Account Engagement Models

You can't treat every customer the same. Does every customer deserve a great experience? Absolutely.  Should every customer have the same engagement model? Absolutely not. I said it. I'll die on this hill. Something I've seen in many Series A/B companies, the customer engagement model is the same for a $5K customer as a $500K customer. Same onboarding. Same check-in cadence. Same QBR format. Small customers are over-serviced—too many meetings, too formal for their needs.Teams are on a literal hamster wheel. Large customers are under-served—not enough strategic partnership, you can’t get the execs into a conversation because you’re not having the right conversations or delivering the right value. CS teams are exhausted trying to be everything to everyone. And efficiency is in the toilet. This approach isn't sustainable but many companies default into it while waiting for the “right” time to tackle it. Ready is a decision, not a feeling. Every customer deserves the right engagement model to maximize the value of their investment in your product. But that model has to vary. Different customer sizes, complexity levels, maturity stages, and industries have fundamentally different needs. And economically, it doesn't make sense to deliver the same experience across the board. When you get honest about segmentation, everything changes. In one company I worked with, this is how we approached the first phase of segmentation—we kept it simple: Strategic Accounts (Top 20% of ARR): Named CSM with <30 accounts. Quarterly business reviews with executive sponsors. Custom success plans tied to their business goals. Proactive roadmap discussions. Growth Accounts (Next 30% of ARR): Named CSM with ~60 accounts. Digital engagement supplemented with personal touch. Bi-annual strategic check-ins. Standardized playbooks with customization. Scale Accounts (Remaining 50% of ARR): Pooled support with specialized experts. Digital-first engagement. Automated health monitoring with human escalation when triggered by risk or opportunity. We made the changes and we made no excuses. Customers appreciated the honesty. In the company I mentioned above, customer satisfaction improved across ALL segments. Strategic account retention hit 97%. Scale account retention improved from 86% to 91%. CS costs as a percentage of revenue dropped 35%. CS team engagement scores went up. They were no longer context switching all day every day. Your customer engagement model should be developed and iterated based on what actually works for each customer group. Segmentation isn't about treating customers unfairly—it's about serving them appropriately so each one can achieve maximum value. The model you design today won't be the model you need in 18 months. Customer mix changes. Product evolves. Market shifts. Your engagement approach has to evolve with it. #CustomerSuccess #CustomerExperience #CustomerJourney #RevenueGrowth

I recently talked with a fellow marketer about account scoring in #ABM.  They struggled to adapt to the recent privacy law, which made me think: We must rethink how we do this. Account scoring is evolving. It's moving way beyond simple intent data.  You need a new approach to find these high-potential accounts in a privacy-first world. I call it Account Scoring 2.0. But why? ➖Traditional intent data is becoming less reliable.   ➖Privacy regulations are changing.   ➖Third-party cookies are fading away.   ➖We can't rely on old methods.   ➖We need to be more innovative. 👉🏾 Using Account Scoring 2.0 helps you focus on first-party data. This is data you collect directly from your target accounts like: Website visits. Content downloads.    Engagement within your emails.  Collecting and analyzing this data is valuable. It's also privacy-compliant. 👉🏾 Other things to look out for are behavioral signals.  Look out for target accounts engaging with your content.    Are they attending your webinars?  Are they interacting with your sales team?  These actions show interest and suggest potential. 👉🏾Predictive modeling plays a key role too. You can use AI to analyze first-party and behavioral data.  This helps you predict which accounts are most likely to convert.  It allows you to prioritize your efforts. Remember, it's about working smarter, not harder. 👉🏾 Don't forget contextual data; it matters, too.  What's happening in the market?  Look for industry trends that align with your offerings.  Are there changes in your target accounts' businesses?  Understanding the context helps refine your scoring. Look at Account Scoring 2.0 as a strategy, not just technology. It's more about understanding your ideal customer profile. It's about aligning sales and marketing and building relationships while respecting privacy more efficiently. What are your thoughts on the future of account scoring? Have you used it before? #b2bmarketing #marketingstrategy

