Customer Relationship Management Consulting

The “CRM” you know today is quietly dying. Not with a bang, but with a slow takeover. Look at the last few months: • ServiceNow buying AI workflow and GTM automation players • Clari acquiring Groove • Clari and Salesloft merging These aren’t just logos swapping hands. This is the quiet dismantling of the old Salesforce-style CRM stack. When I first got into sales, the CRM was the source of truth. We were told “if it’s not in Salesforce, it didn’t happen.” The reality? It was mostly a graveyard of stale notes and inflated pipeline. Fast forward to now, the action isn’t in the CRM…it’s in the revenue platforms that actually run sales. Pipeline inspection, buyer engagement, AI-driven forecasting, real-time deal orchestration. It’s like the difference between a dusty ledger and a live market ticker. The contrarian take? I think the future “CRM” won’t be a single system of record at all. It’ll be an ecosystem of orchestration layers, AI-first, deeply integrated, invisible to reps. By the time you “log into” something, it will have already updated itself and acted on your behalf. The CRM will stop being a place you go and start being something that works around you. The real winners here? Not the companies that own the most logos, but the ones that nail: • Seamless AI-powered interoperability • Sales team adoption without force • An actual lift in win rates, not just prettier dashboards I’ve lived through 4 different “end-all” CRM rollouts. Each promised transformation. Most delivered… more admin work. This M&A wave? It feels different. If they execute right, sales leaders might finally stop fighting the tech stack and start trusting it. What do you think? Are we watching CRM’s evolution, or its obituary? #FutureOfSales #SalesStrategy #AIForSales #ModernSelling

The hardest part of being a salesperson? Not closing deals? Not handling objections? It’s updating the CRM 😅 We’ve all been there. But your CRM is only as good as the data you put into it. If it feels like a chore, it’s time to make it work for your team, not against them. Here’s how: 1️⃣ Simplify the process Too many fields or unnecessary steps? Cut them. Keep it lean so your team can focus on selling, not admin work. 2️⃣ Automate data entry Use tools like email tracking, call logging, and activity sync to handle the basics. Less manual input = happier reps. 3️⃣ Make it useful for reps If your CRM feels like it’s only for managers, no one will care. Show reps how it helps them prioritise leads, track follow-ups, and close more deals. 4️⃣ Provide proper training Don’t assume everyone knows how to use the CRM effectively. Run training sessions to show shortcuts, best practices, and how it fits into their workflow. 5️⃣ Reward good habits Recognise and reward the reps who consistently keep the CRM updated. Positive reinforcement goes a long way. 6️⃣ Use data to sell smarter Make the insights visible and actionable. Show your team how CRM data can uncover trends, highlight hot leads, and predict customer needs. 7️⃣ Integrate CRM with other tools Make it seamless. Connect your CRM to email, calendars, and project management tools to reduce context switching and manual effort. 8️⃣ Set the tone from leadership If managers aren’t updating the CRM, reps won’t either. Lead by example and make it part of the team’s culture. 9️⃣ Limit duplicate data entry Nothing frustrates a salesperson more than entering the same information in multiple places. Streamline your systems to avoid redundancy. 1️⃣0️⃣ Review and refine regularly Your CRM setup isn’t set in stone. Get feedback from your team and adjust workflows, fields, and tools to make it more effective over time. Updating the CRM doesn’t have to be the hardest part of the job. A few tweaks can turn it into a tool your sales team wants to use. What’s your team’s biggest CRM challenge and how have you solved it?

