When two companies merge, most of the headlines talk about market share, valuations, and synergies. Maybe there’s a stock bump. Maybe there’s a press release with the word “transformational” used at least four times. But behind the scenes - usually just out of earshot from the boardroom - there’s a group of people quietly hyperventilating. They’re the ones staring down two overlapping, bloated, often contradictory MarTech stacks and wondering: how the hell are we going to make this work? Because here’s the truth: mergers and acquisitions are just as much about technology alignment  as they are about financial engineering. And for Marketing Operations and digital strategy teams, the real battle often starts after the deal is done. Two stacks enter, one stack limps out When two organisations come together, their MarTech ecosystems rarely fit like puzzle pieces. More often, it’s like trying to combine a high-performance sports car with a family SUV - both have value, both serve different needs, and neither was built with the other in mind. You’ll usually find: Two CRMs  (with wildly different field definitions and dirty data) Multiple email platforms  (because someone once liked the UI better) Redundant CDPs, CMSs, DMPs, and data warehouses Conflicting customer journeys A dozen tools that no one remembers paying for And guess what? Every one of those tools has passionate defenders who will tell you, with complete confidence, that their platform is the backbone of everything . Welcome to the tug-of-war. The quiet chaos of post-merger integration While the finance team celebrates synergy and the CEO posts a LinkedIn selfie with the “new team,” Marketing Operations is in the trenches. Their job? Rationalise the stack, migrate data, maintain campaigns, and somehow keep revenue flowing. Here’s where the wheels often come off: No clear owner of the integration strategy.  Is it IT? Is it Marketing? Is it both? (Spoiler: it’s neither unless someone gets accountable fast.) Incomplete audits.  The deal went through, but no one actually inventoried the platforms in use. Political battles.  Each team wants to keep their tech, their process, and their preferred vendor contacts. Different maturity levels.  One org might have a deeply automated, AI-enabled stack. The other may still be manually segmenting lists in Excel. Integration becomes a minefield of duplicated functions, stalled migrations, and “temporary” dual systems that somehow persist for years. Where M&A deals go wrong (from a MarTech perspective) Let’s be blunt: many M&A deals fail to realise their full value because no one properly plans for the tech stack fallout. Here’s where the cracks start: MarTech due diligence is an afterthought In most M&A negotiations, tech gets lumped into the “IT” bucket - a checkbox in the due diligence process. But MarTech isn’t just back-office tooling. It’s how you acquire, retain, and grow customers. It’s how you engage in-market buyers. It is  your go-to-market engine. Ignoring it until after the contract is signed is like buying a house without checking the plumbing. There’s no common customer definition This one’s sneaky and deadly. If the two merging companies don’t have a shared taxonomy for accounts, contacts, personas, and stages, then combining their data is going to be a mess. And your CDP, CRM, and reporting dashboards will all end up lying to you. You can’t sunset what you don’t understand Sunsetting tools is essential post-M&A. But you can’t kill what you can’t map. If no one knows exactly what’s connected to which systems, what automations are running, and what campaigns are dependent, you end up keeping everything “just in case.” That’s how tech debt becomes cultural. How to win the tug-of-war (or at least not lose) Let’s be practical. If you’re navigating a merger or acquisition — or see one coming — here’s how to approach your MarTech stack with some sanity. Start with a stack audit You need to know what you're dealing with. Inventory all platforms, licenses, user roles, API integrations, and critical workflows. Map what’s actively used versus what’s shelfware. Look at renewal dates. Look at dependencies. Bring the skeletons out of the closet early. Define the future-state architecture Don’t just reactively merge tools. Design a future-state stack that aligns with the new business goals. That might mean keeping one platform, merging two, or ripping everything out and starting fresh. But make it intentional. Ask: What are our core platforms? Which system becomes the source of truth for customer data? What functionality is duplicated, and what’s missing altogether? How will we handle compliance, consent, and governance post-integration? Prioritise integration by value, not ease It’s tempting to knock out “easy” integrations first, but that’s how you end up optimising trivia. Instead, prioritize based on business impact: Which systems drive revenue? Where are the biggest customer experience gaps? What’s stopping campaign velocity or reporting? Focus there first. Communicate - and then overcommunicate A lot of MarTech integration fails because people assume things. Don’t. Marketing needs to know what’s changing. Sales needs to know what’s working. IT needs to know what not to unplug. Document everything. Share timelines. Set expectations. M&A is a time of uncertainty. Transparency wins. Bonus round: What AI means in a merged world A quick aside - AI has a role to play here, but only if your data’s clean and your systems are talking to each other. AI can’t solve a fragmented customer view or deduplicate 200,000 contacts across two CRMs. But it can  accelerate personalisation, scoring, and segmentation once your stack is rationalised. Just don’t expect ChatGPT to clean up your HubSpot–Marketo hybrid monster. That’s still your problem. Final thought: It’s not a tech stack, it’s a growth stack M&A deals aren’t just about combining P&Ls. They’re about unifying teams, customers, and go-to-market strategies. Your MarTech stack sits at the centre of that. If you treat it as an afterthought - something to “sort out later” - you’ll end up with fragmented experiences, misaligned teams, and wasted investment. But if you lead with strategy, clarity, and ruthless prioritisation, you can turn the chaos into an advantage. You’ll move faster, get more value from your tech, and actually deliver on the growth the M&A was supposed to unlock. Because in the end, it’s not just a tug-of-war. It’s an opportunity to build a stack that’s stronger, smarter, and finally worth the hype. Discover our Podcast Discover our Services

Separating hype from help when integrating systems with AI-driven features Mergers and acquisitions are a special kind of chaos. MarTech teams often find themselves in the eye of the storm, trying to stitch together two (or more) wildly different tech stacks while maintaining business continuity, data integrity, and some semblance of sanity. In recent years, AI has been hailed as a silver bullet that can magically solve the complexity of stack integration. Spoiler: it’s not. That said, AI does bring real capabilities that can genuinely ease the burden - if you know where to look and what to ignore. This article cuts through the noise to show what AI can actually  do during post-merger MarTech integration, where it tends to disappoint, and how to use it as a lever rather than a crutch. The post-merger stack problem: Why this is hard When two companies merge, their MarTech stacks don’t just combine - they collide. You’re likely dealing with: Overlapping platforms (two MAPs, two CRMs, multiple analytics tools, etc.) Conflicting data models that don't speak the same language Inconsistent campaign logic and legacy workflows that don't align Multiple systems of record for the same customer, all claiming to be the truth Different privacy and compliance protocols based on region or industry It’s not just a tech problem - it’s a people and process problem. MOPs teams are expected to somehow align strategy, tools, and execution across all of it, while keeping the lights on. Add in time pressure, budget constraints, and competing internal politics, and you’ve got a recipe for disarray. In this context, AI gets marketed as a quick fix. The reality? It can help - but only if you understand its strengths and limitations. The promise and peril of AI in MarTech AI thrives in environments where there are large amounts of structured or semi-structured