In a recent blog post, we wrote about the importance of comprehensive data capture in B2B marketing and how to create a value exchange for buyers that would enable that data acquisition.

Once you’ve captured that data, the next step is making it useful. Collected data means nothing if you can’t act on it and use it in meaningful ways. Our whitepaper, Setting the Analytics Stage for Tomorrow’s Consumer, covers that in depth through a business-to-consumer (B2C) lens. For businesses in the B2B sector, data curation looks a bit different. The core motivations and strategies are the same, but those strategies manifest in a way that’s very specific to the B2B buyer.

Step 1: Know the Data You’re Collecting

Before you can act on data, you must know what’s available and how it’s been collected. For all the data points you’re capturing, ensure there is clear documentation showing what the data is, where it comes from, what it means, and possible values for each customer. Understanding the various you’re your data can describe a customer is imperative to creating meaningful segments.

Understand who in the organization might find the data to be meaningful. Is it a sales metric or indicator? Does marketing need the data for messaging? Grouping like data together can create efficiencies for teams trying to refine audience opportunities.

Ensure the data is intelligible. Do your systems allow for standardization within fields? The more we can limit input options for our customers, the more reliable our data is, which means less investment in smoothing or refinement before end users can put the data into action.

Step 2: Remove Barriers from Data Usage

The data we capture should be accessible by all who need to use it for business purposes. Gone are the days of requiring a team of developers or data miners to create audiences from vast arrays of customer data. Data should be structured in a way that allows end users to leverage it with minimal assistance.

In B2B, the relationship between sales and marketing is critical for creating leads and capturing sales. Both teams need different types of data readily available to them to understand lead quality, lead velocity, where customers may be getting stuck or creating objections, and what type of messaging is resonating with the consumer base. But marketing teams and sales teams have inherently different capabilities. Create self-serve data environments, like a customer relationship management (CRM) or graphical user interface (GUI)-driven customer data platform (CDP), where users can intuitively segment and analyze customer data for their own use.

To make data helpful to all, we must be cognizant of foundations for good data stewardship. This means collecting data in compliance with your privacy policy and appropriate privacy legislation, securing data with proper open and visibility permissions, and ensuring any artificial intelligence (AI) or machine learning (ML) used on the data occurs in an ethical way that provides value both to the business and the consumer.

Step 3: Build Data Sets that Add Value

Valuable data sets will look different within each sector, especially among different customer types in B2B. Think about your audience subsegments not just in terms of what makes each one unique, but how that uniqueness translates into customized needs from an activation standpoint.

For most B2B organizations, the task of lead scoring and grading requires a data set that will intrinsically add value to both the sales and marketing teams. Each organization will approach it a bit differently, but the questions that need to be answered are largely the same. For grading, what does a prospect’s company, role, budget, etc. mean in terms of potential opportunity? For scoring, what does a prospect’s activity with our site, advertising, webinars, downloads, etc. tell us about their level of engagement and propensity to buy with us?

In B2B, valuable data sets will additionally attempt to describe audiences in ways that set them up for the next sale. If your product has a cyclical purchase pattern, can we group customers that are approaching the next purchase point? Are you able to define customers that are loyal to the brand vs. those that are constantly shopping and buying based on price alone?

Step 4: Iterate on Your Data Curation

Over time, the segments you make will need to be reevaluated to ensure they are still providing value. This can be for any number of reasons:

1. Market forces are changing the way your buyers purchase. Look at inflation for a recent example of this.
2. The ways customers evaluate and research are changing. This might prompt a change from cold-call prospecting to SEO and SEM optimization for more website hits, for example.
3. Your product mix or company’s value proposition is changing. As your company adapts to an ever-changing market, your position as a solution to your customers may change, as well, which will require rethinking how you communicate with them. Many companies were forced to reckon with their position during the pandemic, which fundamentally changed how many sectors go to market and how their solutions impact their customers. 

At a minimum, if you’re using a lead scoring or grading model, it’s important to evaluate these models to ensure they are still providing the most qualified leads to your sales team as the market evolves.

Conclusion

Like we discussed for the data capture process, the key to data curation is starting with the end in mind. As you define your data curation strategy, think of how to group elements of customer data into coherent segments that provide value to the various teams in your organization. And ensure that data is intuitive and accessible, so there are no bottlenecks in the process to leverage the data.

Keep an eye out for our final installment, which will explore how to leverage thoughtfully captured and curated data to create intelligent B2B customer experiences at every touchpoint.