Recent headlines suggest CRM is disappearing. But what does that really mean? A good week-end reflection. Over the past year, vendors like Salesforce, Microsoft, and HubSpot have begun embedding AI directly into collaboration tools employees use (Slack, Teams, or email). Users no longer need to open the CRM. This is supposed to make traditional CRM interfaces obsolete. Some recent examples: - Salesforce: AI in Slack allows users to query customer data, update opportunities, and generate summaries directly within conversations. - Microsoft Dynamics 365: Integration with Teams and Microsoft Copilot captures meeting notes and updates CRM records automatically. - HubSpot: Emails and meeting transcripts are logged automatically, keeping CRM records up to date in the background. Consulting and analyst perspectives reinforce this trend. McKinsey and Accenture call it “workflow-embedded AI”: Insights and actions happen inside the tools employees already use. What this looks like: Traditional CRM: User → opens CRM → updates record → continues work AI-embedded CRM: User → works in Slack / Teams / email → AI updates CRM automatically The CRM remains the system of record. But its interface gradually disappears from daily work. How I see it: 1. This is more than a usability improvement. It is a platform competition between collaboration platforms (Slack, Teams), AI assistants, and CRM platforms. Whoever wins may control the enterprise customer ecosystem. 2. This all makes sense. People spend most of their day in collaboration tools and communication platforms, not inside CRM systems. Adoption may finally improve if users no longer feel like they are feeding a system. 3. Customer processes may become less transparent: If interactions happen primarily through AI agents and collaboration tools, visibility into the sales process may become harder. Organizations need even stronger discipline around data quality, data models, governance, and AI supervision Executive takeaways: 1. CRM interfaces may become less visible, but architecture, data quality, and governance are more critical than ever. 2. Executives should evaluate collaboration platforms as part of their CRM and AI architecture strategy. Not as standalone tools. 3. Organizations must remain cautious about overdependence on AI and collaboration ecosystems, to avoid new forms of vendor lock-in. #CRM #salesforce #AI

𝗪𝗵𝘆 𝗮 𝗦𝗵𝗮𝗿𝗲𝗱 𝗦𝗲𝗻𝘀𝗲 𝗼𝗳 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 𝗶𝘀 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗳𝗼𝗿 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 Truly successful ERP, CRM, HR, Payroll implementations don't just go live “𝙩𝙝𝙚𝙮 𝙨𝙩𝙖𝙣𝙙 𝙩𝙝𝙚 𝙩𝙚𝙨𝙩 𝙤𝙛 𝙩𝙞𝙢𝙚”. Successful projects usually have a team that is united by shared sense of purpose. However, this collective vision requires constant nurturing. As projects evolve, team composition changes and business priorities shift. There are many moving parts, which is why it is critical for the leadership team to always be communicating the reality of the situation and what the "win" will look like when you get there. And, most importantly, what everyone's role is in helping to achieve that goal. 𝗣𝗲𝗼𝗽𝗹𝗲 𝗙𝗼𝗰𝘂𝘀𝗲𝗱 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀 𝗼𝗳 𝗦𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 Combined Management Consultants has extensive experience with complex, multi-month (and sometimes multi-year) implementations and we focused on the following elements: 1️⃣ Creating achievable milestones that allow for regular celebration of progress. 2️⃣ Limiting customization to strategic necessities, ensuring faster implementation and clearer focus. 3️⃣ Cultivating adaptability among staff, encouraging embrace of the system's native processes. 4️⃣ Proactively address resistance and cultural barriers before they jeopardize progress. 5️⃣ Identifying and mitigating risks early, meeting governance requirements while protecting the project. 6️⃣ Establishing psychological safety so team members feel supported and heard throughout. 7️⃣ Communicating relentlessly to build awareness and desire for change among all stakeholders. 8️⃣ Fostering collaborative relationships between vendors, staff, and consultants to maintain unified direction. 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗶𝗻𝗴 𝗘𝗺𝗼𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗼𝗺𝗲𝗻𝘁𝘂𝗺 The emotional trajectory of ERP and CRM projects often follows a predictable pattern. Users commence a project with initial enthusiasm followed by declining energy as challenges emerge. The projects that succeed maintain emotional momentum until completion. Leadership must therefore constantly monitor not just technical progress but team morale and engagement. Knowing when the project is veering off track and where to apply corrective effort is essential to bringing your implementation across the finish line with the benefits originally envisioned. What other techniques have you found successful?

