How Nektar Automates Buying Committee Engagement RevOps 10 min In today’s complex B2B sales environment, understanding and engaging with buying groups is crucial for driving revenue. Modern B2B buying decisions are made by groups, not individuals so traditional methods, like tracking Marketing Qualified Leads (MQLs), are no longer sufficient. This shift necessitates a new approach to tracking engagement, one that leverages advanced technology to automate and streamline the process. Nektar offers a powerful solution for sales teams looking to stay ahead. By automating the tracking of Buying Group Engagement, Nektar’s features ensure that every interaction is captured and analyzed, providing deep insights into the buying process. Features such as Automated Opportunity Contact Role Creation, Conditional OCR, Intelligent Meeting Tagging, and Contact Participation (Meeting Intelligence) work together to create a comprehensive and automated engagement tracking system. In this blog, we will explore how Nektar’s advanced capabilities can transform your sales process, making it more efficient and effective by automating the tracking of Buying Group Engagement. We’ll delve into each feature, highlighting its benefits and real-world applications, to show you how Nektar can help your team achieve greater success in today’s competitive market. Understanding Buying Group Engagement Buying Group Engagement refers to the interactions and activities involving multiple stakeholders within an organization who collectively make purchasing decisions. Unlike the traditional focus on individual leads, Buying Group Engagement acknowledges the collaborative nature of B2B purchases, where various roles such as decision-makers, influencers, and end-users all contribute to the final decision. In modern B2B sales, it’s essential to track and manage these engagements effectively. Understanding who is involved in the buying process, their roles, and their level of participation helps sales teams tailor their strategies and communication. This approach not only enhances the relevance of sales efforts but also increases the likelihood of closing deals by addressing the needs and concerns of all key stakeholders. By automating the tracking of these engagements, sales teams can gain comprehensive insights into the buying dynamics, enabling them to engage more effectively and drive better outcomes. How Nektar Automates Buying Group Engagement Choosing From 5 Types of Sales Territory Mapping Nektar’s innovative approach to automating Buying Group Engagement leverages several key features that streamline and enhance the process of managing interactions with multiple stakeholders. Here’s a detailed look at how each feature contributes to a more efficient and effective sales strategy: 1. Automated Opportunity Contact Role Creation Nektar’s Automated Opportunity Contact Role Creation feature automatically identifies and assigns contact roles to opportunities within your CRM. This automation ensures that all relevant stakeholders are accurately documented and associated with each opportunity, reducing the manual effort typically required. Benefits: Time-Saving: Eliminates the need for sales reps to manually input and update contact roles, freeing up their time to focus on selling activities.Accuracy: Ensures that all contact roles are correctly and consistently assigned, reducing errors and improving data integrity.Visibility: Provides a clear view of all individuals involved in the buying process, helping sales teams better understand and manage their interactions. 2. Conditional OCR (Opportunity Contact Role) Conditional OCR allows for the creation of contact roles based on specific, predefined conditions. This feature enables sales teams to customize how and when contact roles are created, based on criteria that are most relevant to their sales processes. Benefits: Customization: Tailors the contact role creation process to fit the unique needs of different sales teams or business units.Efficiency: Automatically applies the right conditions for contact role creation, ensuring that only the most relevant contacts are included.Scalability: Supports the management of complex sales environments with numerous stakeholders and varying engagement scenarios. 3. Intelligent Meeting Tagging Nektar’s Intelligent Meeting Tagging feature automatically tags meetings with relevant information, making it easier to track and analyze interactions with buying group members. This feature leverages AI to identify key details from meetings and associates them with the appropriate contacts and opportunities. Benefits: Enhanced Insights: Provides detailed insights into the content and outcomes of meetings, helping sales teams understand engagement levels and follow up effectively.Consistency: Ensures that all relevant meeting information is captured and tagged correctly, enhancing the quality of data in the CRM.Productivity: Reduces the manual effort required to document meetings, allowing sales reps to focus more on strategic activities. 4. Contact Participation (Meeting Intelligence) The Contact Participation feature tracks and analyzes the participation of contacts in meetings. By monitoring who attends and actively participates in meetings, sales teams can gain valuable insights into the engagement levels of different stakeholders within the buying group. Benefits: Engagement Tracking: Identifies key influencers and decision-makers based on their participation and engagement in meetings. Actionable Insights: Helps sales teams tailor their follow-up strategies based on the involvement and interest levels of different contacts. Data-Driven Decisions: Provides a data-driven approach to understanding and managing buying group dynamics, leading to more informed sales strategies. How GuideCX got Visibility into $1.7Mn Inactive Pipe Every company wants to squeeze out every drop of revenue from its active pipeline. Missing out on achievable quotas and letting potential revenue slip away due to inactivity and poor engagement have become the biggest sins in Sales. But what if there was a way to bring these dormant deals back into the spotlight for the sales teams? This is the scenario we explore—a practical challenge met with a pragmatic question, setting the stage for the journey ahead. This is the story of GuideCX and how they transformed deal prioritization using process automation, powered by customized rules that considered the engagement data that Nektar captured in their CRM. This helped GuideCX gain instant visibility into $1.7M worth of inactive deals, which otherwise would have been lost forever.   Ready to revolutionize your sales process with automated Buying Group Engagement? Discover how Nektar’s advanced features can help your team capture every interaction, gain deep insights, and drive better outcomes. In this blog

