Report:

Magic Quadrant for Sales Force Automation Platforms

Top Vendor Products for Sales Force Automation Platforms in 2024

• Microsoft
• Oracle
• Salesforce

Sales Force Automation Platforms Vendor Product Comparisons 2024

• Microsoft - Leader
  

  Pros:

  • SFA and GenAI packaging: Microsoft's SFA product, enhanced by Copilot functionality, offers a compelling package. The Enterprise edition includes partial GenAI features (through Copilot) across SFA, Outlook and Teams, which allows sales organizations to use basic features and pilot the product. More-advanced features are available for an additional $30 per user per month, including functionalities like recommending SFA updates, tracking recent changes in sales records, and meeting prep.
  • Roadmap: Microsoft's roadmap for Dynamics 365 Sales focuses on enhancing the Copilot experience and leveraging AI agents. These autonomous or semiautonomous software entities utilize AI to perceive, make decisions and achieve goals without excessive human supervision, demonstrating Microsoft's commitment to intelligent and automated assistance for sales professionals.
  • Usability for account and contact management: Due to its Copilot for Sales sidecar experience, Dynamics 365 Sales offers improved data capture and synchronization capabilities between core productivity applications (Outlook and Teams). This allows for seamless record updates across the three applications, reducing the need for manual data entry and improving seller usability.

  Cons:

  • Limited AI configurability: Dynamics 365 Sales has limitations in configurable sales ML, including traditional and GenAI. It doesn't support ML training on custom data entities for predictive scoring, and unlike Copilot capabilities in Dynamics 365 Sales, its Copilot for Sales lacks support for custom prompts and entities. More sophisticated ML use cases may require Azure AI and Azure Machine Learning, needing deep data science expertise. Copilot performed slowly during Gartner demos. Prospective customers should carefully evaluate their needs and expectations.
  • Mobile app innovation: The Dynamics 365 Sales mobile app has limitations in using voice and text to create SFA tasks and records, which still require manual form field selection. Unlike its desktop version, the mobile app has not seen significant innovations over the past year.
  • Forecast management: Microsoft will deprecate forecast management snapshots in October 2024, replacing them with a forecast API for daily data sync. Its forecast management has limitations, such as its inability to forecast on split opportunities without custom entities. Predictive forecasting features only use historical sales wins and losses, not sales activities or time-and-date attributes, to inform predictions.


• Oracle - Leader
  

  Pros:

  • Mobile: Oracle possesses strong mobile app capabilities for its SFA product. It has a clean and simple user interface where sellers can manage actions within a single pane. Its Oracle Sales Assistant can be used within the Sales Cloud mobile app to provide a conversational experience, initiate and complete record updates, and search for data.
  • Market understanding: Oracle demonstrates a strong commitment to composite AI, with plans to integrate GenAI large language models (LLMs) with traditional AI models, automatically extract contractual insights, and tailor outbound communications based on customer-specific business problems. This provides a good mix of AI capabilities with feature explanation, extraction and feedback loops using both GenAI and traditional AI techniques.
  • Roadmap: Oracle demonstrates a rich product roadmap compared to its competitors in this market, especially in the areas of composite AI, GenAI content expansion, GenAI prediction explanations, enhanced analytics and GenAI-augmented lead conversion. This will appeal to customers looking to expand AI capabilities in their revenue technology roadmaps.

  Cons:

  • AI strategy: Oracle gravitates toward providing a machine-learning platform that allows sales teams to create custom AI solutions. While it offers several out-of-the-box AI use cases (such as, but not limited to, lead scoring, opportunity contact recommendation, and win prediction among others), these use cases have limited configurability. As a result, sales organizations may need in-house data science expertise to effectively scale their AI initiatives if using Oracle Sales as their SFA platform.
  • Advanced forecasting: Oracle's predictive forecasting features are demonstrated through its sales planning module, a separate product from Oracle Sales Cloud. While it is contained in a single Professional edition license, this separation creates a disjointed user experience and additional costs. Prospective clients should carefully consider their requirements, especially if they plan to complement manual forecasting in Sales Cloud with machine-learning-based predictive forecasting.
  • Product usability and integration complexity: Oracle's SFA demonstrations for this Gartner Magic Quadrant included features like forecasting insights for anomaly detection and predictive forecasting in the Sales Planning module. Conversation intelligence features require integration through Oracle Digital Assistant, CX Text Analyzer and Webhooks. Additional AI features were developed using Oracle's Data Science platform, which require advanced expertise.


