Implementing a CRM should make Marketing and Sales more aligned. Both teams begin working with the same contacts, companies and opportunities. Marketing can understand what happens after a lead is handed over. Sales gains access to campaign context and previous interactions. Management can follow the journey from acquisition to pipeline and revenue.<\/p>\n\n\n\n
Yet many companies implement a CRM and discover that Marketing and Sales remain misaligned.<\/p>\n\n\n\n
Marketing believes it is generating enough demand while Sales continues to argue that the leads are not qualified. Sales works opportunities that Marketing knows little about. Campaigns continue to be evaluated according to lead volume while the commercial team is measured on revenue. Some contacts receive immediate follow up while others remain untouched for several days. Deals exist in the CRM without a clear source and qualified leads never reach the right sales professional.<\/p>\n\n\n\n
The problem is not necessarily the CRM. It lies in the data and process gaps that the CRM has made visible but the implementation has not resolved.<\/p>\n\n\n\n
Marketing and sales alignment requires much more than putting two teams on the same platform. It requires a common definition of the customer, clear lifecycle rules, ownership, qualification criteria, handoff processes, data integration, commercial feedback and shared performance indicators.<\/p>\n\n\n\n
Salesforce describes sales and marketing alignment as a model in which both teams work around the same customers, data and objectives, supported by common language, processes and technology. Its recommendations include shared definitions, a common buyer journey, integrated technology and handoffs that provide Sales with enough context to understand why a particular opportunity deserves attention.<\/p>\n\n\n\n
This distinction matters because automation does not correct the absence of alignment. If the data is wrong, automation uses the wrong data. If qualification criteria are unclear, automation routes the wrong leads. If nobody knows who owns an account, a workflow simply assigns the record faster according to a rule that may itself be wrong.<\/p>\n\n\n\n
Before automating coordination between Marketing and Sales, the company needs to correct the infrastructure that supports that coordination.<\/p>\n\n\n\n
Read why Marketing and Sales can remain misaligned even after CRM implementation<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Marketing and Sales remain misaligned even with CRM when the platform contains incomplete data, different definitions for the same stages, unclear ownership rules, inconsistent handoff processes and reporting that is still separated by department.<\/p>\n\n\n\n Having a shared CRM does not automatically create a shared operation.<\/p>\n\n\n\n A lead may exist in the same system for Marketing and Sales, but Marketing may classify that lead as ready for contact while Sales considers it too early. An opportunity may technically be associated with a campaign while the sales professional has no visibility into the content consumed or the signals that triggered qualification.<\/p>\n\n\n\n The problem can also occur in the opposite direction. Sales learns through meetings that a particular segment has poor fit, but that information never returns to Marketing. Campaigns continue generating the same type of leads because the feedback remains in sales notes or internal conversations.<\/p>\n\n\n\n Salesforce identifies precisely this need for closed loop feedback. Marketing needs to understand which campaigns generate commercially valuable opportunities and Sales needs to provide structured information about objections, quality and outcomes.<\/p>\n\n\n\n The question should therefore not be only, \u201cDo we have a CRM?\u201d It should be, \u201cDoes our CRM contain the information and rules Marketing and Sales need to make consistent decisions?\u201d<\/p>\n\n\n\n Explore seven ways to scale Marketing and Sales around shared processes, data and responsibilities<\/strong><\/a>.<\/strong><\/p>\n\n\n\n One of the first gaps appears in the way the organisation represents the journey from unknown visitor to customer.<\/p>\n\n\n\n Marketing may use concepts such as subscriber, lead, MQL and SQL. Sales may prefer new lead, contacted, qualified and opportunity. The sales pipeline adds another set of stages.<\/p>\n\n\n\n None of these models is necessarily wrong. The problem appears when they overlap without a clear purpose.<\/p>\n\n\n\n Lifecycle Stage, Lead Status, Deal Stage and similar properties should answer different questions. One may describe the overall relationship between the contact and the company. Another may indicate the operational status of commercial follow up. The pipeline should represent the progression of a specific opportunity.