Configure Contact Roles on Salesforce to Unlock Immediate Efficiency Gains Discover how defining OCRs can enhance visibility into your buying committee, improve sales execution, and boost win rates. RevOps 10 min Let’s begin by simply defining what is an opportunity contact role (OCR). An OCR is a standard object on Salesforce within the Opportunity object that links Contacts to Opportunities, specifying the Contact’s role in that Opportunity. Having great OCR hygiene means sales leadership teams gain better visibility into the buying committee for each opportunity. Better visibility helps monitor if reps have at least identified the necessary people needed to win the deal. If the necessary people are involved, then sales leaders can guide their teams on the right engagement playbook to navigate the deal toward success. While sales teams know the importance of identifying and engaging the entire buying committee, not many follow this through to execution. This becomes worse when we consider the OCR data available in a CRM. Open any CRM today, and you will notice that a majority would have an average of 3 contact roles. Of those 3, one is usually a required field made mandatory by the revenue/sales operations or CRM admin. In companies that practice MEDDIC (and its variations), the ‘Economic Buyer’ and ‘Champion’ are identified and added to the CRM, but the remaining buyer roles are either identified but not added to the CRM or not identified at all. So why should OCRs matter? The answer to this question lies in whether or not you’re working on improving sales execution, rep efficiency, win rates, and forecasting. You’d be surprised if we told you how often we hear prospects say “We have no idea who our sellers are talking to” or “We don’t know how often we’re engaging buyers in open deals”. ‘Who’ you are talking to and ‘how often’ are you talking to buyers are the fundamental units of generating revenue. The ‘process’ of generating revenue can only be improved by tracking and measuring such fundamental units. You may be doing a great job with creating contact lists from third-party data tools like Zoominfo or Lusha or by auto-creating contacts in accounts with tools like Clari or Gong, but if such contacts are not being linked to opportunities, you are losing out on critical data. Technically, in CRM terms, opportunities are won, not accounts. And so having contact data is not good enough. You must aim to have granular and comprehensive contact role data. Introducing Configurable OCRs Using AI, automation, and graph inference, Nektar automatically creates contacts in the relevant accounts present in Salesforce. Until recently, Nektar would automatically associate these contacts as OCRs within the relevant open opportunities. There was no configuration needed. However, through customer feedback and research, Nektar is excited to announce ‘Configurable OCR’. An OCR record is only useful if it is: associated because it is actually involved in the deal a buying role was identified and assigned to it With configurable OCR, you can define rules using buyer-seller engagement data that Nektar has already added to the (open) opportunity and account. For example, a rule can be: “If engagement with contacts in an account is more than 5 times in the last 10 days, and if there is an open opportunity in those accounts, then associate such contacts as opportunity contact roles.” This example considers the recency and frequency of buyer engagement. So, only those contacts that are frequently engaged by the seller will get added to opportunities as OCRs. As a result, sales leaders gain instant visibility into who is actually involved in deals. This is just a simple, straightforward example of a rule. You can define your own rules. Additionally, you can customize the rule for the different segments you may have. For example, have a rule specifically for strategic accounts, expansion accounts, new business accounts, vertical-specific accounts, or any other segmentation you may have. Next, you can configure the second component – the buying role. If you’ve used Nektar, you would know that it extracts job titles from email signatures. A default capability we’ve always offered is to map out job titles to the respective buying roles. With this one-time configuration, as and when Nektar links OCRs, it also assigns a buying role to the OCR based on the corresponding job title. Now, using genAI automation you can define rules to assign an appropriate buying role. You can consider a combination of job titles and engagement trends, job titles and seniority, job titles and engagement and segment – whichever factors address your requirements. After all, the process of generating revenue is unique to a company. The best part is that all this is done using the standard Salesforce records, so they are easily reportable on Salesforce. This can also be achieved for your historical opportunities by backfilling them. Benefits of configurable OCRs Nektar customers use this OCR data for deal inspections, win-loss analyses, playbook optimization, and enhancing their multithreading strategy. Every opportunity has only those contact roles that are involved in the deal while the remaining stakeholders such as legal remain in the account as contacts. So sales leaders are able to monitor which job titles and buying roles are being engaged. Since historical data is also plugged in, you can study buying committee engagement for won and lost deals to analyze what worked and did not work. Some of our customers identified new personas in their closed deals, and have now started prospecting this persona actively to generate new pipeline. By studying won deals, you can also track the engagement pattern and work towards improving your multithreading strategy. Lastly, playbooks can be transformed. For example, one of our customers now has made it mandatory to have a specific number of contact roles if the deal is in stage 3 of the sales process. Similarly, answers to who, how often, and when should different people of the buying committee be engaged can be detailed out. Outside of the sales team, a clean and