data and repeatable tasks. This is exactly what much of Marketing Operations deals with: data normalisation, lead scoring, behavioural predictions, and campaign automation. The promise is real. But AI in MarTech is also oversold. Many tools claim "AI-powered" capabilities that amount to glorified rule engines or simple if/then logic with a shiny front end. Worse, some teams adopt AI tools expecting strategic clarity or stack consolidation decisions to be made for them. AI can: Process enormous volumes of customer and campaign data Identify patterns and trends that humans would miss Predict future behaviors with a degree of statistical accuracy But AI cannot: Understand your unique customer journey nuances Weigh human dynamics, org politics, or customer sentiment Decide which tech stack to keep or sunset post-merger Use AI to accelerate and enhance human judgment—not to replace it. Where AI can help Data deduplication and cleanup One of the biggest headaches post-merger is duplicate and inconsistent data across systems. AI-powered deduplication tools can recognize fuzzy matches across fields and databases. These systems use machine learning to detect patterns such as variations in spelling, abbreviations, or partial entries, which traditional rule-based deduplication tools might miss. Examples: Merging "John Smith, Acme Inc" and "Jon Smyth, Acme Corporation" Matching records where email domains are different but behaviors align AI tools can also flag outliers and inconsistencies, making it easier for teams to cleanse data at scale. This reduces downstream integration errors and avoids broken segmentation or scoring models. Identity resolution Customers interact with brands across channels: email, web, social, in-app, events, etc. Merging two stacks often results in disjointed customer records across platforms. AI can help unify these disparate identities using probabilistic matching and behavioral linkage. Benefits: Build more accurate customer profiles Improve segmentation and targeting Reduce data fragmentation between CRMs, CDPs, and MAPs A solid identity resolution engine enables personalization at scale and sets the stage for compliant data handling. Journey mapping and personalization AI can analyze historical customer journey data across both merging organisations. It can uncover successful pathways, drop-off points, and content preferences. This analysis helps MOPs teams create hybrid nurture programs that reflect the best of both legacy companies. Use case: A newly unified MAP can use AI to suggest new multi-touch campaigns based on cross-company engagement patterns. AI can also help tailor content to personas or lifecycle stages by learning from historic engagement data, enabling smarter, faster personalization. Predictive lead scoring and prioritisation AI-powered scoring models use historical engagement, firmographic, and behavioural data to predict which leads are most likely to convert. In an M&A context, where sales teams are likely being reshuffled and territories redrawn, this can be a lifesaver. Key advantages: Enables faster prioritization of leads from both legacy pipelines Helps newly unified sales teams focus on high-propensity targets Just make sure your data is cleaned and mapped first. Garbage in, garbage out. Workflow automation AI can observe how teams previously used automation platforms and make recommendations to simplify or unify processes. Some platforms even auto-build workflows based on historical logic or offer templates based on best practices. Use case: Creating re-engagement workflows for inactive segments based on campaign history This is particularly useful when your new combined MAP or CRM is overloaded with fragmented or duplicative workflows. Where AI falls flat Tool rationalisation and stack strategy AI doesn’t understand vendor relationships, political dynamics, or budgetary nuances. It can’t tell you whether your team prefers Pardot over Marketo or whether Salesforce's renewal contract includes heavy penalties. This is strategic work.  It requires workshops, interviews, and a long-term vision for marketing and sales alignment. Contextual business decision-making AI doesn’t understand business nuance. It can’t decide whether expanding to a new market or integrating a specific tool supports the new business model. It doesn’t know your board’s risk tolerance, your compliance obligations, or your internal turf battles. These are executive-level decisions, and no machine can substitute for that context. Data model design AI can help suggest field mappings or infer relationships between data points. But designing a unified data model - especially across complex B2B buying cycles - requires deep architectural thinking. AI won’t: Know what your key segmentation drivers are Understand your lifecycle stages Make decisions about attribution model priorities That kind of model-building is foundational, and it takes human collaboration to get it right. Consent and compliance logic AI tools may be able to automate elements of consent management (e.g., surfacing records missing consent flags), but they don’t understand legal nuance. Data privacy regulations like GDPR and CCPA are complex and often require manual interpretation. Risks of relying too much on AI: Sending marketing emails to contacts without compliant opt-in Assuming outdated consent logic is still valid Ignoring jurisdictional data handling requirements You still need a legal team and strong governance protocols. How to vet AI tools during a Merger or Acquisition Here are smart questions to ask any vendor claiming AI capabilities: Is the AI embedded or bolt-on?  Native AI tends to be more reliable and better integrated than third-party add-ons. What data does the model need, and where does it live?  Make sure it can connect to your unified systems and not just your legacy data silos. Can you audit the model’s decisions?  Transparency is key. If a model scores a lead or suggests a workflow, can you see why? How customizable are the outputs?  If the tool is rigid, it may be worse than doing it manually. What happens if the model is wrong?  Have guardrails, overrides, and human review steps in place. The best tools treat AI like a helpful analyst, not an infallible oracle. The human element: Why AI won’t replace judgment Post-merger integration is fundamentally about people: aligning cultures, blending teams, negotiating priorities, and making hard calls about what stays and what goes. No model can do that for you. Your AI tools can: Speed up analysis Eliminate repetitive tasks Suggest optimizations But your MOPs leaders must: Define the integration roadmap Navigate organizational complexity Own the strategic outcomes There’s no replacement for human judgment, especially in moments of uncertainty. Treat AI like an assistant, not a decision-maker. Conclusion: Use AI like a scalpel, not a sledgehammer AI has its place in post-merger MarTech stack integration, but it’s not a one-size-fits-all solution. Used wisely, it can dramatically improve speed, accuracy, and scalability. Misused, it can add complexity, amplify errors, and create a false sense of security. Your job is to: Know where AI adds value Know where it doesn’t Set the right expectations with leadership Stay in control of the strategy Because when someone says, "AI will take care of it," your answer should be: "Great. But only after we know what 'it' actually is, and why it matters." Want some support? 