<\/p>\n\n\n\n When these dimensions are mixed, the information loses meaning. A person may appear as an MQL while already having an opportunity in negotiation. Another contact may remain classified as a lead even after becoming a customer. Marketing may use a property to segment campaigns that Sales updates manually for a completely different purpose.<\/p>\n\n\n\n The problem is not limited to reporting quality. Automation begins to depend on statuses that no longer represent the customer journey reliably.<\/p>\n\n\n\n This is why implementation should begin with process mapping. CRM stages should represent concrete steps in the buying process, with clear definitions and observable events that allow teams to understand when a record should progress.<\/p>\n\n\n\n The company should be able to explain in one sentence what each status means, who updates it and what needs to happen before the record moves forward.<\/p>\n\n\n\n If that is not possible, the organisation does not yet have a strong foundation for automation.<\/p>\n\n\n\n Read the seven steps for implementing CRM and Marketing Automation around processes, data and measurable outcomes<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Another common gap appears when everyone agrees that only qualified leads should reach Sales but nobody can explain precisely what qualified means.<\/p>\n\n\n\n Marketing may consider company size, job title, sector and engagement. Sales may focus on problem, timing, authority or the existence of a real project.<\/p>\n\n\n\n In practice, both teams may be assessing different dimensions.<\/p>\n\n\n\n This creates one of the oldest conflicts between Marketing and Sales. Marketing reports strong MQL numbers. Sales argues that those leads do not justify commercial attention. Each side has data that appears to prove it is doing its job.<\/p>\n\n\n\n The solution is to turn implicit knowledge into explicit criteria.<\/p>\n\n\n\n Fit and intent can be combined. Fit assesses whether the contact and organisation match the ideal customer profile. Intent assesses signals that may indicate genuine interest or proximity to a buying decision.<\/p>\n\n\n\n These criteria can later be translated into lead scoring, segmentation and automation, but the technology should come after the agreement.<\/p>\n\n\n\n A scoring model does not eliminate disagreement around qualification. If the criteria have not been aligned first, the model simply transforms disagreement into a formula.<\/p>\n\n\n\n The company should also review its scoring model periodically using actual commercial outcomes. Which characteristics appear most frequently in won deals? Which signals were present before opportunities were created? Which leads receive high scores and are consistently disqualified by Sales?<\/p>\n\n\n\n Answering these questions turns CRM into a learning system rather than simply a classification system.<\/p>\n\n\n\n Learn how lead scoring works in HubSpot, Zoho CRM and Salesforce and how it can support qualification and prioritisation<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Ownership sounds like an administrative setting until it begins to fail.<\/p>\n\n\n\n Who owns a lead that belongs to a company already managed by another sales professional? What happens when the contact is interested in a product managed by another team? What if the organisation operates in several countries? What if the contact came through a partner?<\/p>\n\n\n\n These situations are common in B2B organisations and need to be addressed through consistent rules.<\/p>\n\n\n\n The problem becomes more complex when Marketing uses Contact Owner for distribution, Sales relies on Company Owner for account management and opportunities have different Deal Owners. This structure may be perfectly appropriate, but only if the logic is understood.<\/p>\n\n\n\n Otherwise, the organisation starts creating workflows to correct exceptions. Every new scenario produces another rule. After a few months, nobody can clearly explain why a particular record was assigned to a particular person.<\/p>\n\n\n\n Automating lead routing without resolving ownership does not improve sales process coordination. It simply applies unclear rules faster.<\/p>\n\n\n\n This is the type of decision that should be resolved before technical configuration and tested against real scenarios.<\/p>\n\n\n\n Read how better process design, data quality, ownership and governance can prevent CRM implementation failure<\/strong><\/a>.<\/strong><\/p>\n\n\n\n A lead handoff should involve more than changing an owner or lifecycle stage.<\/p>\n\n\n\n When Sales receives a contact, the sales professional needs to understand why that person is being handed over at that particular moment.