Top 5 Trends That Will Impact Sales Operations in 2025 RevOps 10 min Sales operations has become one of the fastest growing functions over the last few years. According to LinkedIn’s State of Sales Operations 2021 Report, the number of sales operations professionals increased by 38% around the world between 2018 and 2020. What’s the reason behind this growth? Bradley Gray, Director of Business Development at Enterprise Holdings attributes two reasons for the growth in this role. There has been a significant increase in the amount of data that gets generated within organizations. The proliferation of data is creating a need for sales operations to generate contextualised insights for sales teams to succeed.   RELATED RESOURCE : SALES OPERATIONS TRENDS FOR 2025   A well run sales operations function enables businesses to operate efficiently with data-driven decisions, and also identify gaps that exist in the sales process, and help fill them up through analytical insights.  In short, a great sales operations function can help an organization unlock massive productivity gains.  To make the most out of your sales operations function, it is important to be aware of the trends that will shape in 2025 (and beyond). Let’s take a look. 1. Multithreading Will Be a Key Sales Tactic The world is in the midst of a great reshuffle for talent. The turnover among corporate director-level-and above, that constitutes the majority of B2B buyers, increased by 31% in 2021. With key people in the B2B buying committee quitting jobs so often, many deals fall apart because reps fail to develop strong relationships with more than one buyer. And with an average of 6.8 decision makers in every B2B purchase, not having strong relationships with all of the key players within the buying committee can be a big risk. When a key stakeholder leaves the organization, reps are forced to start from scratch, causing 80% of them to lose deals. Having just one primary contact for an account, or single-threading, thus increases the chances of missing out on deals. This is where adopting multithreading as a sales practice becomes extremely crucial. Multithreading is when reps form relationships with multiple stakeholders on the buying committee of an account.  This way, even if the primary stakeholder quits the organization, reps can capitalize on the relationships they have with the rest of the stakeholders within that account. Multithreading increases the chances of closing a deal by 16%. Successful sales teams in 2025 will master multithreading by gathering champions, influencers and decision-makers, and engaging with them on a regular basis. 2. An Increase in Regulations Will Impact Tech Stack Decisions There has been an increase in the number of regulations across the globe around protection of consumer information and data privacy.  Non-compliance of these regulations can be a huge cost. Organizations lose an average of $4 million in revenue due to a single non-compliance event.  To prevent such events from taking place, sales and revenue leaders must narrow down on their tech investments from a compliance-first lens. With the world increasingly moving towards a cookie-less world, highly compliant first-party data will become key in helping sales teams make data-driven decisions. First-party data is the information that is handed off with consent from a user to a company. This can be from sources like email, calendar, Zoom or other tools that buyers use.  For example, organizations can use their own first-party data to drive contextual insights that can help them make their sales operations function more efficient, while staying compliant with GDPR regulations.  Forward thinking leaders will realize this and take control of their first-party data in 2025, and use it to make powerful data-driven decisions.  Technologies like artificial intelligence can help enrich CRM with first-party buyer and seller interaction data. Nektar has built an advanced data capture solution that intelligently connects first-party data to the CRM and enriches it for sales teams. 3. AI Based Guided Selling Will Help Sellers Win More Deals B2B sales is getting increasingly complex, with buyers getting bombarded with information across channels, and sellers tackling multiple tasks and responsibilities while chasing their quota. AI based guided selling is helping sellers navigate this complex selling environment by helping them improve their sales execution through a data-driven approach.  Along with increasing productivity, AI based guided selling helps identify patterns that lead to more intelligent business decision making, ultimately helping in revenue generation. The pandemic exposed cracks in many organization’s sales processes. Knowing that sales process discipline must be improved to increase the chances of closing new deals, sales leaders are investigating new data-driven, AI-based guided selling functions for improving sales execution. Tad Travis, VP, Gartner AI-based guided selling offers prescriptive as well as predictive insights to sellers to close more deals.  From a prescriptive lens, it recommends the next best actions for sales reps and managers to undertake within the sales process. As an example, organizations can use AI to improve their playbook compliance within teams for consistent selling.  From a predictive lens, it offers insights that help identify lead indicators to make the sales process more efficient.  For example, by having insights on the activity data of sales reps, sales managers can define which deals are real and which need to be eliminated from the pipeline. With such functionalities, sales teams can decide on what to do next to move a relationship, deal or quote forward on the basis of analytics (rather than relying on instinct to make decisions). 2025 will see organizations add AI based guided selling solutions to their tech stack. 4. Training in Consultative Sales Will Take Priority Today’s B2B buyers prefer to conduct their own research before they speak with sales reps.  According to research, most buyers engage with more than 13 pieces of content before connecting with a seller.  Forrester’s research found that buyers went to all forums for information in 2021 – from webinars and online events to learn about the category and competitors, to speaking with peers and industry experts to have their questions answered. These changes in buyer preferences have raised the bar for sales. Understanding the buyer’s intent and offering them personalized solutions

