elemental’s Post

Ben Manashe at INFUSE recently posted that he's officially moving on from #intentdata in the traditional sense as there's plenty of value in leveraging intent data for prioritization but by itself, it's not strong enough to accurately determine in-market activity and clearly define messaging strategy. This is something we have been saying for a long time at Personal ABM - Account-Based Marketers. In Ben's post at the bottom of this discussion, he talks about segmentation layers. But he's missing the layers that have the greatest predictability for success (according to Corporate Visions) and that's critical events/key strategic priorities and problem profiles. Like Ben, we use intent data as a springboard. It's not something to react to. We look to see "why" there is intent. If there is a strategic priority in place and they match a problem profile, we take a 1:1 #ABM approach with these accounts because they are part of our ACTIVE ICP -- accounts that are actively doing something and have a problem that we can fix. According to Corporate Visions - and we concur: The most common segmentation methods—buyer personas, industry verticals, and past purchase history, aren’t the best predictors of buying behaviors. 

Whether you're building an ICP from scratch for your latest product, or taking a fresh look at your current ICP to hit the ground running in 2025, it's no secret that data is critical. But, it’s not just about the data you have–it’s about which data you use and how you layer it! 🎯 Creating a truly effective Ideal Customer Profile (ICP) demands precision, and that starts with carefully selecting the right data: 🔹 Data Selection Matters – Not all data is created equal. Choosing insights that represent who your ideal customer is (firmographics, technographics, exegraphics) versus those that simply inform anyone that could buy from you is key to building a refined ICP. 🔹 Layering Data in the Right Order – Start with foundational data points (e.g. industry) and progressively layer on richer data for a bullseye profile (e.g. exegraphics). This strategic layering ensures that your ICP isn’t just accurate but actionable, giving you a real edge in targeting. 🔹 Intent Signals & Event Data ≠ ICP – Intent signals and event data is for timing your outreach; creating an ICP is about identifying the core characteristics of your best-fit customers. Each approach requires a different set of data, serving unique purposes in your go-to-market strategy. Optimizing your targeting and read more about how a precise ICP can bring efficiency and growth to your marketing efforts: https://lnkd.in/gh49_das #B2BMarketing #ICP #DataDrivenMarketing #TargetingEfficiency #GrowthDriver

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I'm officially moving on from "Intent data". In the traditional sense anyway🧠 Don't get me wrong, there's plenty of value in leveraging "intent data" for prioritisation and to enable targeting with messaging that resonates. However, on it's own, it's not strong enough to accurately determine in-market activity and clearly define messaging strategy. My new passion is "Segmentation Layers". It's the combination of multiple data sources and signals, that allow me to develop programs that surface accounts showing an increased likelihood of purchasing, and enables me to adapt the messaging at different stages of a campaign, whilst increasing the depth of engagement. There's a multitude of "Segmentation layers" that we could use in tandem to create granular audience clusters: ▶ Target Accounts List's ▶ Firmographic data (for look-a-like accounts) ▶ Traditional Intent data (combination of 1st + 3rd party) ▶ Competitor insights ▶ Technographic (Tech spend, install base & contract renewal) ▶ Industry ▶ Core services / products / services ▶ Persona / Job role / Job function ▶ Job title By leveraging as many (ideally all) of these layers to create audience clusters, we can take a 1:1 / 1:Few ABX approach to demand generation, and map out nurture paths with messaging that resonates. We can create a true "Brand to Demand" experience. We can also hone in on certain layers at various stages of the campaign, to adaptively adjust the messaging the further down the funnel prospects travel. Some of the campaigns I'm crafting at the moment using the "Segmentation layer" way of thinking are a symphony of sophistication, substance and function. What are your thoughts? Any layers I'm missing, and should be throwing into the mix? 💡 #abm #accountbasedmarketing #marketing #segmentation #intent #b2bmarketing #thoughtleadership #marketinsights INFUSE

𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗗𝗮𝘁𝗮 𝘄𝗶𝘁𝗵 𝗕𝗜 𝗳𝗼𝗿 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 In today's digital age, social media has become an invaluable source of customer information and market trends. By incorporating social media data into Business Intelligence (BI) systems, organizations can gain deeper insights into their customers, competitors, and overall market landscape. Let's explore the key benefits of this integration: 1. 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀   - Monitor brand perception and product feedback instantly   - Identify and address issues before they escalate   - Tailor marketing messages based on current sentiment 2. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗽𝗿𝗼𝗳𝗶𝗹𝗶𝗻𝗴   - Enrich existing customer data with social media preferences and behaviors   - Create more accurate buyer personas for targeted marketing   - Understand customer interests and pain points for product development 3. 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲   - Track competitors' social media presence and engagement   - Identify emerging market trends and consumer demands   - Benchmark your performance against industry leaders 4. 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗥𝗢𝗜   - Measure the impact of social media campaigns on overall business metrics   - Optimize content strategy based on engagement data   - Identify influential customers and brand advocates 5. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀   - Gather ideas for new features or products from customer discussions   - Validate product concepts through social media feedback   - Prioritize development efforts based on customer demands 6. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗲𝗿𝘃𝗶𝗰𝗲   - Identify common customer issues and frequently asked questions   - Provide proactive support by addressing concerns on social platforms   - Measure customer satisfaction and loyalty through social interactions 𝗧𝗼 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝘁𝗵𝗶𝘀 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆, 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿: - Investing in robust social media listening and analytics tools - Developing a clear strategy for data collection and analysis - Ensuring data privacy and compliance with regulations - Training teams to interpret and act on social media insights 𝘐𝘯 𝘤𝘰𝘯𝘤𝘭𝘶𝘴𝘪𝘰𝘯, 𝘵𝘩𝘦 𝘧𝘶𝘴𝘪𝘰𝘯 𝘰𝘧 𝘴𝘰𝘤𝘪𝘢𝘭 𝘮𝘦𝘥𝘪𝘢 𝘥𝘢𝘵𝘢 𝘢𝘯𝘥 𝘉𝘐 𝘰𝘧𝘧𝘦𝘳𝘴 𝘢 𝘱𝘰𝘸𝘦𝘳𝘧𝘶𝘭 𝘮𝘦𝘢𝘯𝘴 𝘵𝘰 𝘦𝘯𝘩𝘢𝘯𝘤𝘦 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴, 𝘥𝘳𝘪𝘷𝘦 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯, 𝘢𝘯𝘥 𝘴𝘵𝘢𝘺 𝘢𝘩𝘦𝘢𝘥 𝘪𝘯 𝘵𝘰𝘥𝘢𝘺'𝘴 𝘧𝘢𝘴𝘵-𝘱𝘢𝘤𝘦𝘥 𝘮𝘢𝘳𝘬𝘦𝘵. 𝘉𝘐 @ 𝘊𝘦𝘳𝘵𝘢𝘪𝘯𝘵𝘺 𝘐𝘯𝘧𝘰𝘵𝘦𝘤𝘩 (certaintyinfotech.com) (https://lnkd.in/gTdNaBkY)  #SocialMediaBI #BusinessIntelligence #DataIntegration #CustomerInsights #MarketTrends #SentimentAnalysis #DataDrivenDecisions #SocialListening #CompetitiveIntelligence #CustomerEngagement