<\/p>\n\n\n\n Which campaign generated the lead? Which content did the person access? Which form was submitted? Which product or service generated interest? Is this a new contact or does the company already have a relationship with the account? Which signals led Marketing to conclude that the contact showed commercial intent?<\/p>\n\n\n\n Without this information, Sales effectively starts from the beginning.<\/p>\n\n\n\n The sales professional researches the company, looks through old emails, asks Marketing where the lead came from and tries to reconstruct the journey manually. Part of the value created by Marketing is lost because the context never reached the person who could use it.<\/p>\n\n\n\n Good CRM integration should make sure context travels with the record.<\/p>\n\n\n\n This does not mean displaying dozens of fields. It means presenting actionable information.<\/p>\n\n\n\n Source, main interest, high value pages viewed, submitted forms, campaign, recent activity and qualification criteria may be more useful than an endless activity history with no prioritisation.<\/p>\n\n\n\n Explore how CRM can centralise customer information and provide Sales with the context required for better follow up<\/strong><\/a>.<\/strong><\/p>\n\n\n\n The opposite problem is equally common.<\/p>\n\n\n\n Marketing hands over the lead and then loses visibility.<\/p>\n\n\n\n Was the contact reached? Did they respond? Was there genuine interest? Was the company the wrong fit? Were they already using a competitor? Was the project postponed? Did the person lack authority? Was an opportunity created? Was a deal won?<\/p>\n\n\n\n When these answers do not return to the CRM in a structured way, Marketing cannot improve.<\/p>\n\n\n\n Disqualification reasons should be treated as especially valuable because they provide direct feedback about demand quality.<\/p>\n\n\n\n If many leads are rejected because the companies are too small, targeting may need to change. If many have no active project, scoring may be placing too much value on engagement. If the companies have good fit but the contacts are in irrelevant roles, the campaigns or content may be reaching the wrong audience.<\/p>\n\n\n\n The same principle applies to opportunity loss reasons.<\/p>\n\n\n\n Customer relationship management creates real organisational learning only when the system connects demand origin with commercial outcome.<\/p>\n\n\n\n Without that connection, Marketing optimises what it can observe, usually clicks, conversions and leads. Sales optimises what it can observe, usually opportunities and closed deals. The business loses the connection between the two.<\/p>\n\n\n\n Reporting should therefore make it possible to connect Marketing activity with lead quality, opportunity creation, pipeline and revenue.<\/p>\n\n\n\n Explore the CRM metrics and dashboards that connect Marketing performance with pipeline and revenue<\/strong><\/a>.<\/strong><\/p>\n\n\n\n A company may say it has CRM integration because two applications are technically connected. But technical integration does not automatically mean functional integration.<\/p>\n\n\n\n Contacts may synchronise while important properties do not. Campaigns may exist in the Marketing platform without their source reaching opportunities. Status updates may flow in only one direction. Some information may be overwritten when synchronisation takes place.<\/p>\n\n\n\n Duplicate contacts may also appear when the same person enters through different sources.<\/p>\n\n\n\n The essential question is whether Marketing and Sales can operate from a coherent customer view.<\/p>\n\n\n\n This has become even more important because CRM integration is no longer only a requirement for reporting. It is increasingly a requirement for artificial intelligence.<\/p>\n\n\n\n AI with access to fragmented data makes decisions from fragmented context.<\/p>\n\n\n\n Integration should therefore begin with specific use cases. Which information does Marketing need to send into the CRM? Which Sales data needs to return to the Marketing platform? Which application creates and updates each field?<\/p>\n\n\n\n The objective is not to connect systems for the sake of integration. It is to guarantee that information reaches the team and process that need it.<\/p>\n\n\n\n Explore Liminal\u2019s system integration services for connecting CRM, Marketing Automation, ERP and other critical business platforms<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Read how CRM and Marketing Automation can be implemented as one connected operating system<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Many organisations implement a new CRM or connect a new platform and import their complete historical database without first questioning the quality of that information.