At a glance:
- Intent data reveals prospects’ online research behaviour, enabling early identification of potential customers.
- The three types of intent data – third-party, first-party, and predictive – offer unique insights for customer engagement.
- Implementing intent data requires careful tool selection and a tiered outreach strategy based on intent signals.
- Effective use of intent data involves combining data types, quick action, personalised approaches, and continuous learning.
Ever feel like you’re shooting in the dark when it comes to finding hot leads?
Understanding your prospects’ behavior and interests is crucial for sales success. This is where intent data comes into play. But what exactly is intent data, and why is it becoming an indispensable tool for Sales Development Representatives (SDRs)?
Table of contents
What is Intent Data, Anyway?
Imagine being able to peek over the shoulder of your potential customers as they browse the internet. That’s essentially what intent data does for you. It’s information that shows you what companies or individuals are researching online, particularly when it comes to products or services like yours.
Think of intent data as a way to spot window shoppers before they even enter your store. It tells you who’s out there, actively looking for solutions that you provide. This information comes from tracking online activities like website visits, content downloads, or specific searches.
Unlike trigger events, which are specific changes or occurrences within a company (like a new CEO or a merger), intent data is all about online behavior. It’s the digital footprints that potential customers leave behind as they research and explore their options.
By using intent data, you can reach out to prospects early in their buying journey, often before they’ve contacted any vendors. It’s like getting a head start in the race to win new customers.
The Impact of Intent Data on Sales
Intent data offers unprecedented insights into prospect behavior and interests. Let’s dive deeper into how it’s transforming various aspects of the sales journey:
Improved Lead Scoring
Traditionally, lead scoring was based on demographic data and basic engagement metrics. With intent data, we can now incorporate real-time intent signals for dynamic scoring. This results in more accurate prioritisation of leads, allowing sales teams to focus on prospects most likely to convert. For instance, a prospect researching “cloud migration strategies” might receive a higher score if you offer cloud services, ensuring that your sales efforts are directed towards the most promising opportunities.
Enhanced Account-Based Marketing (ABM)
While traditional ABM targeted accounts based on firmographic data and past interactions, intent-driven ABM identifies and prioritizes accounts showing active interest in relevant topics. This approach leads to more precise account selection, improved timing of outreach, and higher engagement rates. A case study from an IT services company showed a 3x increase in engagement rates for their ABM campaigns after implementing intent data, demonstrating its powerful impact on ABM strategies.
Personalized Outreach
Gone are the days of generic messaging based on broad segment characteristics. Intent-based approaches allow for tailoring messages to align with prospects’ demonstrated interests and research patterns. This personalization results in higher response rates, more meaningful initial conversations, and faster trust-building with prospects. For example, if intent data shows a prospect is researching “data security in finance,” your outreach can focus on your solution’s financial sector security features, immediately demonstrating relevance and expertise.
Shorter Sales Cycles
The traditional sales process often engaged prospects after they’d reached out or entered the consideration phase. Intent-driven processes, however, allow for identifying and engaging prospects earlier in their buying journey, often during the research phase. This early engagement can reduce time from initial contact to close, provide opportunities to shape the prospect’s requirements, and increase win rates by being first-to-market.
Improved Sales and Marketing Alignment
A common challenge in many organizations is the misalignment between marketing-generated leads and sales team priorities. Intent data provides a common language and shared insights for both teams, leading to more efficient lead handoff processes, better-qualified marketing-generated leads, and increased trust between sales and marketing teams. A best practice is to hold regular meetings between sales and marketing to review intent data insights and align strategies, ensuring that both teams are working cohesively towards the same goals.
Predictive Sales Forecasting
Traditional sales forecasting often relied on historical data and sales team intuition. Intent-enhanced forecasting incorporates real-time market demand signals, resulting in more accurate revenue predictions, better resource allocation, and proactive identification of market trends. For example, a sudden spike in intent signals around a specific product category could indicate a potential increase in demand, allowing sales leaders to adjust forecasts and strategies accordingly, staying ahead of market shifts.
Competitive Intelligence
Without intent data, sales teams had limited visibility into a prospect’s consideration of competitors. Intent data provides insights into which competitors’ prospects are researching, offering several strategic advantages. Sales teams can address competitor comparisons proactively, refine their competitive positioning, and even identify at-risk accounts (current customers researching competitors). Tactically, if intent data shows a prospect is researching a specific competitor, sales reps can prepare targeted comparison materials, entering conversations fully prepared to differentiate their offering.
By leveraging intent data across these areas, sales teams can operate more efficiently, engage more effectively, and ultimately drive better results. As intent data technologies continue to evolve, their impact on the sales process is likely to become even more pronounced, making it an essential tool for forward-thinking sales organizations. The key to success lies in not just having access to intent data, but in effectively integrating it into your sales strategies and processes, always with an eye towards providing value to your prospects and customers.
Why Should SDRs Care About Intent Data?
According to a study by Gartner, the average conversion rate at the top of the marketing funnel is 6%, but organizations using intent data are two times more likely to have a 10% conversion rate. These impressive statistics highlight the transformative power of intent data, offering SDRs several key advantages in their prospecting efforts:
- Early-Stage Prospecting:
Intent data helps you identify potential customers before they’ve even started evaluating vendors. - Topic-Based Targeting:
You’re able to understand the topics and solutions prospects are researching. - Competitive Intelligence:
You’re able to gain insights into which competitors your prospects might be considering based on their research patterns.