The MQL was never what we wanted — it was just what we could measure. Time to fix that. What we actually want are engaged buying groups showing legitimate purchase intent. Not just one person downloading an eBook, but multiple stakeholders from a target account actively researching and demonstrating they're moving through a buying process. HAND RAISERS I’d argue that the best way to measure this is a steady stream of actual hand-raisers who genuinely want to talk to Sales. This was a key metric we used at Marketo. Real hand-raisers: ✅ Show demonstrate legitimate purchase intent ✅ Have genuine budget and timeline constraints ✅ Want to validate decisions, not collect information These people (and accounts) convert. They close. Sales velocity and win rates increase dramatically. WHY WE NEED LEADING INDICATORS But… buyers are far along their journey before raising hands. 6sense research shows 81% of buyers have a preferred vendor by first contact, and 85% have established requirements before reaching out.  In other words, they’ve already basically made their decision by then. So… we also need earlier signals (e.g. leading indicators) to help us know we’re on the right track. This leads to the following framework: TIER 1: TARGET ACCOUNT ENGAGEMENT Web visits, content downloads, etc. from the right accounts TIER 2: MEANINGFUL MOMENTS Real engagement from decision makers at target accounts, including executive attendance at your events or dinners, participation in your community discussions, and live discussions with your team. (This is especially important in the Age of AI, where increasingly AI will disintermediate our traditional digital signals, like web visits and email opens.) TIER 3: BUYING GROUP FORMATION & INTENT Activities that show purchase intent, including multiple visitors from the same account, intent signals, and pricing/ROI research. TIER 4: HAND RAISER Genuine inbound requests to engage with Sales. So, this means we should also be tracking: ✅ Account Coverage: What percentage of our target account list is showing engagement? ✅ Buying Group Velocity: How quickly are accounts moving through the journey stages? ✅ Engagement Intent: Are we seeing surface-level interest or genuine research behaviors? ✅ Multi-threading Success: How many stakeholders per account are we reaching? The beauty of this approach is that it gives both Marketing and Sales much richer intelligence. Sales isn't getting a random lead who filled out a form, they're getting context about an entire buying group's journey, key stakeholders, and specific interests. And it forces marketing to think like sales, activating buying committees, not generating individual leads. The MQL obsession has created what I call “lead theater” — lots of activity that looks productive but doesn't move the revenue needle. This is a better way. #B2BMarketing #MarketingAutomation #AccountBasedMarketing #LeadGeneration #MarTech

I stopped caring about attribution five years ago. And pipeline got better. Not because I figured out the perfect multi-touch model. Not because I finally nailed first-touch vs. last-touch. But because I started measuring something else entirely. Account progression. Think about it. We're selling six-figure software. Did one ad, email, or phone call ever exclusively get that company to become a customer? Of course not. Yet we often try to associate impressions, clicks, form fills, MQLs scientifically to individual activities and sales outcomes. Early in my career, I fought hard to convince leadership that my MQLs were perfect. And it's only a matter of time before they convert. I was wrong. Activity metrics don't predict revenue. What actually predicts revenue is MOVING accounts move through defined stages: ↳ unaware → ↳ aware → ↳ engaged → ↳ qualified → ↳ sales ready → ↳ customer. Each stage has clear criteria. An account moves from unaware to aware when multiple contacts are exposed to your brand. Aware to engaged when those contacts start interacting. Engaged to qualified when they match ICP criteria and data shows research intent from multiple people. The entire GTM knows the definition of each stage and what needs to happen to move accounts forward. When you see data for each account at each stage, you don't need to argue about which touchpoint gets credit. For example, you can measure whether accounts exposed to your LinkedIn campaign progressed from aware to engaged at twice the rate of accounts who weren't. You get to ask better questions: Did this campaign accelerate account progression? The attribution debate is really a symptom of a deeper problem—marketing and sales aren't aligned on what success looks like. Account progression gives both teams a shared metric to rally around. Marketing focuses on moving accounts forward. Sales engages when accounts are ready. Anyone else think this way? If you want our framework that replaces touchpoint "attribution" with a system that measures whether GTM activity actually moves accounts forward, get our Account Progression Framework 👇🏼 https://lnkd.in/esd5s2cS