<\/p>\n\n\n\n Duplicate contacts, companies without domains, outdated job titles, owners who no longer work for the organisation, fields nobody understands and opportunities that have remained open for years all move into the new system.<\/p>\n\n\n\n The team then begins working with a database that is technically centralised but operationally unreliable.<\/p>\n\n\n\n This has a direct effect on alignment.<\/p>\n\n\n\n If Marketing and Sales do not trust the same data, they look for alternative sources. Spreadsheets, personal lists and manual exports begin to appear. The CRM stops being the common source of information.<\/p>\n\n\n\n From that point onwards, every automation has a limitation. The company may build sophisticated workflows, but they continue executing actions using information the teams do not trust.<\/p>\n\n\n\n Data cleaning should therefore not occur only during migration. Organisations need ongoing policies covering duplicates, required values, normalisation, obsolete fields, object associations and records without clear ownership.<\/p>\n\n\n\n Trust in data is not a purely technical matter. It directly influences adoption, reporting and the ability of Marketing and Sales to make decisions using the same information.<\/p>\n\n\n\n Read why SMEs lose trust in their own data and how CRM governance can help restore confidence<\/strong><\/a>.<\/strong><\/p>\n\n\n\n One of the most serious gaps for Marketing appears when an opportunity is created without enough connection to the lead, campaign or source that came before it.<\/p>\n\n\n\n In that scenario, Marketing can measure how many people converted. Sales can measure how many opportunities were created. The organisation cannot easily understand which activity generated pipeline.<\/p>\n\n\n\n This creates the wrong incentives.<\/p>\n\n\n\n A channel producing hundreds of contacts may appear highly efficient even if almost none becomes an opportunity. Another channel may appear less relevant because it generates fewer leads even though those leads create high value deals.<\/p>\n\n\n\n Sales and marketing alignment requires both teams to observe the same journey.<\/p>\n\n\n\n That means preserving source information, associations between contacts, companies and opportunities, and consistent attribution criteria.<\/p>\n\n\n\n There is no need to pretend that one perfect attribution model can explain every influence on a complex B2B purchase. Buying journeys may involve multiple channels, people and interactions.<\/p>\n\n\n\n But there is an important difference between acknowledging the limitations of attribution and being unable to connect opportunities to any Marketing activity at all.<\/p>\n\n\n\n Explore eight CRM metrics and dashboards for analysing conversion, pipeline, attribution and revenue<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Reporting should move beyond cost per lead and include cost per opportunity, pipeline generated and revenue outcome.<\/p>\n\n\n\n Read how to measure CRM and Marketing Automation ROI in 2026<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Another less visible gap appears when the CRM contains the correct records but not the interactions needed to explain what is happening.<\/p>\n\n\n\n Sales professionals make calls that are not logged, maintain email conversations that are not associated with the CRM, attend meetings without recording notes or leave opportunities open without a next activity.<\/p>\n\n\n\n From the perspective of Marketing and management, the opportunity appears inactive.<\/p>\n\n\n\n From the sales professional\u2019s perspective, several conversations may be taking place.<\/p>\n\n\n\n This damages reporting, forecasting and coordination.<\/p>\n\n\n\n It also makes artificial intelligence less useful because the system does not have enough information to summarise context, identify risks or recommend actions.<\/p>\n\n\n\n Adoption is therefore part of data quality.<\/p>\n\n\n\n The CRM needs to reduce work and improve decision making. If it only creates additional administrative tasks, users will find ways to work around the system.<\/p>\n\n\n\n The Sales team needs to understand how CRM helps prioritise opportunities, prepare meetings, organise follow up and prevent deals from being forgotten.<\/p>\n\n\n\n Learn how to ensure that the Sales team uses the CRM from day one and avoid parallel spreadsheets and processes<\/strong><\/a>.<\/strong><\/p>\n\n\n\n It is possible to have all the data inside the same CRM and remain misaligned through reporting.<\/p>\n\n\n\n Marketing analyses visits, conversions, leads and cost per lead. Sales analyses opportunities, forecast, win rate and revenue.<\/p>\n\n\n\n These indicators are important, but they do not show the relationship between the two functions.<\/p>\n\n\n\n A shared reporting model needs to measure transitions.