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The new B2B buying journey has significantly shifted how people think of the role of traditional marketing attribution. Data privacy and dark funnels have made it impossible to simply log into your attribution tool and uncover insights into the complete customer journey.

To fill the gap, marketers today turn to signal data.

Definition of Signal Data

Signal data refers to a user’s digital interactions across different platforms, devices, and applications. Each interaction generates a data point that can inform you about a user’s behavior, preferences, and intent. 

Unlike marketing attribution, which attempts to align a particular channel to a conversion or closed-won, signal data focuses on the activities the buyer takes to reach that conversion point. By offering an extended view, signal data provides go-to-market teams with actionable insights into user behavior, preferences, and intent.

As the B2B field becomes more saturated and digital noise continues to increase, many marketers have struggled to uncover and interpret this data. To avoid getting lost in the void, we’ve outlined signal data and how to use it to decipher your customer’s buying journey more effectively. 

Understanding Signal Data

Like most data today, signal data has no single source. It can come from a variety of sources, and those sources and the KPIs you capture can uncover different interactions within the buying journey. 

Common Types of B2B Signal Data

  • Clicks: Data generated when users click on links, buttons, ads, or other interactive elements on websites, emails, or social posts.
  • Page Views: Information about the pages or content viewed by users on websites, blogs, or other online platforms.
  • Conversions: Data indicating when users take desired actions, such as signing up for a newsletter or filling out a form.
  • Engagement Metrics: Metrics such as likes, shares, and comments on social media platforms reflect user engagement with content.
  • Search Queries: Data generated when users enter search terms into search engines, providing insights into their interests and intent.
  • Location Data: Information about users’ geographic locations, derived from GPS coordinates, IP addresses, or other location-tracking technologies.
  • Event Triggers: Data indicating specific events or triggers, such as time-based events, user interactions, or system events, which can be used to trigger automated actions or personalized experiences.

Examples of Signal Data Sources

Signal data can be captured and analyzed from a variety of sources. Some common B2B signal data sources include:

  1. Google Analytics
  2. Social Media Platforms
  3. Email Marketing Platforms
  4. Ad Platforms
  5. CRMs
  6. Marketing Attribution Platforms

To dig into additional sources of data, head over to our Guide to B2B Sales and Marketing Data Sources.

The Importance of Signal Data in Go-to-Market Strategies

Signal data can help organizations understand their audience better. This understanding is crucial when shifting from product-lead growth to a true go-to-market strategy. At its foundation, your go-to-market strategy should be focused on creating a more predictable pipeline. And a more predictable pipeline requires you to deeply understand your customer. 

Effective usage of signal data can look like: 

  • Accessing real-time insights into customer interactions to prioritize efforts to customers that are currently active in the buying journey
  • Increased personalization and targeting based on data gathered 
  • Resource allocation based on what activities are driving the most engagement and conversions
  • Using account data to build a more predictive analytic model, allowing your teams to be more agile in responding to prospect needs and trends
  • Creating a more accurate multi-channel attribution model– enabling your team to gain a holistic view of the customer journey and adjust their efforts accordingly

The Role of Signal Data in Marketing Attribution

Before examining signal data’s role in marketing attribution, we need to define what attribution really means. 

Marketing Attribution Definition

Identify and assign credit to marketing channels, touchpoints and campaigns that contribute to a desired outcome, such as a conversion or closed-won.

Marketing attribution aims to understand how effective marketing efforts influence customer behavior throughout the buying journey.

Traditional marketing attribution models are frameworks or methodologies for assigning credit for conversions to specific touchpoints or marketing channels. Common attribution models include first-touch attribution (assigning credit to the first touchpoint), last-touch attribution (assigning credit to the final touchpoint), and multi-touch attribution (assigning credit to multiple touchpoints along the customer journey).

Impact of Signal Data on Marketing Attribution

On its own, marketing attribution cannot capture a complete picture of the buying journey, but by weaving in signal data, GTM teams can:

  1. Improve data accuracy and granularity across entire accounts
  2. Enhance their understanding of the customer journey
  3. Identify previously overlooked touchpoints that weren’t easily captured through first-party data

These core improvements allow sales and marketing teams to adjust their strategies to target more effectively. 

Challenges and Considerations

While signal data can improve attribution modeling, it’s not always enough. Jon Miller, founder of Marketo, recently likened attribution and signal data to ingredients in a larger recipe. This is from the person who pioneered attribution modeling. 

Why does Miller add an asterisk to modern data modeling? Well, the biggest culprit is tied to data privacy. More activity is happening in the dark (offline or anonymized), creating black holes within the buying journey that data points cannot capture.

When not used properly, these black holes can create just as many problems as not having any data at all, leading well-intentioned sales and marketing leaders down the wrong path.

Complications arise when signal data is siloed from existing systems, eliminating the ability to see the whole journey. Data, and especially black holes within the data, leads to more flexibility in interpretation and analysis – for better or worse.

Embracing Signal Data for Enhances Marketing Insights

Your go-to-market strategy is only as strong as your customer journey data. While signal data is not a complete solution to holes in your marketing attribution model, it is an enhancement and needs to part of your overall strategy. As data privacy and digital noise continue to throw wrenches in the ability of marketers to understand customer data, revenue operations will continue to become a critical piece of any go-to-market team.

Need help putting the pieces of your customer journey together? RenderTribe’s team of RevOps experts can help you craft the infrastructure and understand customer data better, filling in the gaps to your go-to-market strategy. 

Talk to a RevOps Strategist

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