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Introduction: When data becomes collateral damage Mergers and acquisitions are high-stakes moves designed to unlock growth, expand market share, or streamline operations. But buried under the headlines and handshake photos is the unglamorous reality: data chaos. Marketing Operations (MOPs) professionals often inherit this mess. Disparate systems, duplicate records, mismatched models, missing consent flags - it all creates a perfect storm of bad decisions and broken customer experiences if not handled right. While strategy, branding, and integration get the boardroom attention, it’s the unseen data layer that makes or breaks post-merger success. This article is a deep dive into post-M&A data hygiene: What it is, why it matters, and how to do it right. It covers the four pillars of MOPs-led data recovery and hygiene: Governance Mapping Validation Cleansing And yes, we’ll also walk through the disasters that happen when these are ignored. Data governance: laying down the law Why it matters Post-M&A, data governance isn’t just a compliance checkbox - it’s a risk mitigator, a trust builder, and a sanity saver. Without strong governance, your unified MarTech stack becomes a Frankenstein of conflicting standards, undefined ownership, and liability minefields. What good governance looks like Ownership clarity:  Every dataset needs an owner, especially when multiple teams are involved. Documentation:  Define data types, standards, usage rules, access rights, and lifecycle policies. Policy alignment:  Harmonize policies from both companies to ensure legal compliance across geographies and industries. Steering committees:  Form a cross-functional team to oversee ongoing data decisions and resolve conflicts. Pitfalls to avoid Assuming one company’s policies will automatically take precedence Letting IT own governance without marketing input Neglecting governance for shared systems (CRM, CDP, MAP) Governance isn’t sexy, but it’s the backbone of sustainable integration. Data mapping: Connecting the chaos Why it matters You can’t unify what you don’t understand. Mapping is about creating a clear inventory of fields, formats, relationships, and flows across your systems. It’s the first step toward integrating meaningfully - not just technically. What mapping entails Field matching:  Align first name to first name, job title to job title—and know where they don’t match. Field definition:  Understand how each field is used , not just what it’s called. ("Lead Source" may mean wildly different things across systems.) Data lineage:  Document where data originates, how it flows, and where it’s stored. System roles:  Know which systems are sources of truth, which are derivative, and which are deprecated. Tools and tactics Schema comparison tools (e.g. Talend, Informatica) Field-level audits Data flow diagrams Workshops with key users to uncover tribal knowledge Mapping is tedious—but skipping it guarantees bad integrations. Validation: Trust but verify Why it matters Post-merge systems may look unified on the surface, but they can hide deep inconsistencies. Validation ensures your mapped and migrated data is not just present, but accurate, relevant, and usable. Validation best practices Sample-based QA:  Run audits on subsets of records across systems to confirm consistency. Business rule checks:  Validate that scoring models, segmentation logic, and lifecycle stages still function correctly. User testing:  Bring in marketers and sales reps to test real workflows. Volume monitoring:  Watch for spikes or dips in activity that could indicate pipeline blockages. Key questions to answer Is the right data arriving in the right system at the right time? Are marketing workflows firing as expected? Is sales seeing the same customer truth as marketing? If not validated, even clean data is useless. Cleansing: Removing rot at the root Why it matters Cleansing is where the real hygiene happens. All the governance, mapping, and validation in the world won’t help if you’re sitting on outdated, duplicate, or irrelevant data. Core cleansing activities Deduplication:  Use AI or rules-based tools to consolidate records Normalization:  Standardize field formats (job titles, phone numbers, countries, etc.) Obsolete data removal:  Delete or archive records that no longer meet quality thresholds Consent flag alignment:  Update or remove contacts without compliant opt-in Enrichment:  Add missing firmographic or behavioral data to enhance records Tools and tech Data cleansing software (e.g. Openprise, Ringlead) CRM/MAP dedupe tools Custom scripts for batch cleansing Data brokers for enrichment Cleansing is an ongoing discipline, not a one-time event. Build it into your post-merger roadmap. Real-world data disasters The misaligned country field:  One company used "US" while the other used "United States." Result: segments broke, emails went out to the wrong regions, and privacy violations occurred. Duplicate CRM records:  Sales teams unknowingly worked the same accounts twice. Customers received multiple outreach emails and churned. Consent chaos:  Half a million records lacked GDPR flags after a merger. Emails went out. Fines followed. Final thought: Integration is a hygiene test, not a tech test Technology won’t save you if your data hygiene sucks. What matters is the discipline behind how data is governed, mapped, validated, and cleansed. For MOPs teams, post-M&A is your moment to lead - not just to clean up, but to shape how the new marketing engine runs. Because when the data is clean, compliant, and aligned, everything else becomes easier: campaigns, segmentation, lead flow, reporting, sales handoffs, and customer experience. The tech stack might merge in a few months. But the data battle? That’s yours to win every day. Need help navigating a messy M&A integration? Get in touch for a MarTech assessment built for the chaos. Discover our Services Discover our Podcast

Firstly, let’s get something out of the way: Not everything that claims to be AI is  AI. In fact, if you removed the letters “A” and “I” from the average MarTech vendor's current slide deck, you'd be left with a handful of vague promises and a chatbot that panics when you type in anything unexpected. And yet, here we are. Boards are demanding “AI integration.” CMOs are parroting vendor hype. MarTech budgets are shifting, and everyone is suddenly fluent in buzzwords. But here’s the rub: much of what’s being sold as artificial intelligence is really just advanced automation, glorified statistics, or, frankly, smoke and mirrors. Spoiler: it’s not all doom and gloom - some of it’s genuinely revolutionary. But the first step is knowing the difference. What is AI - really? Let’s define terms before anyone gets too excited. Artificial Intelligence in a business context is software that can perceive , decide , and act - often in a way that adapts over time. If your MarTech tool isn’t doing at least two of those three things with a feedback loop, it’s not AI. It’s just good engineering. The main AI categories: Symbolic AI (Old-school rules engines) Think: “If X, then Y” No learning, just a decision tree with a superiority complex Statistical AI (Modern ML) Think: predictive lead scoring, anomaly detection Finds patterns using models and data Generative AI (ChatGPT, etc.) Think: net-new content from training data Humanlike surface abilities, still stats underneath Spotlight: Agentic AI (the real future of Marketing Ops) Agentic AI  refers to systems that don’t just respond - they take initiative. What makes it different? Goal-oriented : You set a target; it figures out how to reach it Autonomous : It makes decisions, not just predictions Self-improving : Learns from its own wins and fails In Marketing Ops, this could look like: Launching a campaign based on observed buyer journey breakdowns Rebuilding workflows without asking Flagging data model drift and rewriting attribution rules If your current stack needs babysitting and prompting? That’s not agentic - it’s needy. The “fake AI” problem Most vendors using the AI label are actually delivering: Conditional logic Scripted flows Rule-based automation Useful? Sometimes. Artificially intelligent? Nope. Fake AI giveaways: Behaves the same every time Doesn’t improve without a developer Avoids technical explanations No feedback loop or adaptation Offenders in the wild: “AI” lead routers using ZIP codes Chatbots with three scripted paths Subject line tools with 200 pre-written options If the tech can’t learn, adapt, or do more than it did on day one , it’s not AI. It's a fancy spreadsheet with ambition. The grey area - borderline AI Some tech walks the line. It’s not fake, but it’s not truly intelligent either. These are tools that often include: Predictive modeling Some adaptation Limited autonomy Examples: Lead scoring : ML-trained, but only retrains if a human says so Send-time optimization : Might update weekly, might not Web personalization : Uses rules, not learning Litmus test: Can it change its behaviour without  you telling it to? If yes : it’s on the AI spectrum. If no : it’s still just automation. Real AI in the wild Now for the good news. These are tools that do  cross the line into true AI: Dynamic content generation Writes emails, scripts, pages based on real-time data Adaptive journey orchestration Changes campaign flows based on audience behavior Conversational intelligence Summarizes calls, flags deal risks, suggests next steps Auto-optimizing media spend Real-time bid shifts without a media buyer watching Campaign-building copilots You describe the goal; it builds and adjusts the strategy These tools evolve. They learn. They actually earn the “intelligent” label. Why the confusion persists 1. Vendor inflation AI gets funding, so everyone says they use it - even if it’s duct tape under the hood. 2. Board-level FOMO C-suites want “AI adoption” but often don’t know what it means. 