List building challenges great companies, not because they lack ambition or market knowledge, but because they struggle to turn account strategy into a clean production line for sellers. The fatigue is real. Reps are told which accounts matter, then left to spend hours validating titles, searching LinkedIn, reconciling CRM records and guessing who is worth calling. Marketing says the data exists. Sales says the data is unusable. Ops worries about duplicates. Everyone has a point, which is exactly why it gets stuck. The better model is a list production cadence. Not panic sourcing when pipeline feels light. Not every rep reinventing persona logic in isolation. A disciplined rhythm: score core CRM accounts, then open opportunities, then closed-lost, then inbound and events, then partner pools, then dormant accounts. Inspect what happened. Rescore, enrich, suppress, rebuild or expand. Centralised list building creates the baseline: right accounts, right personas, best available data, clear call priority. Reps should then add intelligence back through conversation outcomes. A switchboard gives you a name. An IVR exposes a team structure. A referral gives you the real operator. A wrong number still teaches you something. That field intelligence should feed the next production cycle. This is why “more data” is often the wrong first answer. Most companies already have years of CRM contacts, old opportunities, inbound leads, events, partner lists and dormant records. There is usually reachable demand trapped inside the business. The job is to extract it, classify it, score it, produce phone-ready worklists, inspect results, then improve the next cycle. The best outbound teams will not be the ones with the biggest databases. They will be the ones with the cleanest production line from account strategy to rep action, and the discipline to run it every week. Owned data first. Centralised production. Rep intelligence fed back in. New sourcing only where the evidence says the gap is real.

I am beyond thrilled to share the latest salestech technology landscape. This iteration marks its 10th edition and has been the most complex to assemble for several reasons: 1. Explosion of Players: The number of vendors has surged to 2,100, a 34% increase from just 14 months ago. This massive growth, driven by AI lowering barriers to entry, counters the notion of consolidation. 2. Fluid Categories: The salestech space remains dynamic with many mini-categories. These mini-categories emerge quickly, but very few graduate. Many solutions target specific workflows but struggle to expand beyond supporting a few tactics. 3. Generic Claims: The proliferation of websites with similar claims makes it challenging to discern what products actually do and how they work. 4. Frequent Pivots: The pace of pivots has reached an all-time high, making it difficult to keep track of changes. This edition features 5 new categories: • Autonomous BDRs • Autonomous SDRs • Instant Qualification-Routing-Booking • Pipeline Intelligence and Revenue Analytics • AI Role-Play It’s worth noting 2 emerging categories: • Warm Introduction & Referral (now part of Relationship Intelligence) • RFP Response (now part of Sales Content & Collaboration) I anticipate these categories will 'graduate' in the next iteration. Here are the 3 fastest-growing categories: Signals & Intent Data: With over 80 participants (+200%), this category is rapidly evolving beyond monitoring content consumption and job changes to pursue the holy grail: uncovering buyers in market by tracking activities on the open internet and social channels (dark funnel). Sales Data & Signals Aggregation Platforms: This category has grown by 190% and represents the evolution of B2B Customer Data Platforms (CDPs). It unites players from various domains: • ABX platforms that orchestrate sales motions • B2B-focused CDPs (account-aware) • Crossovers from Product-Led Growth CRM • New entrants that let you build a unified data repository by federating multiple data sources, processing signals, and driving various GTM motions Sales (AI) Assistants: This category now includes 150 players (+165%) and features assistants that help sellers with tasks such as account research, uncovering personalities, personalizing engagement, writing communications, sequencing interactions, and preparing and managing meetings. AI’s influence is pervasive across every category and is also driving the development of new ones, such as Autonomous SDRs, Autonomous BDRs, and AI Role-Play. Given the rapid changes in this ecosystem, a LinkedIn post can only cover so much. For a deeper discussion or to brief me on new offerings, please book time during my office hours (link in the first comment of this post). Salestech is buzzing with innovation and rapid change. This is an exciting time to be part of such a dynamic industry. Link to the landscape hi-res PDF in the second comment to this post. #salestech #gtm #ai