What is Predictive Sales Analytics? Sales When it comes to weather forecasting, experts use historical weather patterns, current atmospheric conditions, and advanced technology to predict future weather. They anticipate whether it will rain, snow, or be sunny. The better their data points are, the better their predictions.  Similarly, Predictive Sales Analytics ​​examines past sales data, market trends, customer behaviors, and other factors to forecast future sales outcomes. This allows organizations to make informed decisions, allocate resources effectively, and tailor their sales strategies, much like how people adjust their plans based on the weather forecast to ensure a smooth day ahead. Let’s have a detailed walkthrough. What is Predictive Sales Analytics? Predictive sales analytics uses data, statistical algorithms, and machine learning techniques to forecast future sales outcomes and trends. The approach employs statistical modeling, data mining methods, and machine learning to detect patterns within diverse datasets. Predictive sales analytics studies historical sales data, customer behaviors, market trends, and other relevant factors, applying sophisticated mathematical models to forecast upcoming sales outcomes.  Predictive sales analytics has several features, including: 1. Data collection and preparation The process begins by collecting relevant data from multiple sources. This can include historical sales data, customer interactions, website activity, marketing campaigns, and external market trends. The data is then cleaned and organized to ensure accuracy and consistency. The quality of the data directly impacts the accuracy of predictions. 2. Feature selection In this step, the most important variables, also known as features, are identified. These features can include factors such as time of year, customer demographics, purchase history, marketing channel, and economic indicators. 3. Model building Predictive models are built using statistical algorithms and machine learning techniques. These models use historical data to establish relationships between the chosen features and the target variable, which is typically future sales. 4. Training and testing The built model is then trained using a portion of the historical data. This training process involves adjusting the model’s parameters to minimize errors and improve accuracy. The model’s performance is evaluated using testing data that the model has not seen before. This step helps ensure that the model can generalize its predictions to new data. 5. Prediction and analysis Once the model is trained and validated, it is ready to predict future sales. It takes new data inputs, such as current market conditions and customer behaviors, and generates forecasts based on established patterns. 6. Actionable insights The predictions generated by the model provide actionable insights for businesses. These insights can include forecasts of future sales volumes, identification of high-potential leads, recommendations for optimizing marketing campaigns, and insights into product demand. 7. Iterative refinement Predictive sales analytics is an ongoing process. As new data becomes available, the model can be retrained and refined to improve its accuracy and relevance. This ensures that the predictions remain aligned with changing market dynamics. 8. Decision-making and strategy formulation Businesses use the insights from predictive sales analytics to inform their decision-making and shape their sales and marketing strategies. For example, they can allocate resources based on anticipated sales trends, tailor marketing efforts to specific customer segments, and optimize inventory management. Now that we have understood the process of predictive sales analytics, let’s have a look at the companies that are using this tool.  Which Companies Use Predictive Sales Analytics? Predictive sales analytics is embraced by a diverse range of companies spanning various industries. These companies want to transform raw data into actionable insights, enabling them to anticipate customer behavior, allocate resources more efficiently, and make well-informed decisions. Let’s see how different companies across industries are using predictive sales analytics:  1. Retail Industry Retail companies use predictive sales analytics to forecast demand for products, optimize pricing strategies, and manage inventory levels. This helps them avoid stockouts and overstocking, ensuring they meet customer demands effectively. 2. Financial Services Banks and financial institutions employ predictive sales analytics to assess credit risk, detect fraudulent activities, and make informed lending decisions. This results in reduced financial losses and improves the quality of loan portfolios. 3. Healthcare Sector Healthcare organizations leverage predictive sales analytics to predict patient trends and allocate resources efficiently. It aids in optimizing hospital operations, patient admissions, and resource utilization, ultimately leading to better patient care. 4. Technology Companies Technology firms utilize predictive sales analytics to analyze customer behavior and preferences, enabling them to tailor their products and services. This helps increase customer satisfaction and loyalty. 5. Manufacturing Sector Manufacturing companies apply predictive sales analytics to optimize supply chain processes, anticipate equipment maintenance needs, and enhance production efficiency. This minimizes downtime and maximizes productivity. 6. Automotive Industry Automotive companies use predictive sales analytics to anticipate market demand for specific vehicle models, allowing them to adjust production and marketing strategies accordingly. 7. Telecommunications Telecommunication companies employ predictive sales analytics to analyze customer usage patterns and predict churn rates. It helps them design targeted retention strategies and personalized offerings. 8. Hospitality and Travel Hospitality businesses utilize predictive sales analytics to forecast occupancy rates, optimize room pricing, and enhance guest experiences by tailoring offerings based on customer preferences. 9. Energy Sector Energy companies leverage predictive sales analytics to predict energy consumption patterns, optimize energy distribution, and plan maintenance activities for power infrastructure. 10. Consumer Goods Consumer goods companies apply predictive sales analytics to identify consumer trends, anticipate shifts in demand for different products, and optimize marketing strategies. 11. Real Estate Real estate firms use predictive sales analytics to analyze property market trends, anticipate property values, and optimize pricing strategies for rental and sales properties. 12. Pharmaceutical Industry Pharmaceutical companies utilize predictive sales analytics to forecast demand for medications, optimize inventory levels, and align production with market needs. With so many companies using predictive sales analytics, it is important to understand what it actually does? How does it affect processes, and why is it so important?  Why Is Predictive Sales Analytics Important? Predictive analytics software can handle complex and demanding data processes behind the scenes, allowing sales professionals to concentrate on their core strengths. In simpler terms, predictive sales analytics assists sales professionals in transforming a potentially overwhelming volume of data into straightforward, comprehensible insights that serve the

