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

Top 7 Data Cleansing Tools Blog Ensure your business decisions are based on accurate data. Discover what data cleansing is, its importance, and how it can transform your messy data into a valuable asset. Learn about top data cleansing software to keep your CRM clean and efficient. Dive into our comprehensive guide to make your data work for you, not against you. 10 min What is data cleansing?   Data. It’s the lifeblood of modern business, fuelling insights, driving decisions, and ultimately, shaping success in the larger picture. But raw data is often messy, riddled with inconsistencies, errors, and duplicates. This “dirty data” can lead to inaccurate analysis, flawed decision-making, and eventually wasted resources. The amount of data around us has increased and so is the need of validating its quality. As this data surge has made room for inevitable errors, companies are dabbling with the subsequent data quality checks. Did you know only 3% of data meets basic quality standards? As per Gartner, Poor data is responsible for an average of $15 million per year in losses. This is why the need for Data Cleansing is at all time high! Data cleansing, also known as data scrubbing, is the process of identifying and correcting or removing corrupt, inaccurate, or irrelevant data from your datasets. It’s essential for maintaining data integrity and ensuring your company can make accurate, informed decisions. Why Does Your Company need it? Just picture your best salesperson enthusiastically pursuing a lead only to get stuck. The phone number is wrong and the email has bounced back. Yes, it’s frustrating. This “dirty data” is battled against by reps every now and then. Inaccurate, missing or duplicated information that are in your CRM system can constitute unnecessary barriers for your reps. It’s like being lost and taking the wrong turns while traversing through a town; you may eventually arrive at your destination but after several hours of wasted efforts. Therefore, dirty data is a silent killer waiting to feast on potential opportunities in your CRM with a possible domino impact such as: Wasted Time & Resources: Data-detective mode takes over for your reps who spend hours following up cold leads, fixing mistakes or verifying details. This means that they lose significant selling time that could have been used to close deals. Missed Opportunities: Inaccurate data can be likened to a blind spot. You cannot reach existing customers via targeted advertising nor find new ones using it more effectively. What if you miss out on a big client simply because their mail account was returning an error message? Poor Decision Making: Dirty data also takes the crown when it comes to generating skewed reports and metrics. This can lead to distorted representation of things, poor business decisions and finally missed opportunities. Strained Customer Relationships: There is hardly any doubt that sending irrelevant emails or reaching out wrong individuals will yield a negative experience for customers. Your company name can be tarnished by bad data while at the same time clients can be left annoyed and made feel like digits. Doing proper data cleansing will make sense out of your chaotic data transforming it into one clean reliable source of truth. Top 7 Data Cleansing Softwares Luckily, you can tame the dirty data with several data cleansing software in the market today. A good data cleansing software can transform your messy CRM into a well-organized filing system, ready to empower your sales team. We have curated a list of top 7 data cleansing softwares for your company to choose their perfect fit. Nektar.ai Salesforce data could end up being a mess of information that can hinder the reliability of your reports. This is where Nektar.ai can help you navigate out of the clutter by putting your data hygiene on auto pilot using AI. Here’s how Nektar.ai solves the problems with data cleansing: Unmatched Sync Accuracy: Nektar.ai does not only import data at basic level, it also analyzes your records using AI algorithms for establishing links between accounts and opportunities and provides confidence scores for correct synchronization. This helps in cutting out redundant entries more importantly by enabling you to view all needed details.Time Travel for Data Retrieval: What if I told you that you can unearth the actuals of an old conversation that happened with a particular client? Nektar helps in identifying interactions like contacts, emails and meetings linked to a given domain which are then added into newly created opportunities. Its “time travel” functionality facilitates knowledge transfer among sales people and adds context to live conversations during ongoing engagement.Easy Report Creation: High-quality reporting is dependent on clean data. Nektar.ai makes it simpler to