• Salesforce - Leader
  

  Pros:

  • Roadmap: Salesforce plans to integrate customer data platform features into its SFA — including a streamlined Enterprise edition — to enhance client visibility across marketing, service, and website interactions. It will also enhance GenAI with autonomous agents for prospecting and launch a sales-specific LLM for sales content.
  • GenAI flexibility: Salesforce allows custom prompts, action creation, and the use of multiple LLMs from external developers via a bring-your-own-model approach, offering sales ops admins greater flexibility and customization.
  • Forecasting: Salesforce's forecasting features are visually intuitive, offering team-based and individual views of pipeline health. They support predictive models with a standard calendar setup, allowing in-line edits, annotations, and improved what-if scenario modeling. Users can visualize changes in a waterfall chart when deals are moved or removed from the committed stage.

  Cons:

  • AI/ML limitations: Salesforce's machine learning models, primarily available through its Einstein Discovery product (either as an add-on or included in the highest edition for SFA), have certain limitations in the types of use cases they support. These are restricted to numeric outcomes, binary classification (yes/no answers, for example) and multiclass classification (such as identifying top factors driving deals). Additionally, the qualitative relevance of the insights provided lacks deeper context. For example, predictive forecasting explanations are often basic and require administrators or data scientists to build separate model explanations in Einstein Discovery, making them difficult for novices to interpret. These explanations focus on individual deal-level factors and do not account for qualitative relevance or support semistructured/unstructured data sources.
  • GenAI assistant user experience: Einstein Copilot's user experience has issues. Prompt outputs may lack structure, and may fail to differentiate key insights using a specific text style, making them hard to read. Sellers may not always be able to invoke actions from prompt outputs, requiring manual copying and pasting for tasks like navigating to Copilot-created records or creating quotes. Einstein Copilot also lacks reference links within its interface, preventing users from easily verifying the accuracy of extracted information.
  • Einstein Copilot functional maturity: Some Einstein Copilot features shown to Gartner were not fully functional, especially Custom Copilot Actions. Generated account fields and website hyperlinks were preconfigured but not clickable by end users receiving the output of a prompt due to lack of rich text formatting. Additionally, the tool couldn't create quote documents or provide live links to view the quote.


• Pega - Challenger
  

  Pros:

  • Innovation: Since this research was last published, Pega has introduced significant product enhancements. These include a GenAI email composer, email interpretation and action items, meeting prep and summaries, route planning optimization, a GenAI assistant/coach for deal progression, and Pega GenAI Knowledge Buddy for contextual searches. Additionally, Pega is developing a GenAI Plan My Day feature and a GenAI simulated buyer for pitch practice and objection handling. These innovations may appeal to organizations aiming to optimize their sales teams' use of GenAI.
  • Platform and composability: Pega distinguishes itself with its newly released Pega GenAI Blueprint, a GenAI feature that enables admins and business leaders to create workflows, rules and apps using natural language. This is especially significant, given Pega's reputation for a complex platform with a steep learning curve. Pega GenAI Blueprint can streamline deployment and reduce complexity, making Pega's SFA platform more accessible for sales organizations.
  • Activity management: Pega provides a correspondence timeline with GenAI summaries, meeting prep support, and guided selling through Pega GenAI Knowledge Buddy, which delivers real-time answers to product and messaging questions. Pega Customer Decision Hub identifies top offers with a propensity-to-buy prediction, both on the account record and during calls, using conversation intelligence.

  Cons:

  • Third-party marketplace: Pega has among the fewest number of prebuilt third-party apps that can be used with its SFA product. Prospective clients looking for a vast ecosystem of plug-ins and connectors will have a difficult time sourcing developed products that are tightly integrated with Pega.
  • Forecasting: Pega's forecasting capabilities are relatively weak, with usability sometimes resembling that of spreadsheets. Forecasting visualizations are confined to the dashboards tab as configured components. Its predictive forecasting demands significant data science expertise, particularly for adding or removing model predictors. The platform lacks qualitative explanations of how predictors influence outcomes, and does not allow admins to adjust feature weights based on business importance.
  • Mobile offline capabilities: Pega's mobile app has limitations regarding offline support, particularly in its new Constellation architecture, which aims to offer a consistent experience across the desktop and mobile versions of SFA. Specifically, the app does not support offline functionality. Additionally, for clients using the traditional Theme UI, offline support is lacking for business card scanning for leads and contacts, as well as for adding attachments to appointments.