Tech Stack Consolidation for Revenue Teams: Streamlining Efficiency and Productivity in 2025 Sales Sales Tech Stack The entire customer journey, from lead to opportunity to revenue to retention is riddled with complexities.  Buyers are well-informed about a product and its competitors way before they even book a demo.  Sales cycles are longer with multiple stakeholders influencing the buying decisions at each stage of the customer journey.  Customer churn is at an all time high. To successfully close deals and generate revenue, GTM teams today need to be hyper fluent with customer pain points. And be able to offer tailored solutions to them at their time of need.  This is why revenue teams need technologies at their disposal. Which can help them act on insights that can enhance the customer experience. And lock in more revenue every quarter. While investing in these tools is table stakes to achieve revenue targets, the concept of “the more the merrier ” does not work here.  While it might be tempting to solve a problem with the shiniest new software out there, organizations are realizing that this can actually drain the productivity of their teams and lead to a myriad of inefficiencies. Ep #11: Components of a Modern Sales Tech Stack Bloated tech stacks can create many problems in disguise and add to a lot of hidden costs such as cost of integrating, tool fatigue, cost of siloed data and much more. Which is why 2024 is the year of tech stack consolidation. 62% of businesses are trying to cut down the number of tools they use and trim the excess fat from their tech stacks. In this blog, we will explore what revenue tech stack consolidation means. And how you can go about approaching it. Before we get into what tech stack consolidation means, let’s take a quick look at what led us here.  More Tools Don’t Mean More Revenue A whopping number of tools are being added to the modern revenue technology stack. SaaS organizations use an average of 130 applications! But only 53% of users say their technology aids productivity and positively impacts results.  According to another survey, 57% of marketing leaders, who had over 20 tools in their tech stack, strongly doubted if they’d reach their ROI goals.  It’s clear that the promise of efficiency gains and higher productivity that these tools claim for GTM teams are often not met. Instead, it leads to enormous amounts of tech debt for businesses.  And an ever increasing stack of tools only adds to the complexity of daily operations, causing revenue to leak across various points along the customer journey.  Here are some of the ways too many tools lead to loss of revenue opportunities: A web of fragmented tools adds to complexity in day to day operations. Tools are siloed in multiple systems which makes it difficult to track data, draw insights from it or keep it consistent and secure. No unified view of data leading to misalignment among GTM teams Overwhelmed employees who spend nearly 70% of their time on painstaking administrative tasks such as updating spreadsheets, adding prospects to CRM and augmenting data. These damaging effects of too many tools have increased the need for a leaner tech stack. Revenue leaders are taking a critical look at their existing tech stacks. And figuring out how to trim the fat without losing out on any core functionality. This is what we call tech stack consolidation. What is Tech Stack Consolidation? Tech stack consolidation is the process of reducing the number of tools in a company’s tech stack by merging functionalities into lesser and more exhaustive platforms. The goal of consolidation is not to knock down all of the investments in point solutions that already exist. It demands a structured approach in analyzing which tools offer real value for GTM teams. And eliminate tools that don’t add any merit to their day to day workflows. More tools in the tech stack add the burden of deployment, management and adoption. The goal for revenue leaders is to evaluate what they have, consolidate wherever they can and optimize their tech stacks to improve productivity and execution across every role. Consolidated technology seems to be the recipe for winning teams as per this survey by Sales Hacker. Teams that use tech stack that enable the full sales motion, from creating pipeline to closing deals are more likely to meet their revenue goals. A lean and fully capable sales tech stack is a reality as companies look to consolidate vendors while retaining the features and capability of their previous array of point solutions.  With the need for efficiency and productivity rising, let’s discuss an evaluation framework that can help you consolidate your tech stack. Evaluation Framework for Tech Stack Consolidation Too many tools add a lot of costs to a business. These costs go beyond the price on the invoice.  Poorly implemented and managed tech can lead to a lot of soft costs which arise from frustration in using the tools, poor adoption, implementation and maintenance and other issues that eat up the precious time of revenue teams.   Making changes to a tech stack is a delicate affair and needs to be handled with caution. It’s important to have all the necessary information at the very beginning to make informed decisions.  Here is a step by step evaluation framework to consider for a tech stack consolidation.  Step 1 – Identify your biggest challenges along the customer journey As a first step, inspect where your problems lie. Ask yourself: How can you boost productivity and efficiency at every stage of the customer journey with your existing tech stack? Are your marketing, sales and customer success teams meeting their targets? Do they have the right tools at their disposal? If not, what can make them more productive? Do you have clear visibility into the deals in your pipeline? If not, why? Identify where frictions exist in your customer journey. Is it at the very beginning of the funnel when a lead enters your CRM?  Is it when an opportunity needs to be handed over to customer