<\/p>\n\n\n\n How many Marketing qualified leads were accepted by Sales? How long did they take to receive first contact? How many created opportunities? What pipeline value was generated? How many deals were won? How much revenue came from those leads?<\/p>\n\n\n\n When these indicators are available, the discussion changes.<\/p>\n\n\n\n If Marketing generates many MQLs but few are accepted, qualification criteria may be the problem.<\/p>\n\n\n\n If most are accepted but first contact takes too long, the issue may lie in Sales capacity or lead distribution.<\/p>\n\n\n\n If leads generate opportunities but few reach closed won, the company should analyse the sales process, pricing, value proposition or competition.<\/p>\n\n\n\n Reporting should explain where revenue is being created and where it is being lost.<\/p>\n\n\n\n Explore the eight CRM metrics and dashboards needed to analyse pipeline, conversion, sales velocity, forecast and revenue<\/strong><\/a>.<\/strong><\/p>\n\n\n\n The purpose is not to create a larger number of dashboards. It is to give Marketing, Sales and management a shared view of progression through the revenue process.<\/p>\n\n\n\n Not every piece of information needs to live inside the CRM.<\/p>\n\n\n\n A company may use an ERP for invoicing, a Marketing platform for digital behaviour, a support application for technical interactions and specialist systems for operations.<\/p>\n\n\n\n Problems appear when two or more systems try to own the same piece of information.<\/p>\n\n\n\n What is the official source for the company name? Which application controls customer status? Where is company size updated? Which system determines ownership? Where is the final sales value stored?<\/p>\n\n\n\n CRM integration requires defining a system of record for each information domain.<\/p>\n\n\n\n Without this decision, integrations can create loops where one system repeatedly overwrites changes made in another.<\/p>\n\n\n\n Good architecture does not necessarily attempt to centralise every piece of data physically. It creates a coherent view and clear ownership rules around the information.<\/p>\n\n\n\n This distinction is especially important for mid market organisations that begin connecting CRM, ERP, Marketing Automation and Service platforms.<\/p>\n\n\n\n Explore Liminal\u2019s approach to integrating CRM with Marketing, ERP, ecommerce and other business systems<\/strong><\/a>.<\/strong><\/p>\n\n\n\n The technology architecture should also be evaluated as a whole rather than allowing applications to accumulate without a clear purpose.<\/p>\n\n\n\n Explore Liminal\u2019s MarTech Strategy approach for designing processes, systems and technology around business objectives<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Once basic processes are corrected, another temptation appears: automate everything.<\/p>\n\n\n\n Lead scoring, routing, nurturing, lifecycle stage changes, deal creation, tasks, notifications, synchronisation between systems and AI agents can significantly reduce manual work.<\/p>\n\n\n\n But every automation introduces a rule into the operation.<\/p>\n\n\n\n Who owns that rule? Who checks whether it remains correct? What happens when new products are launched? What happens when the sales organisation changes? Who validates whether the workflow still represents the real process?<\/p>\n\n\n\n Without governance, the platform accumulates invisible logic.<\/p>\n\n\n\n One property changes another, which triggers a workflow, which updates a third application. A few months later, nobody can clearly explain why a particular record changed status.<\/p>\n\n\n\n The solution is not to limit automation. It is to govern it.<\/p>\n\n\n\n The organisation needs owners for processes and data, naming conventions, criteria for creating new properties, documentation for critical automation and regular reviews.<\/p>\n\n\n\n Automation should reinforce decisions the business understands rather than become a layer of logic that only administrators can interpret.<\/p>\n\n\n\n Read why automation without strategy can create more friction instead of improving customer and commercial processes<\/strong><\/a>.<\/strong><\/p>\n\n\n\n There is a simple reason automation appears only after these gaps have been addressed.<\/p>\n\n\n\n Automation multiplies consistency. If the process is correct, that is extremely valuable. If it is wrong, the organisation can execute the mistake at scale.<\/p>\n\n\n\n A workflow can make sure every qualified lead reaches Sales immediately. But it needs to know what qualified means.<\/p>\n\n\n\n An AI agent can recommend which opportunity deserves attention. But it needs complete information about activities and pipeline.<\/p>\n\n\n\n A sequence can guarantee follow up. But it needs to know which contacts are already in active negotiations and should not receive automated outreach.