The Three Flavours of Intent Data
Intent data comes in several forms, each offering unique insights and advantages for SDRs. Let’s explore the three main types of intent data and how they can be leveraged in your sales strategies:
1. Third-Party Intent Data
This is collected by external providers who track user behavior across thousands of websites.
How it works:
- Platforms like ZoomInfo, Bombora, and G2 monitor content consumption by placing tracking pixels across the web, mainly on publisher websites.
- They look for spikes in research around specific topics related to your product or service.
How to use it:
- Choose a provider (or multiple for broader coverage)
- Set up keywords related to your product or service
- Monitor for increased research activity around these topics
2. First-Party Intent Data
This is data you collect from your own digital properties. First-party intent data focuses on how prospects interact with your:
- Website
- Content
- Emails
- Ads
To leverage this:
- Implement robust tracking on your digital assets
- Analyze engagement patterns
- Create a scoring system based on different interactions
3. Predictive Intent Data
This advanced approach uses AI and machine learning to predict future intent based on historical data and complex behavior patterns. It goes beyond simple trigger events by attempting to forecast which accounts are likely to enter a buying cycle soon.
Recommended tools for predictive intent data:
- 6sense: Offers AI-powered insights and predictions about account behavior and buying stages.
- Lattice Engines (now part of Dun & Bradstreet): Provides predictive analytics and AI-based recommendations for B2B sales and marketing.
- MadKudu: Uses machine learning to score leads and predict customer behavior, integrating with popular CRM and marketing automation tools.
- Leadspace: Offers a B2B customer data platform with predictive analytics capabilities.
- TechTarget Priority Engine: Combines intent data with predictive analytics to identify accounts likely to make a purchase.
- Mintigo (now part of Anaplan): Provides predictive marketing and sales insights using AI and big data analytics.
- EverString: Offers a B2B predictive marketing platform that helps identify and prioritize potential customers.
When choosing a predictive intent data tool, consider factors such as integration capabilities with your existing tech stack, the breadth and quality of data sources used, and the tool’s track record in your specific industry. It’s also worth noting that many of these platforms offer features beyond just predictive intent data, so evaluate their full range of capabilities to ensure they align with your overall sales and marketing strategy.
How Intent Data is Collected
Intent data providers use various methods to collect and analyze online behavior:
- Cookie Tracking: Monitoring user behavior across multiple websites.
- IP Address Identification: Linking online activity to specific companies.
- Natural Language Processing (NLP): Analysing content consumption to understand topic interests.
- Contextual Analysis: Evaluating the context in which content is consumed.
Implementing Intent Data in Your SDR Workflow
Now that we’ve explored the different types of intent data and their potential impact, it’s time to roll up our sleeves and get practical. Implementing intent data into your SDR workflow isn’t just about having access to the data—it’s about strategically integrating it into your daily processes to maximize its value. The following steps will guide you through setting up a robust intent data strategy that can significantly boost your outreach effectiveness and conversion rates.
- Choose Your Tools: Select intent data providers that integrate well with your existing tech stack.
- Define Your Ideal Customer Profile (ICP): Be specific about industry, company size, tech stack, and pain points.
- Select Relevant Topics and Keywords: Think beyond just product names. Include industry challenges and pain points.
- Set Up Tracking and Scoring: Implement a system to capture and score intent signals.
- Develop a Tiered Outreach Strategy: Create different approaches based on the strength and type of intent signals.
Best Practices for Using Intent Data
Implementing intent data in your SDR workflow is just the first step. To truly harness its power and drive meaningful results, it’s crucial to follow best practices that have been proven effective across various industries. These guidelines will help you avoid common pitfalls, maximize the value of your intent data, and continuously improve your outreach strategies.
- Combine Multiple Data Types: Use a mix of third-party and first-party data for a more complete picture.
- Act Quickly: Intent data has a short shelf life. Reach out within 24-48 hours of detecting strong signals.
- Personalize Your Approach: Tailor your message based on the specific topics prospects are researching.
- Align with Marketing: Collaborate on content creation that addresses the topics your high-intent accounts are exploring.
- Continuous Learning: Stay updated on new intent data sources and strategies.
The Future of Intent Data
The landscape of intent data is rapidly evolving, driven by advancements in artificial intelligence and machine learning. As these technologies continue to mature, we can anticipate significant enhancements in the accuracy and applicability of intent data.
In the near future, we’re likely to see more sophisticated and accurate predictive models that can forecast buyer behavior with unprecedented precision. Additionally, we can expect a seamless integration of intent data across all customer touchpoints, providing a holistic view of the customer journey.
This integration will enable real-time personalization based on intent signals, allowing SDRs and marketers to tailor their approach dynamically as prospects interact with various channels. These advancements promise to make intent data an even more powerful tool for sales and marketing professionals, further blurring the line between predictive and reactive strategies.
Wrapping Up
Intent data is a powerful tool that complements other sales intelligence strategies like monitoring trigger events. By focusing on prospects’ research behavior and interests, you can identify opportunities earlier in the buying cycle and tailor your approach more effectively.
Remember, the key to success with intent data is to use it as a starting point for meaningful conversations, not as a shortcut to skip building genuine relationships with your prospects.