9 Best Account Based Marketing Tools for 2025 Marketing RevOps The rapid growth of Account Based Marketing (ABM) tools proves a couple of things. One, you can’t please everyone – not in today’s competitive B2B market.   And two, you better focus on the markets that really matter. But running multi-channel ABM campaigns at scale is no joke.  That said, the latest ABM tools make the job a lot easier.  They turn you into a master strategist with real-time insights at your fingertips. Let’s look at the top ABM tools that you can consider in 2025. What is ABM? Account Based Marketing or ABM flips the traditional marketing funnel on its head. In it, marketing and sales work together from the get-go to: Identify high-value accounts that fit ICP criteria, Engage them with personalized content   Find key decision makers Lead them to closure Stay engaged to tap future opportunities ABM is not about one-and-done selling.  Rather it drives customer lifetime value. And there have been some incredible success stories here. 9 ABM Tools for 2025 1. 6 Sense Revenue AI 6Sense allows marketing teams to identify high-value accounts, predict their buyer journey based on account activity and engage them with the right messaging across touch points. Features: Updates contact lists automatically with additional information Segments users into cohorts based on specific behavior patterns Can track user activity across channels and attribute to the respective account. Pricing:  Varies based on the number of users, use case, etc. 2. Demandbase Demandbase comes with a comprehensive suite of features built around the Account-Based Experience concept.  It has three modules – ABX Cloud (ABM), Advertising Cloud (campaign management), and Data Cloud (integration support) Features: Provides account-level insights for campaign execution and orchestration Supports multiple ad formats allowing global reach Easy integration Pricing:  Varies based on the use case, number of users, etc. 3. Terminus Terminus offers full-funnel ABM capabilities including account management, execution, and reporting. It is designed for multi-channel campaigns and offers easy scalability. Has its own built-in database. Features: In-depth segmentation options including buyer intent Supports campaigns across multiple channels Pricing:  Varies based on the number of users and features needed 4. RollWorks RollWorks is a good fit for marketers who rely primarily on paid ads for lead generation. Features: Provides useful targeting recommendations based on historical campaign data Maps account to key decision-makers and present their contact information Provides contextual client information such as organizational changes, mergers, acquisitions, etc Pricing:  The Starter plan is $975/mo. For information on their other plans, contact customer support.  5. Triblio If personalization is a priority for you, Triblio could be the perfect solution.  Its USP is predictive orchestration which automates segmentation and targeting for higher personalization. Features: Visual campaign builder with drag-and-drop functionality Calculates conversion probability based on user intent and account activity Pricing:  Varies based on the number of features, use case, etc. 6. Vainu This is a sales intelligence tool that lets you find high-value accounts from its extensive global database. Vainu speeds up list building and provides contextual information on target accounts. Features Creates targeted contact lists based on ICPs and filters chosen Updates new contacts automatically Provides a single view of contacts stored across tools Pricing:  Free trial Team plan: EUR 4200/yr Business plan: EUR 9900/yr Global plan: EUR 12000/yr Custom Enterprise plan 7. Apollo.io Apollo offers prospecting, campaign orchestration, and sales engagement capabilities.  It lets you push personalized messages to target contacts and optimize your revenue strategy as you go along. Features: Lead database of 250m people and companies Engagement Suite for creating personalized marketing sequences based on ICPs Intelligence Suite with tracking, analytics, and targeting capabilities Pricing: Basic: $49/mo Professional: $99/mo Custom enterprise plans Free plan with limited features 8. Uberflip Uberflip is a content creation, curation, and distribution platform that helps marketers deliver relevant messaging. Features: Aggregates and serves personalized content based on intent data Built-in analytics for measuring content performance and strategy planning Pricing:  Varies based on the number of users and features 9. Alyce Alyce is an AI-powered gifting platform with ABM as a key use case.  It provides personalized gifting options for marketers to engage, convert and retain customers.  It offers gift tracking, budgeting, and reporting capabilities. Features: Comes with CRM integration and inventory management features Built-in analytics for monitoring spend, conversion, and ROI So How Exactly Does ABM Work? In ABM campaigns, marketers think of buyers not as individuals but as companies.  They work with sales to identify target businesses, nurture contacts (in buying teams), and engage key decision-makers. Both teams will work together to create personalized messaging for each key prospect to drive purchase intent.  This can include unique sales pitches aimed at dramatically increasing the odds of conversion. ABM may involve a lot of research and coordination but the returns (deal size) are well worth it. What are the Big Challenges of ABM? ABM comes with its own set of challenges. Let’s look at some of the big ones: 1. Poor sales and marketing alignment Sales and marketing alignment is crucial for succeeding at ABM. The challenges are many: Both have different KPIs and processes Incomplete or inaccurate lead data Reporting issues These problems can be mitigated by: Integrating workflows so that both teams can function effectively   Syncing campaign management and reporting 2. Flawed key account mapping Many ABM practitioners fail to find the right contacts which affects campaign performance.  The solution? Put buyer behavior in context with their organizational structure and business goals.  Caveat: you have to be looking at the right data points. 3. No clarity on attribution metrics Finding the right attribution metrics is another problem B2B teams struggle with.  Firstly, the raw customer/activity data may be unstructured as the number of channels increases. Then there are multiple buyers with different needs. For these reasons, aligning marketing and sales attribution for ABM is hard. The right ABM tools can make things much easier though. 4. Lack of scalability ABM is high-touch marketing.  It requires relevant messaging at scale – a tall order if you have a large audience.  The solution: create a base template of