<\/p>\n\n\n\n An integration can synchronise thousands of records. But it needs to know which system is responsible for each field.<\/p>\n\n\n\n The correct sequence is therefore data, processes, governance and only then automation at scale.<\/p>\n\n\n\n Artificial intelligence makes this requirement even more important. A system capable of recommending or executing actions needs more reliable context, not less.<\/p>\n\n\n\n Read how to use AI Sales Automation inside CRM with reliable data, clear processes and a direct connection to pipeline<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Explore Liminal\u2019s Artificial Intelligence services for Marketing, Sales, CRM and Analytics<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Diagnosis should begin with real cases rather than with platform configuration screens.<\/p>\n\n\n\n Select several recent leads and reconstruct their journey. Where did they come from? Which information was captured? When were they qualified? Which rule determined the handoff to Sales? Who received the record? How long did the first action take? Did the sales professional understand the context? Which outcome was recorded?<\/p>\n\n\n\n The same exercise should then be completed for won and lost opportunities.<\/p>\n\n\n\n Can the source be identified? Can Marketing understand which commercial result came from its activity? Are loss reasons structured clearly enough to produce learning? Are there opportunities without associated contacts or duplicated records?<\/p>\n\n\n\n The data model should also be reviewed. Similar properties with different meanings are a warning sign. The same applies to important fields with low completion rates, stages that are rarely used or statuses interpreted differently by different users.<\/p>\n\n\n\n Finally, the company should observe behaviour outside the CRM.<\/p>\n\n\n\n Which spreadsheets are still used? Which reports are prepared manually? Which information do salespeople look for outside the CRM before a meeting? Which data does Marketing still request from Sales by email?<\/p>\n\n\n\n These behaviours usually reveal where the CRM still fails to support the real operation.<\/p>\n\n\n\n Read why Sales and Marketing can remain misaligned after CRM and the operating changes required to fix the problem<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Revenue Operations helps turn this problem into a revenue architecture issue.<\/p>\n\n\n\n Instead of Marketing optimising lead generation and Sales optimising only deals, RevOps examines how people, processes, data and technology work throughout the complete revenue cycle.<\/p>\n\n\n\n This makes apparently technical questions, such as property ownership or system synchronisation, relevant to growth.<\/p>\n\n\n\n An incorrect field can change a segment. An incorrect segment can place a lead in the wrong campaign. An incorrect classification can trigger an early handoff. The wrong owner can delay the response. A poorly structured loss reason prevents Marketing from learning.<\/p>\n\n\n\n Revenue Operations connects these events into one operating view.<\/p>\n\n\n\n CRM then evolves from customer relationship management as a database into an operational infrastructure connecting Marketing, Sales and the other functions influencing revenue.<\/p>\n\n\n\n Read Liminal\u2019s guide to Revenue Operations and how RevOps connects people, processes, data and technology<\/strong><\/a>.<\/strong><\/p>\n\n\n\n The objective is not to create another department for the sake of organisational structure. It is to ensure someone is looking across the complete system rather than allowing each function to optimise its own part independently.<\/p>\n\n\n\n The issue has become even more important with the expansion of AI agents.<\/p>\n\n\n\n An agent may summarise an account, prepare a meeting, qualify a lead or recommend the next action. However, the quality of that answer depends on the context available.<\/p>\n\n\n\n If the CRM does not contain the latest meetings, the agent does not understand the current situation. If duplicates exist, it may interpret two records as separate organisations. If the lifecycle stage is outdated, it may recommend an inappropriate action. If Marketing data is not integrated, it loses previous intent signals.<\/p>\n\n\n\n Preparing the company for AI therefore begins long before selecting an agent.<\/p>\n\n\n\n It begins with CRM quality.<\/p>\n\n\n\n Companies should treat agents as a layer that uses existing processes and data rather than as a technology capable of automatically correcting their absence.<\/p>\n\n\n\n Explore how AI Agents can connect CRM, Sales, customer service and operational workflows<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Read the guide to Artificial Intelligence in CRM and how better data can support better commercial decisions<\/strong><\/a>.