3. Procurement theater Buyers tick the “has AI” box without verifying. 4. Zero standards There’s no governing body for what qualifies as AI in MarTech. 5. Media hype feedback loop No one wants to admit the emperor’s new chatbot isn’t actually learning. What Marketing Ops leaders should do ✅  Demand technical transparency Ask your vendors how  their AI works. If they can’t explain it, don’t buy it. ✅  Define the use case first AI is a tool, not a strategy. Start with the problem. ✅  Build AI literacy internally Your team should be able to sniff out hype. ✅  Pilot, don’t commit Test for real adaptation before a full rollout. ✅  Stay ahead on agentic tech The shift from reactive to proactive systems will define the next era of ops. Final thoughts: AI is not magic - it’s maths with ambition True AI doesn’t just automate - it decides , adapts , and evolves . But right now, most marketing tech stacks are drowning in faux-AI tools that talk big and deliver little. The danger isn’t that AI will replace you. The danger is that someone who knows how to use it properly will outpace you - fast. So ask yourself (and your vendors): What does your “AI” do when no one is watching? If the answer is “nothing,” you’ve got work to do. Want to see what real AI could look like in your stack? Let’s talk. No hype. Just reality. Want WAY more detailed information on this topic? Download our whitepaper Discover our Services

The Challenge: Unlocking the full potential of 6Sense A leader in digital experience and business application solutions, sought to enhance their 6Sense investment to better align with their Account-Based Marketing (ABM) strategy. Their goals were clear: Prove 6Sense’s impact on pipeline conversion Improve data integration across their Marketing Automation Platform (MAP) and CRM Strengthen lead activation strategies to drive revenue growth Despite leveraging key 6Sense features such as predictive scoring, segment sharing to Salesforce (SFDC), and LinkedIn Matched Audiences, they faced several challenges preventing them from maximizing their ROI: Key challenges identified: Data integration gaps  – Missed opportunities in integrating 6Sense with their MAP, limiting cross-platform data utilization. Limited data enrichment  – A strong keyword strategy was in place, but brand terms weren’t translated, restricting international market visibility. Additionally, data exclusion settings needed refinement to eliminate unnecessary data from 6Sense scoring. Underutilized orchestration & activation  – While some orchestrations pushed 6Sense segment data to SFDC, Google Ads activation was not configured, and two Sitecore orchestrations were inactive. ROI & conversion proof points needed  – To validate their investment, they needed clear metrics proving 6Sense’s impact on pipeline conversion and revenue generation. The Solution: A strategic overhaul for ABM optimization Sojourn Solutions conducted an in-depth audit of their 6Sense configuration, data administration, and orchestration workflows. Our analysis led to actionable recommendations designed to strengthen data governance, enhance audience targeting, and improve marketing-to-sales activation. Key recommendations implemented: Enhancing Eloqua integration  – Configured outbound syncs from their MAP to receive 6Sense segment names, improving segmentation and pipeline visibility. Expanding audience intelligence & data enrichment  – Leveraged the Marketing Intelligence Package (including People API acquisition) to identify additional high-intent leads and enrich existing profiles. Translating brand terms in keyword tracking  – Captured non-English intent signals to improve international visibility and engagement. Enabling Google Ads activation  – Integrated 6Sense segment data into Google Ads to replicate the success seen with LinkedIn Matched Audiences. Aligning 6Sense MQLs with lead scoring models  – Ensured a seamless nurture process before pushing leads to SFDC, optimizing lead flow and nurturing activities. Strengthening data governance & compliance  – Expanded exclusion settings to filter irrelevant activity, ensuring cleaner insights for sales and marketing teams. The Results: Tangible business impact With these optimizations in place, our client is now better positioned to track 6Sense’s impact on business growth. We are measuring success through key performance indicators (KPIs) aligned with operational efficiency, pipeline growth, and revenue impact. Operational efficiency – Streamlined data & integration KPIs:  Activation of Eloqua-6Sense sync, enhanced data filtering, improved orchestration. Business outcome:  More precise targeting, enhanced lead nurturing, and reduced manual inefficiencies. Pipeline growth & lead quality – Driving higher conversion rates KPIs:  Increased marketing-sourced pipeline, enriched lead data from People API, optimized global keyword tracking. Business outcome:  Higher-value leads, improved sales-marketing alignment, and stronger pipeline contribution. Revenue impact & ROI proof – Demonstrating business value KPIs:  Faster pipeline velocity, higher conversion rates, and an ROI dashboard tracking 6Sense-attributed revenue. Business outcome:  Validated business case for 6Sense, ensuring long-term strategic investment. Final thoughts By addressing key challenges and implementing strategic enhancements, our client has successfully optimized its 6Sense investment. With improved integration, better audience insights, and stronger lead activation strategies, they are now positioned to drive measurable business outcomes and validate ROI. If your organization is looking to maximize the impact of its ABM technology investments, Sojourn Solutions can help you unlock the full potential of your platforms. If you would like to learn how we can optimize your marketing technology stack for success, lets talk. Discover our Services Download our FREE initial Scorecard

Members of the Sojourn Solutions team had the opportunity to attend Demandbase GO  in London on April 30, 2025. The event was a deep dive into the next evolution of Account-Based Marketing (ABM), the rise of agentic AI, and the reality of executing a go-to-market (GTM) strategy in today’s fragmented, buyer-centric world. Here are our key takeaways from the event: ABM is growing up - and getting smarter 80% of businesses say ABM has helped increase revenue. But as we heard throughout the day, ABM isn't just a set of tactics - it's becoming the backbone of modern GTM. The message was clear: ABM must be flexible, cross-functional, and data-driven . Key points: ABM has moved through four phases: from basic targeting to intent-driven, platform-enabled, and now toward integrated orchestration with open APIs and AI agents. Buying groups—not individual leads—are now the primary focus. Demandbase reported a 65% CTR and 2.6X deal size  when targeting entire buying committees. First-party data and integration across the stack (CRM, MAP, CDP, etc.) are essential to keep everything aligned and measurable. Agentic AI is coming fast - and it’s not optional AI took center stage, with a major shift in tone: it’s no longer about using AI tools - it’s about designing your GTM strategy around AI teammates . What we heard: Demandbase is rolling out a system of connected AI agents to help execute GTM tasks autonomously—starting with campaign optimization, insights generation, and buying group engagement. ForgeX highlighted that the biggest blocker to AI isn’t tech—it’s lack of expertise . Most teams are stuck in pilot mode and need support moving to orchestration. The future lies in “agentic execution”—AI that goes from objectives to tasks to outcomes, embedded across teams and platforms. A striking stat: 91% of companies are using AI in their ABM , but only 19% have a roadmap . Buying journeys are fragmented - and marketers must adapt Today’s B2B buying journey is nonlinear, self-directed, and increasingly