generate reports by automatically syncing contacts, emails, and meetings directly into standard Salesforce objects.Self-Healing Energy: Nektar.ai is ever learning and adjusting. It updates CRM records in response to new information by appending manual changes made by users into the system automatically.Smart Contact Automation: Nektar.ai can automatically create a contact point as well as eliminate some repetitive tasks. They are created with matching domains such that they connect with previous accounts as appropriate.Contrary to traditional data cleansing that may necessitate manual work or third party tools, Nektar.ai is an AI-powered solution that integrates well with Salesforce and does many tasks automatically. It is self-learning and ensures data always remains clean and accurate. With Nektar.ai, you can liberate your sales team from the grind of data entry and enable them to concentrate on their core function; closing deals.   Openrefine Google Refine, which is now known as OpenRefine, is already a well-known open source tool. It’s an open-source software like python script that can be freely used and modified by anyone. It also helps to maintain your data in a consistent format and sorts it according to your company requirements. Apart from this, you can import data from the web sources and apply its clustering algorithms for solving complex data cleaning jobs. Where all does OpenRefine stands out? Free and Open Source: Cheap to install and allows

9 Sales Commission Software for 2025 RevOps 10 min What is Data Enrichment? The sales landscape in 2025 is defined by hybrid workforces, AI-driven decision-making, and hyper-personalized incentive structures. As companies navigate global expansion and complex compensation models, legacy tools like spreadsheets are being replaced by intelligent platforms that automate calculations, predict outcomes, and align incentives with business goals. Modern sales commission software now integrates seamlessly with CRMs, ERPs, and collaboration tools, offering real-time visibility, compliance assurance, and actionable insights. In this guide, we explore the top 9 platforms reshaping commission management in 2025, highlighting their unique strengths, pricing, and use cases to help you choose the perfect fit for your team. Top 9 Sales Commission Software for 2025 Spiff ElevateHQ Everstage Anaplan QuotaPath Xactly Performio SalesCookie OpenComp What is Sales Commission Software? Sales Commission Software is a type of tool that helps businesses manage their sales commission calculations and payments to their sales representatives. Basically, it helps companies keep track of how well their sales are doing, figure out how much commission their salespeople should be earning based on different factors like sales targets, and even automate the whole commission payment process. Think of it as your reliable companion in the sales world. It takes the guesswork out of the equation and provides a streamlined process for businesses to accurately determine the commissions owed to their sales team. 1. Spiff Spiff dominates the market with its AI-powered “Commission Cortex,” which predicts quota attainment and recommends incentive adjustments. Trusted by high-growth SaaS and fintech companies, it’s ideal for scaling teams needing dynamic, data-driven plans. Key Features: Predictive Analytics: Forecast earnings using historical data and market trends. Dynamic Quota Management: Auto-adjust quotas based on rep performance. Integrations: Salesforce, HubSpot, Slack, Microsoft Teams, Netsuite. Rep Experience: Mobile-first dashboards with gamified progress tracking. 2. ElevateHQ ElevateHQ excels in global compliance, offering automated tax calculations and audit trails for enterprises. New in 2025: Blockchain-powered commission ledgers for tamper-proof records. Key Features: Multi-Currency Payouts: Supports 50+ currencies with real-time FX rates. Compliance Hub: GDPR, CCPA, and SOC2 compliance tools. Approval Workflows: Streamline disputes with in-app resolution channels. ERP Integrations: SAP, Oracle, Workday. 3. Everstage Everstage’s no-code platform now includes AI-driven “Incentive Co-Pilot,” which designs comp plans tailored to rep behavior. Popular among mid-market agencies and consultancies. Key Features: Drag-and-Drop Rules: Build SPIFFs, bonuses, and clawbacks without IT help. Rep Retention Analytics: Identify at-risk reps using engagement metrics. Collaboration Tools: Comment threads and @mentions for plan feedback. Integrations: ZoomInfo, LinkedIn Sales Navigator, Stripe. 