CRM Data Cleansing: How to Keep CRM data Pristine CRM On average, in a B2B company, the volume of prospect and customer data doubles every 12-18 months.  This massive influx comes with a significant risk of errors, duplicates, outdated records, and other inconsistencies creeping into your CRM. The longer this dirty data sits in your CRM, the worse the outcomes of your CRM would get much like a snowball rolling downhill. What can you do?  Implement robust CRM data cleansing practices. These practices include conducting regular CRM data audits, using CRM data validation techniques, and leveraging automated tools to keep your data clean and credible.  Ep #12: Driving Revenue With Clean CRM Data ft. Jacki Leahy In this article, we will learn all about CRM data cleansing and how automation and AI ease the process of data cleansing.  Let’s get started with the basics.  What is CRM Data Cleansing? Imagine you are going on a trip and need to pack your stuff. Will you be able to do it in a messy room full of stuff scattered everywhere? You’d spend ages just trying to find your things, let alone packing them efficiently. But when everything is neatly organized, you can do all the packing in a fraction of the time. Well, CRM data cleansing is similar. It’s all about taking that chaotic, messy data and transforming them into well-organized and accurate datasets.  In a nutshell, CRM data cleansing is the process of identifying and fixing inaccurate or incomplete data in your CRM database. It involves detecting and eliminating duplicate, outdated, or irrelevant data, ensuring that the CRM database remains accurate and up to date informing smarter and reliable business decisions. How Poor CRM Data Hygiene Affects Your Revenue For great results, you need to be careful of what you feed your CRM system. Or else, it becomes a classic case of “garbage in, garbage out.” You cannot expect great results from CRM insights if the source of the data in it is unreliable.  Data lies at the heart of gaining visibility on where to make improvements, drive focus on leading indicators and fix the revenue funnel before it breaks. If the data in the CRM in itself is plagued, you cannot expect insights from it that drive revenue.  In fact, it is quite the opposite. In a recent survey, 44% of respondents estimated their company loses over 10% of annual revenue due to poor data quality. Such data inefficiencies are causing revenues to leak through your funnel in myriad ways.  Some of them include: 1. High employee turnover CRM users aka your employees are reaching a saturation point. 64% of them say they would consider leaving their current role if organizations don’t invest resources in a CRM data quality plan. In a world where talent is scarce, employees leaving would mean so much more time and resources gone in hiring more people, onboarding them and keeping them engaged. 2. Poor sales forecasting The quality of your sales forecast has a direct impact on your revenue. A poor sales forecast is a result of bad data fed into your systems that fail to predict how much revenue will be closed quarter after quarter. The result is wasted resources on avenues that lead to no result. 3. Poor ROI from tech stack Revenue leaders invest in different tools as a part of their tech stack, CRM being one of them. But all these tech stacks can only deliver ROI when they have the right data to work with. Without quality data, these tech stacks will just remain as shiny objects that eat up budgets without delivering any meaningful value to revenue.  4. Poor targeting Picking up all contacts from a CRM and running a uniform campaign for all is passe. Today’s customers want hyper-personalized messaging, which requires marketing teams access to high-quality data that tell them more about their contacts than simple name and email ids.  CRM data tells marketing teams who to target for their campaigns. It fails to address the “why.”  Bad data aggravates this problem by sending wrong messages to the wrong customers for solutions they might not even be looking for, thus putting the reputation of a brand on the brink of damage. Advantages of CRM Data Cleansing  Leverage the power of a clean CRM to drive business growth in the following ways:  1. Effective prospect communication Clean data ensures you reach the right people with the right message at the right time. By having accurate contact details, preferences, and purchase history, you can personalize your follow-ups, build effective marketing campaigns, and provide exceptional customer service. 2. Improved productivity Outdated or incorrect data leads to wasted time and effort. We are not the only ones saying this, a report by Mckinsey says that employees spend 9.3 hours a week simply searching for the data they need.  By keeping your CRM clean, you avoid redundant tasks, such as contacting the same leads multiple times or dealing with undeliverable emails. It streamlines your processes, increases efficiency, and allows your team to focus on what matters most—building valuable customer relationships. https://youtu.be/-Zi6T1Ny9jI 3. Improved conversion rates 78% of businesses say that the data they collect helps them increase customer acquisitions and lead conversions. Reliable CRM data enables your sales team to target the most promising leads and opportunities. By eliminating duplicates, outdated leads, or invalid contacts, businesses optimize their sales efforts, increase conversion rates, and close deals more effectively. 4. Cost savings Maintaining clean data prevents unnecessary expenses. By avoiding mailing or marketing to incorrect or inactive contacts, you save on time and costs. Additionally, you reduce the risk of penalties associated with non-compliance, such as sending messages to individuals who have opted out.  Here’s something to cement our claim, data quality issues can cost a lot of revenue around 1/5th of the sales to be precise. 5. Better customer segmentation  Clean CRM data allows you to segment your customer base effectively. By organizing and categorizing customers based on accurate data points like demographics, purchase history, and preferences, you can create targeted marketing campaigns and personalized

