Today on the Demand Gen Pod, Episode 28, Ryan from the demandgen pod discusses the false allure of B2B intent data, emphasizing the importance of proper interpretation and actionable insights. He highlights how intent data helps identify prospects’ interests, engagement levels, and potential purchasing timelines but warns against exaggerated claims about its effectiveness. Ryan also delves into assessing data quality, accuracy, and red flags when choosing providers.
Furthermore, he advises balancing intent data with other signals like demographic insights and historical data for a comprehensive view. Integrating intent data with marketing goals such as lead generation targets and personalized campaigns can enhance ROI and lead quality improvement. Approaching intent data critically while setting realistic expectations can lead to successful implementations when aligned with business requirements. Training marketing teams on data interpretation and analytics tools is crucial for leveraging intent data effectively while fostering a results-driven culture.
Summary notes from Episode 28:
Chapter 1: Understanding B2B Intent Data (00:00 – 01:02) 00:23: Definition of
intent data and the importance of proper interpretation. 01:02: Benefits of
B2B intent data in revealing prospects’ interests and purchasing timelines.
Chapter 2: Challenges and Realities of Intent Data Usage (01:39 – 03:18)
01:55: Caution against exaggerated claims and the need for data quality
assessment. 02:55: Red flags to watch out for when evaluating intent data
providers. Chapter 3: Enhancing Data Accuracy and Quality (03:30 – 04:37)
03:55: Importance of complementing intent data with other data sources for a
comprehensive view. 04:28: Leveraging contextual details and demographic
insights to enhance predictive power. Chapter 4: Aligning Data Usage with
Marketing Goals (04:40 – 05:08) 04:51: Integrating intent data with lead
generation and customer segmentation strategies. 05:08: Ensuring actions
derived from intent data tie back to ROI and lead quality improvement. Chapter
5: Measuring Impact and Ensuring Compliance (05:23 – 06:12) 05:52: Metrics for
measuring the impact of intent data, such as lead conversion rates and
campaign ROI. 06:02: Monitoring lead quality improvement and customer
engagement levels for successful implementations. Chapter 6: Critical
Considerations for Intent Data Success (06:16 – 07:41) 06:30: Emphasizing the
importance of educating marketing teams on data interpretation and validation
methods. 07:28: Encouraging a data-driven culture and incentivizing results-
driven strategies for successful intent data utilization. Chapter 7:
Conclusion and Next Steps (07:45 – 07:51) 07:45: Final remarks on approaching
intent data critically and aligning strategies with business requirements.
07:51: Encouragement to reach out for further information and closing remarks
for the podcast episode.
Full Transcript:
00:00
Good morning, my name is Ryan. Welcome to the demandgen pod. Today we are talking about the false allure of b two B intent data. Intent data in general. So let’s get into it. Thanks so much for taking the time to join us today. If you haven’t already, give us a chance and subscribe to the channel. What about b two B intent data? What is it? Right. So intent data is really just behavioral information that’s collected about potential buyers to predict purchase intent and personalized marketing efforts. And there are some common misconceptions here, like viewing intent data as some like magical solution for underestimating and underestimating the need for proper interpretation. I think this happens a lot where we look at data, particularly intent data, where it’s assumed, most of it is assumed.
00:51
And some of that might behavioral for sure, but it’s really important for businesses to validate intent data providers understand the limitations and then focus on actionable insights. So what can you trust and what can you turn into something that actually will create some sort of ROI? Really though, you know, at a high level, b two B intent data helps us to reveal topics of interest, engagement levels, potential purchasing timelines for prospects. It can help us in identifying active business signals and buying signals, prioritizing leads, and crafting targeted messaging challenges with this can include data accuracy, noise from irrelevant signals, and then the need for real time updates as well. So what about that promise versus the reality? So industry marketing often paints intent data as the silver bullet for sale success.
01:46
But beware of exaggerated claims like this because guaranteed conversions are not a thing that just does not exist. We can make predictions based on certain expectations, historical data, things like that. But just adding in something like intent does not just translate to instant sales boosts. The other thing here is that your perception reality gap can be influenced by data quality, proper analysis, and then alignment with organizational goals. So keeping in mind, sort of each of these steps is really important when you’re planning out your intent data and how to leverage it. What about the quality and the accuracy of it? So quality assessment that you can do, you can look at the data sources that you’re pulling things from, the freshness of that data, and the relevance to the target audience.