Your data problem didn't start in your warehouse. It started in that free-text 'Region' in your ERP. Spending $1M modernizing your stack, won't fix your data. Everyone wants accurate data. But when you dig you realize their processes were never built to produce good data. They’re trying to analyze chaos. A few months ago, we were talking to finance company. They’d just spent 14 months modernizing their stack. They hired the data engineers. Millions spent. Hundreds of dashboards. And yet: “Revenue” in Salesforce included refunds. “Customer” in Marketing meant prospects too. Operations had 15 different “regions” spelled 8 different ways. The tech wasn’t broken. The process was. Their CRM, ERP, and sales systems were designed for convenience, not for data. Every time a sales rep skips a CRM field.. You create a leak in your data foundation. Until your warehouse is garbage. If your processes weren’t designed with data in mind nothing will save you Here is how to go about stopping bad data 1. Design Every Process as if Data Were the End Goal If you’re setting up a CRM, ERP, or even a Google Form, build it like a data engineer would. Even if it's yet. Develop a process with data in mind. As down the line, you will need, and rather than waiting 3-5 months to get data. Replace free-text fields with controlled dropdowns. Enforce mandatory fields that align with business-critical metrics. Executives say they want clean data but approve workflows that guarantee mess. In my opinion, data should be clean from the source. Becuse if it's not, managing pipelines, modelling becomes a nightmare. And even that can't save it 2. Treat Metrics Like Products Agreeing on definitions is not easy at all. People change, leave. 2 VPs can't agree on it so they create their own spreadsheet. Every metric you report on should have an owner, version history, use case and single definition across the company. If found in a situation can't agree, ask "What finding this info enables you" If can't answer it, archive it. Or if can't agree on metric. Seperate and define clear use case where each. 3. Asssign Owner & Build Feedback Loops Bad data comes from the frontlines, reps skipping CRM fields, creating custom objects in Salesforce. Assign owners of the metrics. Answer: Who owns the data? Who manages the inputs? Who's keeping operational systems clean? (Data stewards) If no one is accountable or owns it, how do you thing it will get fixed. Tie accuracy to incentives. 4. Enforce Standards, Not Opinions Everyone uses their own definition of “good data” Define how data should look: formats, naming, validation rules. If “Region” is free-text in CRM, you’ve built chaos by design. 5. Data quality isn’t a project or a one-time thing Start where it's most important. Track exceptions, expose results, fix patterns. Embed it in the system, so it's proactive rather than reactive.

Without data structure, intent signals are just noise. After months of refining our account-based program, I've come to a simple realization: without proper data structure, intent signals are drowned and unusable. The team is tackling this challenge using this 3-step process: 1️⃣ Capturing the RIGHT signals With AI, Clay, and countless data providers, intent signals have become more accessible and commoditized (company announcements, reviews, technographics, hiring and more). It's incredibly tempting to buy it all and see what surfaces — I've been there! But I've learned the hard way that collecting signals without strategy creates more noise than insight. Beyond the hype, we took a deep dive into our customer journey (specifically won deals) to identify common patterns in buyer attributes and behaviors. Yes, we scrap and ingest external signals, but we've placed special emphasis on our 1st party data (CRM infos, website/product tracked events, webinar viewers, ad engagement). This gives us an edge that competitors simply can't replicate. 2️⃣ Building a UNIQUE data set Playing around with new intent signals in Clay is fun — and we do it! But the game-changer was figuring out how to structure and process these signals within the CRM. We've customized HubSpot to store them in custom objects. Every signal, regardless of source, follows the same structure: name, desc, source, URL, and timestamp. This standardization has transformed our ability to combine signals, refine scoring models, and surface insights that truly resonate with our team. In the end, better iteration and more educated guesses. 3️⃣ Routing signals for HUMAN engagement The final and hardest part (in my opinion): getting these signals into the hands of our sales team for meaningful action. While we've automated the routing mechanics, we've discovered that enablement and discipline are equally crucial. We’ve set up regular team meetings to go over disqualification reasons, celebrate wins, and come up with new signal ideas. There’s nothing better than seeing our team turn these intent signals into conversations. Technology enables, but the human connection converts. Open questions to the #Growth and #RevOps in my network: what signals are you prioritizing in your growth strategy right now? What sources are delivering the best results? Any tips on improving signal routing and sales enablement? —— Follow me if you found value in this post 🙇♂️ I used to share stuff about growth, marketing and SaaS.