• SAP - Challenger
  

  Pros:

  • UI modernization: SAP Sales Cloud has undergone a modernization of its user experience, which is particularly evident in the simplification of opportunity management. Notably, interactive visuals have been added in a sidecar panel, allowing users to observe predictive and diagnostic trends. These visuals include opportunity scores, a timeline view and highlights on the account with assigned scores. These enhancements improve the likelihood of engagement and overall usability for sellers.
  • Balanced AI roadmap: SAP aims to provide a balanced approach between discriminative/traditional AI and GenAI features. This includes enhancements to activity management insights based on external highlights, as well as opportunity win predictions. Additionally, SAP plans to introduce a discovery questions generator aligned to opportunity signals and characteristics, as well as a synopsis feature that delivers valuable insights on the account. These insights may include the account's current challenges based on industry trends, typical customers and the competitive landscape.
  • Add-ons and integrations: SAP has a robust ecosystem of add-on and integration-ready products to complement its API-first architecture approach for SAP Sales Cloud, some of which are natively built. These include SAP S/4HANA (for ERP), SAP Analytics (for advanced analytics), a configure, price and quote (CPQ) product, and tight integration with Microsoft Teams for collaboration.

  Cons:

  • Mobile app limitations: The new mobile UI lacks offline functionality, which is crucial for field sales users. Its mobile AI assistant is still in beta and not production-ready. Due to SAP's API-first approach, users may need workarounds to integrate with legacy applications until feature parity is achieved.
  • Scope of releases: SAP continues to play catch-up with other Leaders in this Magic Quadrant. Its releases since the previous iteration have been underwhelming, such as the enablement of intent data through a Bombora partnership, guided lead conversion, and sales cadence features. All of these features have been present in products from other market vendors for a few years now, while SAP is just starting to introduce them.
  • AI/ML limitations: SAP does not support configuring AI model predictors for predictive forecasting or lead and opportunity scoring. While additional data sources can be included, users cannot fine-tune data attribute weights, making it less effective for tailored solutions. Current AI/ML use cases rely on historical sales data, limiting adaptability to real-world sales scenarios.


• SugarCRM - Challenger
  

  Pros:

  • Customer community: SugarCRM has a vibrant community platform, called SugarClub, with around 12,000 members, promoting discussion, news sharing, product inquiries and training. It connects customers, partners and stakeholders, offering seamless educational experiences through SugarUniversity (SugarU). Managed by the customer experience team, it features forums, a knowledge base, real-time chat, and networking tools, along with webinars and events for continuous learning and engagement.
  • Implementation and ongoing services: SugarCRM distinguishes itself with tailored professional services, including custom or quick start onboarding, extensive training and dedicated support for data migration and system integration. Its SMOOTH methodology ensures low-risk, consistent and predictable projects. Additionally, SugarCRM offers change management and adoption services, in-person events, and a fan-based program.
  • Strategic product partnerships: SugarCRM regularly adds to its strategic partnerships. In 2024, it launched a partnership with Mediafly for integrated sales enablement and content management, as well as a partnership with Conga, for configure, price, and quote. Existing partnerships include SYSPRO, sales-i (acquired by SugarCRM in 2024), Liferay, Oktopost, DealHub, Mobileforce, Magic Software and Triblio (now Foundry).

  Cons:

  • AI-guided selling: Compared to other vendors in the Magic Quadrant, SugarCRM has poor AI-guided selling capabilities. Most of the guided selling features are its visual Kanban view, with no AI overlay. Even the GenAI aspects used for next best actions are basic at best, with no ability to execute the suggestion within the platform as an integrated action.
  • User experience: SugarCRM provides an outdated experience across its platform. This is particularly noticeable in its workflow and business-process designer, which would benefit from a modernized look and feel. Additionally, during demonstrations to Gartner, SugarCRM showcased add-on products embedded as iframes within the SFA module, resulting in a disjointed user experience. For instance, the integration of the sales-i application for building predictive models highlighted a poor user interface and inconsistent experience when used with SugarCRM.
  • AI/ML product strategy: SugarCRM offers a limited set of ML-based use cases for sales, including predictive lead conversion, opportunity close prediction and sentiment analysis via SugarLive. The depth and scope of configuration for these use cases are restricted to out-of-the-box functionality. This may not meet the expectations of sales organizations seeking a more comprehensive range of sales AI functions from their SFA platforms.


