What is Salesforce Tech Debt? And How Can You Reduce it? RevOps 10 min In a recent statement, Salesforce disclosed its intention to raise list prices across several product offerings, including Sales Cloud, Service Cloud, Marketing Cloud, Industries, and Tableau, with an average increase of 9%. Starting in August 2023, the revised list prices for certain Salesforce products will be implemented, targeting new customers and existing customers acquiring additional cloud services. Salesforce has outlined the following adjustments in pricing for their fundamental Sales and Service Cloud offerings: Professional Edition will increase from $75 to $80 Enterprise Edition will increase from $150 to $165 Unlimited Edition will increase from $300 to $330  These changes are going to affect a lot of organizations. Over 150,000 companies across industries use Salesforce. With specialized solutions for operations across sales, service, marketing, and commerce, it is no wonder that the hike in price would affect almost every industry. For companies using Salesforce, the major concern is: Salesforce Tech Debt.  What is Salesforce Technical Debt? Technical debt refers to the expense of having to do extra work later on due to opting for a quick solution in the present rather than investing the time in a more optimal approach. This concept is also commonly referred to as “Shift Left,” which emphasizes the idea that the sooner you identify and address issues, the more cost-effective it is to resolve them. Technical debt represents the additional effort required to rectify a hasty, less-informed solution chosen in the present (constructed quickly without a deep understanding of business requirements), as opposed to adopting a more time-consuming but superior approach. In a broader sense, technical debt encompasses any customizations, whether through code or declarative means, that were implemented when standard functionality wasn’t suitable or accessible. Technical debt can also contain situations where solutions were initially designed for a specific purpose, but as business needs evolved over time, small adjustments were tacked on. A more constructive perspective on technical debt is to recognize that virtually everything can be considered a form of technical debt, but it’s termed “debt” because it necessitates future efforts to address and resolve. In the past, technical debt was primarily associated with developers taking shortcuts in their code. However, in the era of low-code platforms such as Salesforce, technical debt can arise not only from coding decisions but also from the configuration choices made through user-friendly “clicks” within the platform. What Causes Tech Debt? Salesforce technical debt arises from rushed or suboptimal development practices, including quick fixes, inadequate adherence to best practices, complex customizations without proper planning, and neglect to update and adapt solutions over time. This debt accumulates when shortcuts are taken, making future maintenance and scalability more challenging and costly. Here are a few factors that can cause technical debt: 1. Modified or outdated design This occurs when the business requirements change, rendering certain functionalities unnecessary. However, it’s often deemed safer to retain these functionalities. 2. New releases This arises when the introduction of new platform features surpasses the capabilities of previous releases or custom development efforts. For instance, Salesforce Flows are taking precedence over process builders and workflow rules. 3. Intentional technical debt When a deliberate decision is made to expedite development, fully aware that it will entail higher long-term costs, but it’s considered the appropriate course of action. 4. Unintentional technical debt Accumulates when shortcuts are taken for various reasons, typically due to time constraints or concurrent workstreams. 5. Tacked-on technical debt Occurs when a particular functionality is continually extended incrementally and “bolted on” to maintain its functionality rather than undergoing a proper reconstruction. Up to this point, we’ve delved into the theoretical aspects of technical debt, discussing its causes and mechanisms. However, what does it actually manifest as in real-world scenarios? Let’s have a look: Common Forms of Salesforce Tech Debt Common forms of Salesforce technical debt include the accumulation of unused customizations, outdated roles and permission sets, complex and undocumented workflows, inadequate data modeling, and the absence of thorough testing. This technical debt arises when shortcuts are taken or best practices are overlooked during Salesforce development, making the system harder to maintain and optimize over time. Several prevalent forms of technical debt can be identified In Salesforce, including: 1. Visualforce component vs sales path Before the introduction of Sales Path, organizations required a visual means to depict the progress of an opportunity stage or process. To achieve this, they had to customize a Visualforce component. However, with the release of Sales Path by Salesforce, these visualizations became standardized, which subsequently led to an increase in the technical debt interest rate. 2. Adapting process automation The creators of 10K recognized the necessity of automating their invoice generation process. They initially developed an hourly function to create invoices, incorporating some basic rules. However, as their contract structures evolved, they found themselves adding more functions to their initially straightforward task. Managing these changes became increasingly challenging, prompting them to allocate time to rewrite the process based on the current state of their business operations. 3. Excessive customization As previously mentioned, Salesforce provides a user-friendly environment for creating custom Objects and code, even when a simpler declarative solution would suffice. For instance, opting for a workflow instead of resorting to triggers for scripting tasks. This form of technical debt often arises from an overly responsive approach, where every requested change is implemented without exploring alternative options within standard configurations. 4. Unused customizations Despite being promoted as a ‘no-code’ platform, Salesforce cannot handle every task declaratively. Changes in business requirements may render customizations that were once essential unnecessary. Unless these customizations are retired, they can introduce inherent complexity to every new change and potentially hinder end user adoption by making your Org more challenging to navigate. 5. Access controls You’ve likely encountered the concept of “the principle of least privilege.” On the flip side, we have the “principle of most privilege,” where users end up with excessive access as their roles within the organization evolve. While other forms of technical debt can impede progress, retaining unused profiles and permission

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.