<\/strong><\/p>\n\n\n\n The first priority should be creating a common language. Marketing, Sales and management need to agree on what lead, MQL, SQL, opportunity, customer, disqualification and the other relevant statuses mean.<\/p>\n\n\n\n The next step is defining the ownership model. Every contact, company and opportunity needs a known logic, including the important exceptions.<\/p>\n\n\n\n The third step is reviewing the data model. Redundant fields should be removed, important values should be standardised and mandatory properties should be identified for each stage.<\/p>\n\n\n\n The handoff between Marketing and Sales then needs to be designed. The transition should include criteria, context, ownership, expected response time and a feedback mechanism.<\/p>\n\n\n\n Only after these agreements exist does it make sense to review integrations, dashboards and automation.<\/p>\n\n\n\n Automation should make sure the agreed process happens consistently. It can route leads, create tasks, update properties, send alerts, manage nurturing, identify delays and support reporting.<\/p>\n\n\n\n But every workflow should be able to answer one simple question: which business process are we trying to guarantee?<\/p>\n\n\n\n If that answer is unclear, the process may not yet be ready for automation.<\/p>\n\n\n\n Read the seven steps for implementing CRM and Marketing Automation around a structured operating model<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Correcting misalignment between Marketing and Sales rarely requires only one additional automation or a few new CRM properties. The organisation needs to understand how data enters the system, how it is interpreted, who takes responsibility, how leads move between teams and which information returns to Marketing after commercial follow up.<\/p>\n\n\n\n This connection between strategy, process and technology is where Liminal works.<\/p>\n\n\n\n As a consultancy specialising in MarTech, CRM, Automation, Analytics, integrations and Artificial Intelligence, Liminal helps companies design and implement a revenue architecture that enables Marketing and Sales to work from the same reality.<\/p>\n\n\n\n The engagement may include lifecycle review, qualification processes, lead scoring, ownership rules, pipelines, properties, data migration and cleansing, integrations, workflows, reporting and user adoption.<\/p>\n\n\n\n Liminal works across different ecosystems, including HubSpot, Zoho, Salesforce and Microsoft, which allows the analysis to begin with the business problem and requirements rather than the assumption that the existing technology needs to be replaced.<\/p>\n\n\n\n In some organisations, the existing CRM already has all the capabilities required. The problem lies in configuration or processes. In others, the current architecture cannot create a sufficiently integrated customer and revenue view, making it necessary to evaluate alternative approaches.<\/p>\n\n\n\n Explore HubSpot and the possibilities for connecting CRM, Marketing, Sales and Service in one platform<\/strong><\/a>.<\/strong><\/p>\n\n\n\n Explore Zoho and the integration and customisation possibilities across its business ecosystem<\/strong><\/a>.<\/strong><\/p>\n\n\n\n The platform is only one part of the decision. The implementation partner\u2019s ability to understand processes, data, integrations and adoption can determine the difference between a technically completed project and a genuinely integrated operation.<\/p>\n\n\n\nWhy do Marketing and Sales remain misaligned even with CRM?<\/h1>\n\n\n\n
1. The CRM does not have a shared lifecycle definition<\/h1>\n\n\n\n
2. Qualification criteria exist only in people\u2019s heads<\/h1>\n\n\n\n
3. Lead and account ownership is unclear<\/h1>\n\n\n\n
4. Marketing does not give Sales enough context<\/h1>\n\n\n\n
5. Sales does not return information about lead outcomes<\/h1>\n\n\n\n
6. Marketing and CRM data are not genuinely integrated<\/h1>\n\n\n\n
7. Historical data is dirty or incomplete<\/h1>\n\n\n\n
8. Opportunities are not connected to demand origin<\/h1>\n\n\n\n
9. The CRM does not record the activities needed to understand the process<\/h1>\n\n\n\n
10. Marketing and Sales still analyse different dashboards<\/h1>\n\n\n\n
11. Integration with other systems has no defined source of truth<\/h1>\n\n\n\n
12. The company automated before creating governance<\/h1>\n\n\n\n
Why automation should not be the first step towards alignment<\/h1>\n\n\n\n
How to identify CRM gaps between Marketing and Sales<\/h1>\n\n\n\n
The role of Revenue Operations in Marketing and Sales alignment<\/h1>\n\n\n\n
Unified data is also a requirement for artificial intelligence<\/h1>\n\n\n\n
How to correct CRM gaps before automating<\/h1>\n\n\n\n
Align Marketing, Sales, CRM and data with Liminal<\/h1>\n\n\n\n