• Creatio - Visionary
  

  Pros:

  • Product packaging and pricing: Creatio has a compelling approach to how it packages the SFA platform product. Prospects can purchase a platform base, plus stand-alone capabilities or objects from the SFA module, such as purchasing an account, opportunity or lead object only. This is desirable for companies with minimal requirements or those looking to test the platform on core components before making a broader sales technology investment.
  • Support for sales best practices: Creatio is one of the few vendors in this Magic Quadrant to offer support for sales best practices on specific processes, including it with all base products. This includes guidance on workflows for lead and opportunity management process orchestration, as well as sales forecast and pipeline inspection models, either as a general best practice or by vertical industry.
  • Innovation: Creatio has released the Creatio Copilot experience in its SFA platform product. This allows users to not only summarize records or initiate email replies, but also initiate tasks, rules and workflows, and enrich data with third-party integrated data solutions via prompting in natural language. It also released a library of new drag-and-drop minicomponents to improve SFA workflow developments, such as a new sales progress bar, a next-steps component and simplified records management components.

  Cons:

  • Usability: Creatio has usability shortcomings in forecast management, visualization, analytics, and collaboration. It requires manual steps to invoke calculations, business logic, or collaborate on sales documentation, unlike its drag-and-drop components.
  • Activity management: Creatio has ranked lower in activity management. For example, it does not provide out-of-the-box support for building sequences or cadences to extend sales activities beyond its business process management capabilities. Its native conversation intelligence features are basic compared to other leading SFA vendors and do not offer a robust set of insights and metrics for improving seller productivity.
  • Artificial intelligence (AI)/Machine Learning (ML): Creatio's AI-guided selling relies heavily on rule-based actions, with machine learning suggestions mainly used in its Creatio Copilot experience. Outside of Creatio Copilot, the system suggests generic actions that lack specificity and decision confidence like "send an email" or "create an opportunity." Its ML configurability is complex for business users, requiring detailed knowledge of training datasets and predictors. The generative AI (GenAI) lacks robust debugging and bias-testing features, offering only a basic bias index slider with subpar explainability.


• Zoho - Visionary
  

  Pros:

  • Predictive scoring: Zoho offers robust capabilities for predicting close dates, sales conversions, total contract value and customer churn. It supports custom scores for any field, tailored to unique business needs. Zoho's AI assistant, Zia, updates scores biweekly for accuracy. Sellers can also compare predictive scores by analyzing included fields and records, enhancing data-driven decision making.
  • Workflow automation: Zoho offers multiple workflow automation tools. Sellers can use Workflow Rules for "if and then" automations, available from the free version. Cadences streamline customer follow-ups with targeted, sequential communications based on interactions and actions. Blueprint enables more-complex business process automation for customer journeys, detailing steps like updating opportunity values and identifying customer needs; these are available from the professional license. Zia's workflow suggestions allows it to analyze recurring actions performed and suggest corresponding workflows. This unique feature can improve a sales organization's overall time to value.
  • Lead management: Zoho CRM captures leads from multiple sources and provides real-time notifications through SalesSignals, available from the professional license. Automated lead assignment is optimized based on seller workload and historical trends of successful lead conversion. Additionally, Zoho supports double-opt-in to ensure data privacy and security, addressing any concerns regarding compliance.

  Cons:

  • AI-guided selling: Zoho's guided selling, particularly the next best experience and recommendation builder functions, are basic. Sellers will find the provided suggestions, such as "make a call within two days," largely unhelpful and lacking qualitative relevance and context for what the action should entail. These products are more useful for product recommendations than contextual next best actions.
  • Activity management: Zoho decided to deprecate its activity management tab in March 2024 in favor of individualized modules for tasks, meetings and calls. This is a notable consideration for sales organizations looking to centralize sales activities. While it still retains a system of record, it further introduces productivity complexity.
  • Forecasting: Zoho's anomaly detection and alerts for unusual changes in forecasts are limited. It does not support custom fields in the forecast module. Forecast types, such as forecasting by revenue or by quantity, need to be created as two separate forecasts. Its forecasting experience for predicted insights and values jumps between the Reports and Dashboards tab, and the Forecast Module, providing an inconsistent user experience.