digital. Sessions emphasized the need for a “Help your buyer buy”  mindset. What that looks like: Buyers now shortlist 2–3 vendors, not 5–6, and often delay human interaction until late in the cycle. Content needs to serve like your best salesperson: relevant, timely, and value-driven. Personalization at scale is critical—especially in creative and ad targeting. Metrics that matter now: Funnel velocity Engagement by buying group Account reach and territory health Early pipeline indicators like attention and active usage Ops and data are still the bottlenecks - but solvable ones Several sessions - and customer panels from Xero, Randstad, and The Access Group - spoke candidly about operational challenges. Themes included: The myth that ABM is “just a tech solution” instead of a GTM strategy . The importance of small wins and clear ICPs to get stakeholder buy-in. Integration is hard—but doesn’t require a monolith. Instead, bring the best from each tool, connect via API, and use AI to bridge the gaps. Pro tip : Don’t try to boil the ocean. Start small, prove value, and scale with shared goals between sales and marketing. What we’re taking back to our Clients As a team that works every day in Marketing Ops and GTM execution, here’s how we’re applying the learnings: ✅ Start with a clear ICP and buying group strategy - and communicate it relentlessly. ✅ Invest in AI - but not without a roadmap . Train your team, define objectives, and map real use cases. ✅ Treat ABM as a GTM operating system, not a campaign type. ✅ Build integration bridges, not silos. API-first thinking will help you scale. ✅ Make content work harder. Use AI to personalize and deliver value at every stage of the journey. Final thought: ABM isn’t dead - it’s being reborn If Demandbase GO showed us anything, it’s that ABM isn’t an island anymore. It’s becoming the unifying force  that brings sales, marketing, ops, and AI into one coordinated motion. As one speaker put it: “Agentic AI will be more disruptive than the internet.”  That’s a bold claim. But if what we saw at GO is any sign, they might just be right. Discover our Services

When Norstella, a leader in the information services space, set out to unify platforms and data across its brands, it knew the stakes were high. Two complex HubSpot environments - MMIT and Panalgo - needed to become one. And they needed it fast. Enter Sojourn. The Challenge Norstella’s objective was clear: consolidate platforms to boost efficiency, enhance data quality, cut costs, and enable smarter cross-selling and analytics. But the devil was in the detail. Panalgo’s HubSpot instance was nuanced, and the risk of disruption was real. The migration had to be seamless, fast, and aligned with a wider Salesforce consolidation effort - all without breaking marketing operations or dragging legacy issues into the future. Oh, and it had to be done in just five weeks. The Approach We kicked things off with discovery workshops to get under the skin of both platforms. From there, we built a streamlined migration plan that prioritized speed and  sanity. We reused most assets and leveraged a Tool Migrator Map to cut down manual work. We worked shoulder-to-shoulder with marketing, sales ops, and compliance teams - quickly escalating blockers and making fast decisions. We removed outdated assets and redundant forms, implementing best practices across the board. Cutover was clean, communication was constant, and go-live happened early - with responsive support that kept everything ticking post-launch. The Results The migration was more than just successful - it was a showcase of how to do these projects right: 18% under budget Delivered early Streamlined workflows  and fewer forms Data consolidation that sets the brands up for long-term marketing success “I cannot underscore how pleased I was with Sojourn’s overall professionalism during this massive project... a portal migration skillfully executed, ahead of schedule, and under budget. Couldn’t be more satisfied.” - Eugene Won, Senior Marketing Automation Manager, Norstella Need help with a platform migration that actually works  for your business? Let’s talk. Discover our Services Download our FREE whitepaper

The story behind the success Citeline, part of the Norstella group, didn’t just want better Marketing Ops. They wanted faster, smarter, leaner systems that didn’t just tick boxes - but that actually worked . Like most enterprise marketing teams, they were facing legacy systems, disjointed data, manual processes, and that ever-growing inbox of “we’ll fix it next quarter” tasks. Together, we’ve helped Citeline and Evaluate (also part of Norstella) not only migrate and modernize their systems - but do it in record time and with tangible, bottom-line results. Eloqua migration in record time (and under budget) When Citeline and Evaluate needed to consolidate their Eloqua instances, most vendors would’ve quoted 4-6 months. Sojourn did it in under 8 weeks . Not just the migration - but discovery, solution design, build, testing, go-live, and hypercare. And yes, with full compliance, lead scoring, IP warming, and multi-brand logic included too. The impact: Saved approx. 640 business hours Delivered under budget Minimal disruption  to campaign flow Created a solid foundation for the broader Salesforce consolidation Improved campaign governance and marketing effectiveness “The team worked really well together... Sojourn delivered their usual excellence.” - Kevin Sowden, Marketing Ops Director, Citeline & Evaluate From bounced to brilliant: A masterclass in email deliverability Before the Eloqua migration, Citeline's emails were quietly suffering. Soft bounce rates hovered around 20% , inbox placement was meh, and actionable deliverability insights were hard to come by. Post-migration? Things clicked . With help from Sojourn and Validity’s Everest platform, email metrics jumped: 2024 Gains: 40% increase  in audience reach 2x improvement  in engagement Deliverability hit 83.84%  (up from 79%) Hard bounces dropped by 35% 2025 Update: Soft bounces down to just 5–6% Another 2x improvement  in engagement A growing track record of ROI to the business Smarter send strategies: Beating email fatigue without buying more tech Let’s face it: no one wants 6 emails in 3 days about webinars they didn’t register for. Citeline’s customers were feeling the fatigue. Engagement was falling, unsubscribes creeping up, and marketing performance getting blurry. Sojourn worked closely with the Citeline team to implement a smart fatigue prevention program - using Eloqua’s built-in capabilities instead of expensive third-party platforms. What changed? Dynamic send logic based on “Last Email Date” Tiered filters to manage cadence QA process to prevent email collisions Reporting to track and adjust in real-time The results? More than doubled engagement Saved $65K  by avoiding unnecessary MarTech spend “We can now confidently roll out our fatigue management process to other teams.” - Kevin Sowden From manual chaos to automated brilliance: 75% build time saved Citeline’s Customer Service team was drowning in manual emails - writing and sending 120 a day , one by one, via Salesforce. That’s not Marketing Ops. That’s email triage. Sojourn worked with Citeline to design and implement an automated welcome and nurture campaign that: Supports 100+ product variations Uses Instant Marketing (IM)  to outperform Eloqua’s native dynamic content Tailors content for net-new vs. returning customers Sets the foundation for onboarding, renewals, and dual-brand journeys The impact? 75% reduction  in campaign build time 60 hours saved per month , minimum Instant scalability and better customer experiences The bigger picture: Connected, scalable, measurable These aren’t just isolated wins. Together, they paint a picture of what smart, agile Marketing Operations can look like when strategy meets execution. Sojourn didn’t just bring tools. We brought structure, clarity, and momentum to complex enterprise challenges. And Citeline’s team? They delivered every step of the way. Reimagine Marketing Operations. Redefine Success. We've been saying this for a while now, but we do more than you think.  Our clients see bigger impact when they tap into more of what we offer. Behind the scenes, we’re helping other companies streamline, scale, and seriously outperform - and this customer success story seriously highlights the impact we can have across your entire Marketing Operations. Discover our Services