02:38
We talk about this a lot here, where we’re always relating things back to a target audience. So, okay, one thing that works in one place, but are you considering your target audience when you consider the factors going into the relevance or the accuracy when you’re making those decisions? Some red flags about all of this, especially when you’re looking at providers, a lack of transparency, inconsistent data patterns, and unverifiable methodologies. We’ve seen this in some vendors in the past where we’re looking at data and we recognize there was actually a time when we recognized that data was really old and they weren’t being very clear about that. We knew it was old because we actually knew the people who they were providing the intent data for, which was fantastic. So unfortunately, that resulted in us no longer having a vendor for it.
03:28
But on the upside, that’s a great example where they’re making these predictions and you need to be able to understand where the data, the root data and the raw data is actually coming from. So you can cross reference data, you can track conversion rates on your own side, and then you can also look for customer feedback as and do those, you know, the feedback panels too. In addition to that, you can balance intent data with other signals. So there’s lots of other data that we have access to as marketers. And you can complement the intent data with demographic insights, past behavior. I already mentioned historical data and social signals for a more comprehensive view. And then, you know, you can prevent over reliance by diversifying your data sources, conducting a b testing, leveraging multiple touch points.
04:14
All of these allow you to get a firmer grasp on what somebody is currently doing and maybe therefore planning to do in conjunction with that intent data that you’re paying for. Contextual details like firmographic data and persistent behavioral cues also really enhance that predictive power of intent data. Now, when you have all of that, you can start to align it with your marketing goals. And so you can align that data usage with lead generation targets, customer segmentation strategies. You can also do things like building out personalized marketing campaigns. Integration may involve mapping intent signals to specific buyer journeys, or creating tailored content, and then nurturing those leads dynamically, which obviously we’re a huge fan of here. And you can ensure the actions that are derived from intent data tie back to ROI, lead quality improvement and revenue growth.
05:08
And you can do that through consistent reporting, post mortems, things like that. You can really look back and say, this is what we thought was going to happen, this is what actually happened, this is what we can learn from it. And this is how we can make adjustments, if that’s necessary. Through all of this, though, make sure that you’re respecting privacy and also make sure that you’re obtaining explicit consent. So in the event that you have forms on your website, make sure that things like this are included in that form. In the form of what they’re acknowledging, to which, I mean, nobody really reads anyway, but just make sure that transparency is there. And also make sure that you have a clear path for opt outs and prioritizing data security measures, too. How do you measure the impact of all of this?
05:52
So you can measure that through lead conversion rates, customer acquisition cost, campaign ROI. You can monitor the metrics like lead quality improvement and sales pipeline acceleration. Customer engagement levels and case studies showcase successful implementations of intent data leading to high deconversion rates, improved targeting and personalized experiences. So it’s not that it’s not doable, it’s certainly possible, and people have had success. But I do think that it’s important to kind of take a couple of extra steps, knowing your own data better than anybody else. It’s, it’s not a boot it up and call it a day kind of a thing, and see immediate ROI. Take time also to educate your marketing teams on things like data interpretation, the significance of context, and also validation methods. And you can also provide training to the team on data analytics tools, insights, interpretation and campaign optimization techniques.
06:46
And these are all things that come into play when you are relying on people to give feedback on tools that they need to understand when they’re being tricked themselves by a tool. And that’s really what that is. Providing further training on things like data analytics tools really come into play because if you can understand how to not only not manipulate data in a bad way, but manipulate data to be able to understand what you’re looking for, but also be able to recognize any sort of personal biases, and then, you know, ultimately, don’t be afraid to give it a shot, don’t be afraid to foster that data driven culture and incentivize results driven strategies.
07:28
So when you do this, when you approach intent data critically and you ensure that you have realistic expectations, when you make sure that you have valid sources and you align strategies to what the business is requiring, you can really make a difference with some intent data. But you have to be careful with that. We will see you next week on the demandgen pod. I hope you’ve had a great time and have learned something. You can reach out to us at hello@demandgenpod.com and we’ll see you next time.