The generalist CSM model is becoming outdated. As customer needs grow more complex and outcome-driven, leading organizations are restructuring their Customer Success teams with specialized roles, smarter coverage models, and scalable engagement strategies to meet the moment. We’ve already seen the shift toward scaling Customer Success—now, we’re witnessing a new evolution: delivering high-touch, personalized experiences at scale. This transformation is accelerating the move away from the generalist CSM model. Here’s what we're seeing: 🔹 Strategic Value-based CSMs → Focus: Executive alignment, business outcomes, long-term value realization → Look for: Consultative thinkers, commercial acumen, relationship builders with enterprise experience 🔹 Adoption/Product Consultants → Focus: Driving usage, behavior change, and time-to-value → Look for: Enablement, change management, persona-based engagement 🔹 Technical Advisors → Focus: Deep technical guidance, product integration, onboarding success → Look for: Product depth, cross-functional collaboration, IT & DevOps fluency 🔹 Renewal Analysts → Focus: Churn prediction, risk mitigation, contract forecasting → Look for: Data-savvy, RevOps fluent, outcome-oriented The Shift in Models: From 1:1 to Scalable Coverage Organizations are also moving from traditional 1:1 account ownership to specialized and shared coverage models to drive efficiency and outcomes across the full customer base: 🔸 Dedicated (1:1): Best for top Strategic/Enterprise accounts needing white-glove services/custom planning 🔸 Pooled/Pod Models: Shared CSM resources based on triggers (e.g., lifecycle stage, risk signals), supported by playbooks and automation 🔸 Digital-Led: Tech-touch engagements at scale, powered by AI, in-product guidance, success centers, and lifecycle campaigns 🔸 Hybrid Models: Blend high-touch and digital support based on customer segmentation, value potential, or complexity Why Make the Shift? Because expecting one person to be a product expert, change manager, data analyst, and executive whisperer is unrealistic. Why condition your customers to expect high-touch support across every product—often delivered inconsistently—when you can instead scale the right expertise, at the right moment, through the right channels? Specialized roles and right-fit coverage models enable teams to scale effectively, align to customer needs, reduce burnout, and ultimately drive retention and growth. How is your team evolving to meet modern customer expectations? Which role or model has made the biggest impact? #CustomerSuccess #CSMStrategy #OrgDesign #DigitalCS #Scale #Leadership #PostSalesTransformation #CustomerExperience

Here is how we went from "it's not going to work" sales skepticism to "ABM is driving discovery calls with our enterprise accounts" with Customs4trade team: Here are 5 pillars of our playbook. 1. ACCOUNT ALIGNMENT. We developed clear account prioritization criteria based on cluster definition, industry focus, and engagement signals - not just firmographics. 2. REPOSITIONED SALES REPS AS INDUSTRY EXPERTS. Our pilot sales team shifted from a standard sales messaging to: - Sharing valuable insights on customs automation challenges that their target audience cared about - Building relationships with the entire buying committee - Running in-depth research to identify the needs and the buyer journey stage of the target accounts. 3. VALUE-ADDED ENGAGEMENT OVER PITCHING. This playbook included multiple meaningful non-sales touchpoints to create awareness, including: - Content co-creation with the target buyers - Account-specific "love letters" mentioning target accounts achievements initiatives and - Thoughtful commenting on buyer posts 4. BUYER-CENTRIC WEBINARS. Instead of hidden product demos, we created an educational webinar for supply chain experts, revealing non-obvious mistakes in customs workflows, and how to handle them. Next, showing the best practices. Join promotion by marketing and sales helped to achieve a 50+% attendance rate. 5. WEEKLY SPRINTS. Every week became a sprint where, with the team, we reviewed and planned the next week, including: - What went right? - What can be done better? What can we immediately implement? - How can we work better together during the next sprint? - What distracted or get on our way in the last sprint? - What are the bottlenecks that slow down our velocity? - What should we publish next? - What new accounts should we research and engage? - What can we do to facilitate opportunity generation with the engaged accounts? THE RESULTS: - 10 discovery calls booked - 26 active focus accounts generated - Replies from accounts they'd been reaching out to for years - 4x increase in content impressions and sales profile viewers --- On Wed, we're going live with Joseph Threlfall (marketing) and Bowin Cai (sales) to dive deeper into their pilot program and case study. Save your spot here: https://lnkd.in/d9YEQwDd