Your CRM Contact Data is the Key to Drive Sales Efficiency. Here’s How. Sales Activity Capture Driving sales efficiency remains the ultimate goal for most businesses. Going after the right deals, building long-lasting relationships with multiple buyers in an account is key. This evolving landscape demands sales teams to be agile, strategic, and, most importantly, armed with the right data.  And at the heart of this data-driven revolution lies B2B contact data – a crucial element in the arsenal of sales teams striving for success. Having access to contact data can open several doors for sales teams.  Organizations already have a wealth of contact data. Unfortunately, they are trapped across various applications and tools in siloed Go-to-market (GTM) systems. Think of crucial data trapped in phone calls, emails, meetings, calendars and countless other tools that your reps use.  The CRM, intended as the central hub for this information, often becomes a repository of incomplete, inaccurate, or rapidly decaying data. Manual updates are sporadic, and the consequence is a compromised ability to engage effectively with prospects. The result? Critical data about prospects and buyers never make it to the CRM. And your sales team ends up targeting the wrong people and chasing the wrong deals. That’s the opposite of efficiency.  Merely having a vast pool of contact information is not enough. Tracking the dynamic relationship between sales teams and prospects is table stakes. Every interaction – phone calls, demos, emails, and notes – needs to be meticulously recorded. And shared in a unified manner for sales and marketing teams to be on a common page.  Enter AI-led Contact Data Automation The solution lies in automating contact data capture into CRMs and deploying Artificial Intelligence (AI) for real-time actionable insights. This not only streamlines the process but also ensures that the data is consistently accurate. AI provides a unified, comprehensive view of prospects, enabling sales teams to deliver an exceptional buyer experience at every stage of the journey. Benefits of AI-led Contact Data Automation Introducing AI-led automation into your contact data strategy can yield several benefits across the buyer’s journey. Let’s look at the top ones: 1. Clean, Complete, and Up-to-Date CRM Data AI can automate the process of collecting, cleaning, and updating contact data, ensuring that your CRM is always equipped with accurate and current information. This improves the reliability of your data and allows for more effective communication with leads and customers. 2. Automated Manual Tasks AI can take care of time-consuming tasks like data entry, freeing up valuable time for your sales team. This allows them to focus on more important tasks, such as building relationships with leads and closing deals. 3. Actionable Insights AI can analyze contact data and provide actionable insights about leads and buyer behavior. This can help sales teams understand their target audience better, tailor their sales strategies, and improve the effectiveness of their campaigns. 4. Predictive Analytics AI can use historical data to anticipate future trends and behaviors. This allows sales teams to stay ahead of the competition by identifying potential opportunities and risks in advance. 5. Unified View of Deals and Activities AI can provide both sales and marketing teams with a unified view of data, allowing for better collaboration and alignment. This ensures that both teams are working with consistent and accurate data, leading to more effective campaigns and sales strategies. Use Cases for Sales Efficiency From the first point of contact to nurturing long-term customer relationships. AI-led contact data automation can address various gaps in the sales process. And real-time insights into deal status and buyer behavior can become the foundation for driving sales efficiency. Let’s look at some of our top use cases that contact automation can drive for sales: 1. Contact-Level Engagement Insights for Effective Multithreading Pushing deals from creation to closed-won requires strategically engaging every member of the buying committee. Quite often, sales leaders do not have visibility into who their sales reps are engaging in every deal. They rely on a download from the deal owners during the pipeline review calls. But almost always, most deal owners simply talk about champion and/or economic buyer engagement. It’s rare to discuss buying committee coverage, which can have 10-30 members involved if we consider enterprise deals. What makes this worse is that the opportunities in CRM would also only show 2-4 contacts. Sales leaders and managers continue to be in the dark. They don’t know what they don’t know. By automating contact data capture, sales leaders instantly get visibility into buying committees. Combining this with good Salesforce reporting, they can gain visibility into the depth and breadth of engagement with each member of the buying committee. Such insights enable sales leaders to guide deal owners to execute more effectively. While this is the age of AI, the truth remains that people buy from people, especially in high-touch B2B sales. This mandates reliable visibility into the strength of buyer-seller, or rather buying committee-seller relationships. Each buying committee member is like a door. All doors lead to the same destination, in this case, that destination is ‘closed-won’. But the path to closed-won can vary behind the door. Some doors can open long paths, some short. So every door must be opened because you never know which door has a shorter and quicker path to achieving a closed-won deal. 2. Sales-Marketing Harmony for Account-Based GTM If you’re a sales leader or an operations leader planning an account-based GTM strategy, ask your marketing counterparts if they have unprecedented access to clean and complete contact data. There’s a very high chance that their response will be ‘limited’. And don’t be surprised or offended if they blame your sales reps for not adding contacts to the CRM. Your marketing teams are forced to rely on third-party data vendors for contact data, when in reality, there’s an abundance of those in your sales reps inboxes and calendars. The result? Irrelevant marketing campaigns due to poor audience targeting. This is where sales reps start blaming the marketing team for poor air-cover.