Time travelers, assemble If your Marketing Operations strategy hasn’t had a serious overhaul since Stranger Things Season 1… we have a problem. Because while the tech has sprinted ahead - hello AI, intent data, predictive modeling - the strategy holding it all together is still wearing skinny jeans and quoting "best practices" from 2017. In the B2B space, we love a good platform. We adore automation. But strategy? Real strategy? That gets deprioritized in favor of fire-fighting, campaign-fixing, and playing whack-a-mole with data. The result? Bloated stacks, burned-out teams, and underwhelming impact. The Frankenstack problem: tech that outpaced the strategy Let’s be honest. Most Marketing Operations leaders didn’t build their tech stacks - they inherited them. And then inherited more. And then duct-taped on three integrations and called it a day. The average MarTech stack in 2025 is like a house that’s been renovated by seven different owners with very different tastes. AI platform here. Webinar tool there. Data warehouse duct-taped to a legacy CRM. And oh look, someone snuck in a freemium chatbot in 2021 that’s still live for some reason. But while the tools evolved, the strategy didn't. You’re still: Running quarterly batch campaigns Using MQLs as your primary success metric Routing leads manually based on form fills Reporting on clicks instead of contribution to pipeline The stack may look modern on the surface, but the playbook is aging fast. And the cracks are showing. Marketing Ops isn’t just tech support Once upon a time, MOPs teams were the nerds in the basement who fixed broken workflows and made sure the emails went out on time. Not anymore. Today’s MOPs leaders are expected to: Architect complex customer journeys Lead cross-functional data strategy Integrate AI and predictive tools Own attribution, privacy, compliance, AND performance metrics If you’re still positioning your team as platform admins instead of business strategists, you’re setting them (and yourself) up for irrelevance. Old strategy:  Deliver campaign support and keep the tools running. New strategy:  Drive revenue operations, customer insight, and business scalability. Big difference. One gets ignored. The other gets budget. MQLs still? Seriously? Here’s a fun experiment: Walk into your next leadership meeting and say the word "MQL" with a straight face. Watch the room shift. The truth is, MQLs are increasingly meaningless. Sales doesn’t trust them. Marketing doesn’t even fully believe in them anymore. Yet many orgs still cling to this outdated metric like it’s a lifeboat. Modern marketing ops strategy is moving toward: Qualified buying signals, not just form fills Account engagement over individual activity Sales velocity and conversion metrics Lifecycle stage performance If your team is still optimizing for volume over value, it’s time to retire the MQL and graduate to something more grown-up. You can’t automate chaos Automation is not a strategy. It's an amplifier. If your workflows are messy, your data is dirty, and your lead routing logic is a choose-your-own-adventure novel from hell, automation will simply turn that chaos into a real-time disaster. And now with AI entering the chat, the stakes are even higher. AI doesn’t magically clean data or align teams. It assumes the machine is already well-oiled. If your Marketing Operations foundation is creaky, AI will expose it faster than a toddler with a Sharpie and a white sofa. You need to: Revisit your data structure and taxonomies Map your customer journey end-to-end Audit workflows for redundancy and dead ends Then you can layer on intelligence. Not before. Attribution is still a mess - and you know it Another sign your strategy is outdated? You’re still treating attribution like it’s a finance report. “This click gets 40% credit!” “That webinar was first touch!” “Let’s add another UTM and hope it makes sense later!” Attribution in 2024 needs to be directional, not perfect. Your strategy should aim to: Combine qualitative insight with quantitative data Focus on contribution to pipeline and revenue Align attribution to business goals, not vanity metrics Stop fighting over credit and start looking at influence. (Also, if your attribution report takes more than 10 minutes to explain to a VP, you’ve already lost.) Reporting for the sake of reporting isn’t strategy You know the ones: Weekly dashboards nobody reads Engagement scores that correlate to nothing Time-to-MQL metrics that impress exactly no one Outdated strategies rely on reporting to justify  effort. Modern strategies use reporting to optimize  impact. That means: KPIs aligned with business outcomes, not activities Clear data narratives to support strategic decisions The ability to stop doing things that aren’t working If your reporting hasn’t changed since before GDPR, it’s probably time for a rethink. Your team is capable of more - if you let them Here’s the best-kept secret in B2B: Your MOPs team is one of the smartest, most under-leveraged groups in the company. But if your strategy only allows them to: Pull lists Fix errors Patch integrations …they will stay in the basement, quietly dying inside. Modern MOPs strategy should: Empower them to own process and data architecture Bring them into planning conversations Give them space to experiment, test, and fail You don’t need more headcount. You need a strategy that unleashes the headcount you already have. Leadership hasn't caught up - and it's hurting you Here’s the elephant in the boardroom: a lot of senior leaders still think of MOPs as "that techy thing marketing does." And while you’re trying to modernize workflows and implement predictive lead scoring, your CMO is still asking for a monthly email volume report. Outdated MOPs strategies usually trace back to one thing: Leadership that hasn’t been educated on the strategic value of ops. Your job as a MOPs leader isn’t just to run the systems. It’s to: Translate complexity into business value Advocate for operational maturity Tie marketing infrastructure to revenue outcomes If your strategy doesn’t include upward communication, you’re going to keep hitting the same walls. You’re not behind because you’re bad. You’re behind because you’ve been busy. Let’s be clear - if your MOPs strategy is out of date, it’s probably not because you’re lazy or clueless. It’s because you’ve been: Fixing broken campaigns Training new team members Adapting to org changes Dealing with a never-ending queue of “urgent” requests Strategy got shoved to the bottom of the list. And that’s understandable. But now it needs to move to the top. Because without it, you’re just adding more weight to a system that’s already straining. Key takeaways: Let’s get you out of 2017 ✅ Audit your current MOPs strategy.  What are you still doing that made sense six years ago but doesn’t now? ✅ Shift from platform thinking to system thinking.  It’s not about Eloqua or Salesforce. It’s about how everything works together to drive revenue. ✅ Replace MQLs with more mature metrics.  Think engagement scores, sales velocity, pipeline acceleration, and influence. ✅ Empower your team to operate strategically.  That means fewer ticket requests, more ownership. ✅ Educate leadership.  Don’t just report up. Translate up. ✅ Rebuild reporting frameworks.  Focus on insight, not output. ✅ Future-proof your stack.  Clean up integrations, rationalize tools, and simplify wherever possible. ✅ Make space for strategic thinking.  Book time. Block it out. Treat it like it matters. Because it does. Final thought: The world moved on. So should your strategy. Marketing Operations has come of age. The expectations are higher. The tools are more powerful. The pressure is real. But the opportunity? It’s never been bigger. If your strategy is stuck in 2017, it’s time to let it go. The teams that modernize now won’t just keep up. They’ll lead. And if you need a fresh perspective on where to start? We’ve got one. Let's talk. Discover our Services