Outbound in 2026 isn't about better emails. It's about better routing. Instead of scaling volume - focus your effort on the right account, triggered by the right signal. Here's the full system: 1/ ICP modelling & closed-won analysis → HubSpot, Attio or your CRM of choice Pull the closed-won data. The patterns are already there. You're looking for what your best customers had in common before they bought. Use Claude Code to analyze the data. 2/ TAM mapping & qualification → AI Ark, DiscoLike (TAM mapping) → Claygent, OpenAI, Firecrawl (qualification) Build the universe that matches your refined ICP. Then qualify before anyone touches the list. AI agents now scrape websites, read job postings, and parse 10-Ks for you. Work that took an SDR a full week runs in 20 minutes. The output: a list where every account has been validated against your fit criteria, not just pulled from an Apollo filter. 3/ Scoring & tiering → Clay One platform to score, tier, and route. T1, T2, T3, DQ. Effort should match account value. A tier 1 account deserves 30 minutes of research. A tier 3 account deserves a templated email and nothing more. 4/ Contacts enrichment → Prospeo, FullEnrich, CompanyEnrich Waterfall enrichment is non-negotiable in 2026. Single-source enrichment hits 40-60% coverage on a good day. A waterfall across multiple providers gets you to 85%+. 4.5/ High-intent signal tracking (running in parallel) This runs alongside your outbound, not after it. → Website visits: RB2B, Instantly, Vector 👻 Anonymous traffic deanonymized. The accounts already researching you, before they fill out a form. → Meeting form: Tally, Default Capture intent at the highest moment of interest. Route hot leads instantly, no SDR triage delay. → Webinar attendance: LinkedIn, Luma Event signals are the most underused intent data in B2B. Someone gave you 45 minutes of attention. Use it. → Content engagement: Trigify.io, Teaminfluence, Clay Track who's commenting, liking, and engaging with your content. That's a warm list disguised as social activity. 5/ Outreach (effort scales with tier) → Tier 1 (call + LI + email): Nooks, Lemlist, Instantly The full-court press. Multi-channel, multi-touch, hand-crafted messaging tied to the closed-won patterns from step 1. → Tier 2 (LI + email): lemlist, Instantly LinkedIn warm-up before email. You've already shown up in their feed when the email lands. → Tier 3 (email only): Instantly.ai Volume play, but still personalized at the company level. Templated isn't the same as generic. Tier mismatch is the most common outbound mistake. Calling tier 3 wastes calls. Templating tier 1 wastes the account. 6/ CRM sync → CRM of choice Closed-won data flows back into the ICP model. The loop closes here. This is what makes 2026 outbound a system, not a campaign. Every output trains the next input. Save this if you're rebuilding your stack.

I find that most B2B companies that go after enterprise accounts focus too much of their ABX strategy around data and analytics instead of actual engagement. They buy fancy technology that promises to uncover account-level insights (coupled with contact-level data if they’re a bit more advanced) such as views and website visits, maybe sprinkle some 3rd party intent data from G2 and such, and then spend lots of time and resources on making sense of that data. Most of the time, this data becomes yet another white elephant, and the SDRs and AEs that are meant to act on those signals are overwhelmed and confused as to what a healthy next step should be. This is how most ABX programs die - with a lack of attributed impact and zero buy-in from leadership. And it all started with having overly optimistic expectations from ABX measurement tools, and overlooking the actual engagement strategy. Here’s a refined approach: 1. Invest time in account selection and buying group mapping BEFORE the year/quarter starts, and use the intent signals you have to prioritize the accounts you want to go after. 2. Build your ABX demand generation program around creating 3-4 meaningful touchpoints with each relevant contact on your target buying group. Meaningful = creating awareness for the pain you’re solving and for your brand. Extra points if you can get a case study in front of their eyes featuring a competitor of theirs. 3. Collaborate with SD+Sales teams to define what exactly is the engagement strategy (or reachout, if you will) they should pursue when the right signals come in. Nail specific account strategy, and ensure the value prop is clear, then create email templates as a baseline for personalization, set SLAs and spend time on proper enablement. 4. Launch a small scale pilot (25-50 accounts max) with a handful of reps. Make sure the flow is understood and is effective, scale with your successes, and remember to keep celebrating wins with the SD + Sales teams. Getting their commitment is not about fancy Marketing ROI reports, it’s about highlighting specific winning examples. __ Focus your ABX strategy on the actual account activation and engagement. The tools that help you monitor and track aren’t really “ABX platforms”, and with no proper followup you’ll just overflood your team with heaps of worthless data… #abm #ABXstrategy #b2bmarketing

