forget to schedule regular reviews to ensure that the selected data provider still meets your needs:
- Your ICP can evolve
- The accuracy or freshness of data might have changed
- There could have appeared a better option.
At some point, if your data needs grow, you might want to consider switching from a single data provider to a combination of 2 or more – or moving on to a specialized B2B audience generation platform. Consider the latter for building enriched hyper-specific audiences and syncing with your CRM and paid social platforms for ads.
The Hidden Potential of Data Enrichment
An effective data enrichment will help you unlock the hidden potential of your B2B data:
- More Complete and Actionable Data. Assess your current data set and identify the missing data points needed to align it with your Ideal Customer Profile (ICP). By doing so, you’ll uncover gaps or biases that prevent you from having a holistic view of individual customers. Richer data will help you identify the most promising marketing and sales opportunities.
- More Granular Micro-Segmentation. Enriching customer data with essential attributes enables more granular segmentation. This allows for precise targeting of audience segments, which is crucial if your offers vary by industry, company size, or technology used by customers. Micro-segmentation helps sales reps create more personalized offers, resulting in a higher number of closed deals.
- Identification of Growth Opportunities. Relying solely on internal data can limit business growth. Conversely, fresh and constantly updated third-party data helps you monitor changes in customer behavior and market trends. Data enrichment can reveal adjacent market segments to explore or effective ways to optimize your product portfolio.