Let’s be honest: AI is the shiny object of the moment in Marketing Operations. Every vendor is “AI-powered,” every deck mentions machine learning, and every CMO wants to know why you’re not using AI to do something clever with lead scoring or content. But here’s the problem: most companies are trying to implement AI before they’ve done the basics. They haven’t mapped their processes. Their data is messy. No one knows who owns what. And yet… they’re plugging in AI like it’s going to fix everything. That’s not how it works. And in most cases, it just makes things worse. You can’t automate a mess One of the first things we ask when someone says they want to “bring AI into marketing ops” is: what process are you trying to automate or improve? Half the time, there’s no clear answer. Or worse, the answer is something vague like “personalisation at scale.” If you don’t know your own workflows - what they are, who owns them, what the pain points are - you’re not ready for AI. You’re barely ready for automation. AI should come after  you’ve done the hard thinking: What tasks are repeatable but resource-heavy? Where are humans adding minimal value? Where does speed or scale matter most? Otherwise, all you’re doing is building a Rube Goldberg machine that outputs bad decisions faster. Your MarTech stack is probably already bloated Most B2B companies are already drowning in tools. ESPs, CRMs, MAPs, CDPs, attribution platforms, analytics tools, and now - AI this, AI that, and AI in things that don’t need AI. Adding another tool (especially one branded as “smart”) without reviewing what’s already in place is a fast way to waste money and confuse everyone. So before you chase that new AI plug-in or “intelligent assistant,” ask: Do we already have a tool that does this? Will this add clarity or complexity? Who’s going to maintain it, own the output, and improve it? If you don’t have a solid answer to those, AI is just another acronym in a sales deck. Bad data in = garbage AI out There’s a painful truth most vendors won’t tell you: AI doesn’t magically clean your data. If your segmentation is dodgy, your scoring models are misaligned, or your CRM has a dozen fields for “Job Title” - AI will still give you answers. But they’ll be based on garbage. Fast garbage. Polished garbage. But still garbage. In fact, AI often hides the mess - by making things look smarter than they are. That’s dangerous. Before adding AI, clean house: Fix your taxonomy and tagging. Audit your fields and data sources. Standardize naming conventions. Eliminate duplicates and dead records. The smartest thing you can do with AI is feed it well. AI isn’t a strategy. It’s a tactic. You wouldn’t tell your CFO, “Our strategy this year is Excel.” So don’t say your marketing strategy is “AI.” It’s a tool. It’s a layer. It’s a way of executing faster or smarter - but only after  you know what you’re doing and why. Too many AI projects fail because nobody asked: what’s the actual goal here? Is it reducing manual campaign builds? Improving conversion through smarter segmentation? Forecasting pipeline more accurately? Start with the outcome. Then find the best way to get there. AI may or may not be part of the answer. The teams getting it right? They’re doing their homework. There are  companies using AI well. They’re automating repetitive tasks, surfacing insights, scoring leads more intelligently, and spotting performance patterns before a human would. But they all have something in common: They mapped their workflows. They cleaned their data. They aligned teams on goals. They knew what they wanted AI to do , not just what they wanted it to sound like  on a slide. In other words, they did the unsexy stuff before plugging anything in. 🚫 TL;DR: If you wouldn’t let an intern rewire your entire MarTech stack, don’t let AI do it either - unless you’ve done the research, defined the problems, and prepared the ground. Want AI to make a difference in your Marketing Operations? Step one is strategy. Not software. Discover our Services Download our FREE whitepaper

The death of "Dear Firstname" There was a time when simply remembering a lead’s name in an email felt revolutionary. Today? If your "personalization" is just dropping a first name into a generic email template, you're not nurturing leads - you're nurturing your unsubscribe  rate. Modern lead nurture is no longer about sending more emails, or throwing more webinars into the void. It's about building experiences that actually matter  - tailored, data-driven conversations that make prospects feel seen, understood, and valued before  they ever sign a contract. And the stakes? They're only getting higher. Let’s dig into why smart data  and sharp personalization  are the future of lead nurture - and why those still stuck in the old ways are about to get left behind. The new reality of lead nurture Traditional lead nurturing followed a pretty lazy playbook: Capture a lead (probably with a half-hearted content download) Drip a few emails over 30 days Cross your fingers and hope for a demo request It was mechanical. Predictable. And frankly, it worked - when buyers had fewer choices and longer attention spans . Today, buyers move fast. They expect relevance, immediacy, and proof  that you get them - or they move on . Nurture is no longer a funnel of touchpoints. It’s a dynamic, responsive relationship  that needs to evolve based on each lead’s behavior, preferences, and journey stage. In short: If your nurture doesn't flex, you don't convert. Why personalization is non-negotiable Personalization today is not about slapping someone's job title into a subject line and calling it a day. It's about context : What are their biggest pain points today ? What role do they play in buying decisions? How have they engaged with your brand so far? What content resonates with their specific industry, role, or maturity level? Here's the cold reality: Leads expect a "you get me" experience from the first click - or they'll find someone else who delivers it. Personalized nurture isn't just "nice to have." It’s a conversion accelerator  - and a trust builder  in a world where trust is in short supply. In fact, studies show personalized nurture emails drive up to 6x higher transaction rates  compared to generic ones. But it only works if you’re not faking it. (People can smell fake personalization the way you can smell a desperate sales call.) Data: The fuel behind real personalization You can't personalize without data. Period. Data isn't a "bonus feature" - it's the engine  behind a functioning nurture program. But here’s the kicker: Not all data is equal. You don't need 400 fields of CRM data nobody trusts. You need smart, actionable data  that tells a real story about your leads. The must-have  data buckets: Firmographics:  Company size, industry, geography Demographics:  Role, seniority, department Behavioral signals:  Website visits, content downloads, webinar attendance, email clicks, social engagement Intent data:  Are they actively researching your category? Engagement patterns:  How often, when, and through which channels they interact And crucially: Permissioned data . We’re living in a GDPR/CCPA-first world. If you're collecting and using data without consent, you're not just playing dirty - you're setting yourself up for a fine and  a PR disaster. Smart marketers know: Data-driven personalization isn't creepy. It's respectful , relevant , and rooted in choice . Discover our Podcast - The MOPs Brief Common mistakes that kill lead nurture programs Even with the best tools and intentions, plenty of companies still get nurture horribly wrong. Here’s how: 1. Over-automating and under-thinking Yes, automation saves time. No, it doesn’t replace strategy. If your nurture tracks feel like a cold drip of vaguely connected content, you’re just automating irrelevance. 2. Ignoring the middle of the funnel Top-of-funnel gets all the love: blog posts, webinars, reports. Bottom-of-funnel gets sales calls. But that big, squishy middle - where buyers decide if they trust you  - is often ignored. Nurture is about owning the messy middle, with smart, timely nudges that help prospects self-educate and gain confidence. 