We inherited a Google Ads account burning £4K/month with zero attribution. Here's what we found. A B2B technology company came to us, spending £4,000 per month on Google Ads. They had been running the same campaigns for 14 months. When we asked what pipeline those campaigns had generated, the answer was: we don't know. Here is what the audit uncovered: 1. 62% of spend was going to broad match keywords that attracted unqualified traffic 2. Landing pages had no clear call to action for enterprise buyers 3. The same ad copy was shown to every visitor regardless of company size, industry, or buying stage 4. No remarketing sequences for accounts that showed initial interest 5. Zero integration between Google Ads data and their sales pipeline The total spend over 14 months: £56,000. The attributable pipeline from that spend: £0 confirmed. Not because Google Ads does not work for B2B. It does. But only when it is built into a system that targets the right accounts and tracks the right outcomes. The ABM Paid Media Restructure (what we built in 30 days): 1. Replaced broad keywords with intent-based search terms mapped to their target account list 2. Built account-specific landing pages with messaging aligned to each stakeholder's priorities 3. Created remarketing sequences triggered by account engagement signals, not just page visits 4. Integrated Google Ads conversion data directly into CRM pipeline stages 5. Set up weekly pipeline attribution reports so every pound of spend was accountable The lesson is consistent across every account I audit. Paid media in B2B is not a lead generation tool. It is a pipeline acceleration tool. And it only works when it is connected to named accounts, personalised messaging, and closed-loop attribution. If your Google Ads or LinkedIn Ads spend cannot be traced to specific pipeline, you have an attribution problem before you have a performance problem. DM me "PAID" and I will run a 15-minute review of your paid media setup and tell you exactly where the leaks are. --------------------------------------------------------------------------- Who am I I'm Lukas, founder of LDS Digital. What I do I help businesses build steady lead and revenue systems. What LDS Digital does We turn interest into real enquiries and booked calls using account-based marketing and AI automation. Who we help B2B operators who want growth without guesswork. The outcome A clearer pipeline, better lead quality, and more predictable revenue. Why this works This approach works because it focuses on fundamentals, clean execution, and systems that keep performing over time. If this resonates, feel free to DM me.

Before we touched a single campaign, we built the unit economics model. First-purchase contribution margin: $44. LTV by month three: $123. Breakeven: somewhere between month two and three. That model changes everything that comes after it. We were onboarding a supplements brand. Subscription-based, strong repeat behavior, healthy LTV curve. The instinct at the start of an engagement is always to get into the account, find what's inefficient, and start optimizing. But if you don't know what a new customer is actually worth to the business before you start, you have no basis for judging whether $60 NCAC is too high, acceptable, or conservative. Over the course of the engagement, ad spend increased by roughly 60 percent. NCAC doubled in the same window. On the surface, that reads as a performance problem. With the unit economics model in place, it reads differently: did the LTV hold? Did contribution margin by cohort hold? Did the business actually get less profitable, or did it get more expensive to acquire customers while still recovering that cost within the same payback window? The model is what tells you which story is true. We see this pattern on almost every account we take over. The previous setup was optimizing for ROAS or a blended efficiency metric with no anchor to unit economics. When you don't know your payback window, you can't distinguish between a spend increase that's working slowly and one that's broken entirely. Everything looks like a problem. Everything looks like a fix. The LTV-to-CAC framework isn't something you build mid-engagement when things get complicated. It's the first thing you build, before you touch a single bid, before you change a single budget. Because without it, you're not making decisions. You're making guesses that look like decisions.