CRM is not dead. The old way of using CRM is. There is a growing narrative in the market that CRM is fading. That narrative misses the real shift that is happening. CRM as infrastructure is not going anywhere. What is going away is the outdated operating model that turned CRM into an administrative system instead of a growth engine. For years, legacy CRM environments have looked the same: • Highly customized • Expensive to maintain • Burdened with technical debt • Low adoption from frontline teams • AI added as a layer, not embedded in work That model created systems of record. It did not create systems of impact. What is emerging now is very different. CRM is becoming the revenue operating system for the enterprise. The conversation should not be about product names or feature lists. Customers do not buy technology catalogs. They buy outcomes: • Faster revenue execution • Higher seller productivity • Better customer engagement • Shorter time to value • Lower total cost to operate The modern model is built around a unified platform where humans and AI work together in the flow of work across Sales, Service, Marketing, and Contact Center. Key differences in this new approach: • AI is embedded directly into workflows, not bolted on • Adoption is a design principle, not an afterthought • Value is delivered in the first 90 days, not after a year • Implementations are measured in months, not multi-year programs • The focus is measurable business outcomes, not system configuration Companies still need secure customer data, governance, identity, and core business process infrastructure. That foundation does not disappear. CRM remains the backbone. What changes is how that backbone is used. Enterprises cannot afford to keep layering new agents and tools on top of already expensive, fragmented CRM stacks. The future model is: Simpler platform + lower cost + AI in the flow of work = better business outcomes. CRM is not dying. CRM is evolving from a passive database into the active operating system for customer engagement and revenue execution. That is a very different story.

A lot of people screw up data enrichment. And not in small ways...in big ways. I've walked into instances where: 😱 Recent sales-entered data was being overwritten by stale enrichment data 😱 Instead of setting up an integration, a massive file was imported all at once, into standard fields, without a data back-up....leaving no audit trail and losing historical data 😱 Enrichment was set up to trigger every time a record was created or updated in Salesforce, creating a situation where only 1,000 records or less could be updated at one time without hitting the Salesforce API limits 😱 Enrichment data wasn't standardized, so each vendor was entering in different formats for fields like employee size So, how do you set it up correctly? Here's what it should look like... Typical steps included: 1. Input Stage: Define the entry points for raw data (e.g., web forms, imports, email captures). 2. Cleaning Stage: Build workflows to: 🔺 Standardize formats (e.g., phone numbers, dates, addresses). 🔺 Correct invalid or missing data (e.g., normalize country names to ISO codes). 🔺 Remove duplicates based on unique identifiers (e.g., email or account ID). 3. Enrichment Stage: 🔺 Match records with external datasets to fill gaps. 🔺 Append metadata (e.g., confidence scores, enrichment source). 4. Output Stage: Push cleaned and enriched data back into your CRM or database. Example washing machine flow: 1. Input: New leads enter from web forms or imports. 2. Cleaning: 🔺 Deduplicate by email or company domain. 🔺 Standardize phone numbers to E.164 format. 🔺 Normalize country names to ISO codes. 3. Enrichment: 🔺 Call Clearbit API to append industry, company size, and LinkedIn URL. 🔺 Validate emails with an email verification tool. 4. Output: Push cleaned and enriched data back to CRM, tagging it with the enrichment source and date. Things to consider: 🔻 Typically, you want to enter enrichment data into separate custom fields. This is duplicative, but if you don't have really strong audit trails and strong enrichment rules, you shouldn't write into a default field because you could cause confusion and frustration with sales, if you overwrite their recently entered data. 🔻 You need to understand all of the fields you're enriching very intimately...what is their purpose, at which stage do they need to be enriched? Don't be lazy and enrich every field at every record edit, it'll harm your systems and speed-to-lead. 🔻 Make sure any enrichment automation takes race conditions into consideration -- what other automations could be triggered, and how would that impact the API limits/system performance? 🔻 How will you monitor results? Set up reports and audit trails, whether through Snowflake or field history in Salesforce. 🔻 Don't forget about consent management fields! Running out of room....what else? Did you find this helpful? #marketing #sales #marketingoperations #revenueoperations