3. Personalizing badly Sloppy merges, outdated data, mistaking interest for intent ("you downloaded a whitepaper, marry me?") - bad personalization is worse than none at all. 4. Failing to align nurture with real buyer journeys Many nurture flows are still linear: Step 1, Step 2, Step 3. Real buyers zigzag. Smart nurture adapts - letting content, timing, and channel flex based on behavior, not assumptions. Building a high-performance nurture engine Okay, so what actually works in 2025? The best lead nurture strategies today share a few key traits: ✦ Audience-first design Start with a brutally honest look at your audience’s needs, problems, and context. Build nurture tracks for them  - not for your sales targets. ✦ Dynamic segmentation Move beyond static lists. Use real-time behavior and data signals to shift people between nurture streams dynamically. ✦ Multi-channel orchestration Email alone won’t cut it. Blend email, retargeting ads, social touchpoints, chatbots, webinars, and even SMS (carefully) to create a cohesive experience. ✦ Progressive profiling Don’t ask for everything up front. Use smart forms and gated content strategies to gradually  learn more about leads as they engage. ✦ Content that sells without selling Focus on value-forward  content: Industry insights How-to guides Smart comparisons Customer success stories Bold thought leadership The goal? Make it easier for leads to sell themselves  internally before your sales team ever shows up. The future of nurture: predictive, proactive, personal We’re already moving into a world where lead nurture is: Predictive  (AI models anticipating buyer behavior) Proactive  (outreach triggered by micro-signals, not marketing calendars) Hyper-personal  (experiences crafted for a lead of one, not a segment of hundreds) Brands that invest in smart data infrastructure and ethical personalization now will be the ones closing deals while competitors are still arguing over nurture workflows. Because in the end, lead nurture isn’t just a "marketing task." It’s your first real relationship with a future customer. And like any relationship - if you phone it in, don’t be surprised when they stop answering. Final word: 🚫 Generic nurture is dead. ✅ Personalized, data-powered nurture is the new baseline. The question is: Are you evolving fast enough to keep up? Discover our Services

Let's talk about the closet nobody wants to open Somewhere inside your organization is a marketing technology stack that’s bigger, messier, and more haunted than anyone wants to admit. At a glance, everything looks fine. The campaigns run. The dashboards load. The tech list fits neatly on a PowerPoint slide. But start asking questions - real questions - about what’s in use, who owns it, what it costs, and what value it’s delivering? You’ll quickly discover ghost platforms, forgotten integrations, overlapping tools, and systems nobody’s logged into since pre-pandemic days. The truth is simple: Every MarTech stack has skeletons. MarTech audits are how you find them before they find you. The only real question is: when should you audit? (Spoiler: It’s probably earlier than you think.) Why MarTech audits are non-negotiable The average enterprise now has over 90 MarTech tools  in play at any given time. Even mid-sized companies routinely juggle 40-50 platforms. Each one came with a business case. Each one promised to save time, drive engagement, boost ROI, or “streamline the buyer journey.” Fast forward a year or two: Teams change Strategies shift New tools get layered in Old tools get neglected Budget oversight gets fuzzy Integrations break silently in the background Without a regular, structured audit, your MarTech stack quietly mutates  from a strategic asset into a bloated liability. An un-audited MarTech stack is like a neglected garden: It doesn't just stop growing - it grows wild . The unmistakable signs you're overdue for a MarTech audit You don't need a crystal ball to know when it’s time. Just look for these very real (and very common) symptoms: ✦ Platform sprawl Nobody can answer - with confidence - how many MarTech tools you have, what they all do, and who owns them. ✦ Ghost platforms You discover platforms still being paid for that nobody claims to use. (Bonus points if IT finds them when they’re auditing VPN access logs.) ✦ Duplicate functionality Multiple teams are using different tools to solve the same problem (e.g., three different event management platforms, two CRMs, four survey tools). ✦ Integration chaos "Connected" systems aren’t syncing properly anymore — leading to dirty data, broken workflows, and dashboard numbers nobody trusts. ✦ Shadow IT Departments buy and use their own tools without any oversight. Congratulations: your marketing stack now includes whatever Steve in Demand Gen put on his corporate card. ✦ Rising costs, falling value Budget reviews show MarTech spend creeping upward, but campaign performance isn't moving with it. ✦ Technical debt headaches Your ops team spends more time fixing and patching systems than innovating or optimizing. When is the right time to undertake a MarTech audit? Here’s the honest answer: If you have to ask, it’s time. But there are also some key trigger points where an audit isn't just useful - it’s critical: ✦ Major strategic shifts New GTM strategy? New ICP focus? Expanding internationally? Your tech stack was built for the old playbook. You need to recalibrate. ✦ Leadership changes New CMO, new Head of RevOps, or even a new CFO? You can bet one of their first questions will be: "What are we actually paying for - and is it working?" ✦ Mergers and acquisitions Combining two MarTech stacks is like blending two IKEA furniture sets blindfolded. You must  audit before consolidating. ✦ Budget freezes or cuts If Marketing’s been told to tighten belts, you need to know which platforms deliver real value and which ones are just nice-to-haves. ✦ Tech stack maturity milestones Every 18-24 months, it’s healthy to audit - even if nothing is "wrong" - just to prevent rot from setting in. Discover our Podcast - The MOPs Brief What a strong MarTech audit looks like A real audit goes beyond  asking, "What tools do we have?" It digs deep into performance, fit, cost, risk, and ownership . At minimum, a MarTech audit should assess: Tool inventory:  What’s in use, who uses it, when was it last actively engaged Overlap analysis:  Are multiple tools solving the same problems? Cost vs. value:  Is the platform driving revenue, improving efficiency, or just burning budget? Integration health:  Are data flows clean and stable between platforms? Adoption rates:  If only 10% of your licenses are active, you have a problem. Compliance and risk exposure:  Especially around data privacy, consent management, and security standards Strategic alignment:  Does the tech still match the marketing and sales goals for the next 12-24 months? Common MarTech audit discoveries (and why they hurt) Nobody escapes a MarTech audit entirely clean.Expect to find some (or all) of the following: Finding Why it matters Ghost platforms still billing $10K+ a year Death by 1,000 cuts to your budget Data leakage between poorly integrated systems Compliance risks and dirty data Critical workflows built on unsupported tools Operational fragility Teams clinging to legacy platforms "because they know it" Cultural resistance to change Redundant capabilities Wasted licensing fees and split focus The goal of an audit isn't just to clean house - it’s to create clarity, focus, and efficiency  moving forward. Why people avoid  MarTech audits (and why that's dangerous) Let's be real: MarTech audits feel tedious. They threaten sacred cows. They force uncomfortable conversations about ownership, accountability, and strategic clarity. But avoiding them doesn’t save you from those problems. It just delays  the pain until it's bigger, more expensive, and less fixable. Smart companies don’t audit because something broke. They audit because they know waiting until something breaks is the slowest (and most expensive) way to lose . The bottom line You can either proactively audit your MarTech stack - with intention, structure, and a strategic lens - or you can wait until: A new CFO starts asking hard questions A data breach exposes your compliance gaps Marketing underperformance triggers a stack-wide investigation Your team burns out trying to keep a broken system limping along Audit before the audit audits you. Because in the world of modern marketing, your tech stack isn’t just an enabler - it is  the infrastructure of your entire revenue engine. If you don't know exactly what's in it, what it’s costing, and what value it’s creating?You’re flying blind - and turbulence is inevitable. Final thoughts MarTech audits aren't sexy. They won't win awards. They won’t trend on LinkedIn. But they will potentially, quietly, save you millions ( our last client MarTech audit highlighted $1.1m of savings ), sharpen your strategy, and give your teams the tools they actually need to win. And honestly? That’s the kind of success story that actually matters. Discover our Services