4. Anaplan Anaplan’s enterprise-grade platform now offers a “Compensation Workbench” for modeling M&A scenarios and harmonizing plans post-acquisition. Key Features: Territory Optimization: Balance workloads using AI-driven territory mapping. Scenario Modeling: Simulate comp plan changes on revenue and margins. Security: FedRAMP-certified for government contracts. Integrations: Snowflake, Tableau, Salesforce CPQ. 5. QuotaPath QuotaPath remains a top choice for small to medium-sized businesses (SMBs) and remote sales teams in 2025, thanks to its intuitive design, affordability, and focus on transparency. Designed to simplify commission management, it empowers reps to track earnings in real time while giving managers tools to align incentives with business goals. Below is a comprehensive breakdown of its 2025 features, pricing, and use cases: Key Features  AI-Powered CoachBot ChatGPT-4 Integration: Reps receive real-time coaching via an in-app chatbot. For example, asking, “How can I hit 120% quota this quarter?” triggers personalized tips based on historical performance and team benchmarks. Skill Gap Analysis: The AI identifies weak spots (e.g., low conversion rates) and recommends training modules or playbooks. Gamification & Motivation Tools Live Leaderboards: Reps compete for badges like “Closer of the Month” or “SPIFF King.” Milestone Celebrations: Auto-generated shoutouts in Slack/Teams when reps hit targets. Free Tier for Startups Unlimited Plans: Manage up to 10 users at no cost, with access to core features like commission tracking, basic reporting, and QuickBooks/Xero sync. Ideal for Bootstrapped Teams: Perfect for early-stage startups testing comp structures. Self-Service Rep Portals Earnings Simulator: Reps model “what-if” scenarios (e.g., closing 5 more deals) to forecast commissions. Mobile App: iOS/Android app with push notifications for quota progress and payout approvals. Enhanced Integrations (2025) CRM: Salesforce, HubSpot, Pipedrive. Payroll: Direct sync with Gusto, Rippling, and ADP. Collaboration: Slack, Microsoft Teams, Zoom. Advanced Analytics for Managers Quota Attainment Trends: Spot seasonal patterns or team-wide bottlenecks. Payout Accuracy Reports: Flag discrepancies between forecasts and actuals. 6. Xactly Xactly solidifies its position as a powerhouse for enterprise-grade incentive compensation management (ICM) in 2025, combining generative AI, deep compliance tools, and ESG-aligned incentives. Trusted by Fortune 500 companies and global enterprises, Xactly streamlines complex comp plans while ensuring transparency and scalability. Below is a deep dive into its 2025 capabilities: Key Features –  Generative AI for Comp Design Auto-Generated Plan Docs: Describe goals in plain language (e.g., “Create a plan with accelerators after 100% quota attainment”) and let Xactly’s AI draft policies, saving 10+ hours/month. Smart Clause Library: AI suggests compliance-approved clauses for territories, clawbacks, and SPIFs. ESG-Linked Incentives Sustainability Metrics: Tie commissions to ESG goals (e.g., bonuses for reps who onboard eco-friendly suppliers or reduce carbon footprints). DEI Analytics: Track pay equity across gender, ethnicity, and role to ensure fairness. Global Compliance Hub Tax Automation: Auto-calculate VAT, GST, and income tax for 100+ countries. Regulatory Updates: Real-time alerts for changes in labor laws (e.g., EU AI Act, California SB 749). Predictive Territory Planning AI Territory Mapping: Balance workloads by analyzing deal size, rep capacity, and market potential. Scenario Modeling: Simulate mergers, territory splits, or product launches to forecast revenue impacts. Advanced Security & Governance SOC2 Type II & ISO 27001 Certified: Meet strict data security standards for healthcare, finance, and government sectors. Audit Trails: Track every comp plan change with user-level timestamps. Integrations (2025 Expansion) CRM: Salesforce, Microsoft Dynamics, HubSpot. HRIS: Workday, SAP SuccessFactors, BambooHR. Analytics: Tableau, Power BI, Snowflake. Sustainability Platforms: Salesforce Net Zero Cloud, Watershed. 7. Performio Performio emerges in 2025 as a data-first sales commission platform, tailored for enterprises needing real-time analytics, IoT integration, and seamless payroll processing. Designed for

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

Revenue Leader Caroline Holt on Putting Together the Best Sales Tech Stack RevOps Sales Techstack 10 min Extracting value from a sales tech stack continues to be a frustrating challenge for revenue leaders. Budget freezes across the board have forced revenue leaders to be more mindful of the tools they add to their tech stack. But it can be a daunting project to undertake with the market being so crowded with tools across multiple categories.How can revenue leaders select the best tools for their tech stack? How can they derive value from this steep investment? And how can they make their sales teams more productive? We sat down with Caroline Holt, VP Revenue Training & Enablement at Bonterra, to unpack some of these nuances around creating the best sales tech stack. Caroline shares some brilliant insights on how revenue leaders can create the best sales tech stack. And make the process more efficient and effective. If you’re short on time, here is a quick summary of the conversation.   If you enjoy our discussion, check out more episodes of our podcast. You can follow on iTunes, Spotify, YouTube or grab the RSS feed in your player of choice. What follows is a lightly edited transcript of the episode. The Sales Tech Landscape Has Exploded & Disrupted Sales   Abhijeet: Caroline, thanks for coming on the show. Caroline: Thank you so much for having me. Abhijeet: You’ve been in the sales tech industry for quite some time. How have you seen it change over the last few years? Caroline: Well, it has not only transformed. But it has exploded, right? Technology has disrupted sales. I think the buying process in some cases has not changed over the last 20 years, but the way we sell and the way that the buyer wants to purchase has changed. So when I think about my role as a BDR early on, I was calling, I was faxing, I was emailing. But I could get to someone typically. And I think in some cases the proliferation of things like cadence tools that allow people to drop somebody into a constant flow of information has actually hindered our ability to get to people that we want, who might actually need what we need to do. So to the overarching question of how technology has changed, I think in some ways it’s changed in a really incredible way. Because I am an efficiency geek. I like removing friction from the sales process. But I think that sometimes we actually get in our own way because of how we purchase technology. I think of the tech stack in terms of where your business is and what you need to be successful. And I think that’s actually the biggest challenge right now. The first thing that I would say is that when you think about technology, it’s a great solution if you have a really good process to start with. And people to manage the automation, ongoing configuration, updates, maintenance, and so on. CarolineHolt VP, Revenue Training and Enablement Technology is going to be great at a foundational level. So the first thing you need is a way to engage with people, whether that is your regular old telephone and email, or whether that is some sort of a dialer tool where you’re capturing that information. And then you need some place for that information to live. So you need some sort of CRM to be able to capture that information, figure out who you’ve been talking to, what that’s been like, if you’re opening opportunities, what that opportunity looks like. Then you need to figure out what’s actually happening in those calls.  And then that you can analyze a lot of that data over time in terms of what people are saying in aggregate. So our whole roadmap should be focused on it. It  provides just a really powerful level of insight. But I think for a lot of organizations, they don’t ever optimize those parts of the tech stack, and then they start adding new stuff. They either haven’t gotten it right the first time, or they think that that’s table stakes.  That foundational piece, particularly the architecture around the CRM, if that stuff isn’t right, none of the other tech is really that helpful because you wind up buying stuff and building stuff that doesn’t really help that whole flow from who are we talking to, what are we talking to them about, what’s happening with those deals to closing those deals. Caroline Holt VP, Revenue Training and Enablement So that’s a really simplistic way of thinking about sales technology. But I would say that most organizations need to start with those fundamental pieces and then start thinking about, okay, once we know, now we need to figure out who those prospects are. So what sort of technology is gonna help us identify who those folks are. So how you build that stuff over time becomes really powerful. And then what you do with that data and analytics becomes really powerful over time. But sometimes people invest really quickly in a lot of technologies, but they never really optimize them for performance. So the other part is just thinking about having what you can actually bite off in terms of tech investments in any given year to be able to do the right thing for your business.  Where is Sales Tech Heading Towards?   Abhijeet: If you don the hat of a sales leader who’s going to spend a hundred dollars this year across the technology stack, how should they go about their investment approach? Where should those a hundred dollars be allocated?  Caroline: So I would say that like everything in enablement or any of the back office operations, it’s where are your problems? So if the business should be investing based on what technology is going to actually help them be more effective, where they’re less effective than they could be today. So

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