• BUSINESSNEXT - Niche Player
  

  Pros:

  • Lead management: BUSINESSNEXT offers machine-learning-based predictive lead scoring models that customers can configure. These models analyze lead attributes, including behavioral data, intent signals and engagement history. Sales teams can access lead scores, source signals and recommended actions. The scoring model is integrated with guided selling for an optimized sales experience and supported by a segmentation-building model that enables identification of high-value leads.
  • UI improvements: BUSINESSNEXT has improved its user interface with an enhanced Kanban view, as well as improvements for those preferring compact and split-level views, along with improved calendar views.
  • AI strategy: BUSINESSNEXT provides a comprehensive, user-friendly AI solution tailored for sales use cases. It offers out-of-the-box AI models for lead and opportunity scoring, AI-guided selling, and predictive forecasting, all of which can be configured by administrators who wish to customize sales model inputs. Additionally, BUSINESSNEXT includes a variety of validated machine-learning models designed for specific sales scenarios. This is particularly valuable for sales organizations that require more than standard features, but lack extensive data science resources.

  Cons:

  • Proposal and quotation: BUSINESSNEXT has a relatively weak offering for proposal and quotation generation relative to leading SFA vendors in this Magic Quadrant. It has a basic quote-builder tool that uses templates and offers limited pricing guidance for sellers. Additionally, the AI-guided RFP response function was unable to answer nearly half the questions posed during the demo.
  • Pipeline and forecast management: BUSINESSNEXT's ability to trace, inspect and monitor pipeline health is among the weakest of the vendors evaluated for this report, based upon demonstrated features.
  • Pricing and packaging: BUSINESSNEXT has transitioned from a single edition to a three-tier-edition offering. Relative to features included, these are priced higher than those of leading market vendors, not considering discounts, ranging from $125 per user month for the lowest edition to $450 per user per month for the highest edition. Additionally, opting for 24/7 support will add an extra 24% to the total subscription cost. If you require AI features, expect to pay an additional $45 per user per month per model.


• Freshworks - Niche Player
  

  Pros:

  • Pricing value: Freshsales is one of the more budget-friendly SFA solutions evaluated in this Magic Quadrant. Its highest-priced editions, geared toward the enterprise, lists for $59 per user per month when billed annually, and includes a dialer, sales engagement features, AI features such as scoring, deal insights, and a chatbot.
  • Visualization: Freshsales allows admins to create custom visualizations for both standard and custom objects, and filter charts based on various attributes. The platform also supports drill-down functionality, allowing users to explore data at different levels of granularity. Unique URLs for each dashboard ensure seamless report sharing.
  • Omnichannel support: Freshworks provides omnichannel activity tracking across several channels, including Instagram, SMS, WhatsApp and website chats, which are included in all paid versions of its packaging. The platform enables sales organizations to create pretrained chatbots for engaging prospective customers in multiple languages to help streamline customer interactions in one place. These chatbots can change the object value, trigger API and custom functions in the application to enable task execution, and also support handover to an agent or seller for further human support.

  Cons:

  • Limited AI capabilities: Freshsales lags in several AI capabilities, compared to leading vendors. It lacks predictive and prescriptive analytics for pipeline inspections, sentiment analysis, and conversation intelligence, limiting its ability to provide comprehensive insights from customer interactions. While it does offer account and contact scoring, which are rules-driven, and basic next-best-action recommendations through Freddy AI, these features are rudimentary.
  • Basic mobile experience: Freshsales' mobile app is relatively basic, lacking advanced features such as speech recognition. This limits the ability of field sellers to populate information across various fields using voice input. The absence of route optimization functionality also hinders the efficiency of sellers who make multiple client visits.
  • PRM support: Freshworks' capabilities for partner relationship management are limited. Partner sellers can access accounts and contact objects via restricted user licenses. However, additional capabilities like pricing approval, order and return management require a third-party application.


• HubSpot - Niche Player
  

  Pros:

  • Viability: HubSpot demonstrates above-average growth and customer retention in the SFA market. This is particularly reassuring for prospective clients seeking stability from vendors that cater to small and midsize businesses.
  • Usability: HubSpot has an intuitive, connected-platform approach to SFA. In its demonstrations to Gartner, it features a friendly user interface with easy-to-follow task execution. The UI also includes an activity timeline that allows you to filter activity types shown across adjacent modules, such as showing marketing email signals or events.
  • Activity management: HubSpot possesses notable activity management features, ranging from a native conversation intelligence tool to a sequence builder, a LinkedIn bidirectional sync and an email template library for outreach efforts, all contained natively.

  Cons:

  • AI/ML strategy: HubSpot lags behind leading vendors on AI and ML features provided out-of-the-box, such as machine-learning-based lead scoring, next best actions and predictive forecasting. Most of these features have been present in several SFA vendors for the last two-plus years, whereas HubSpot is either planning to release them, or has released them more recently. Furthermore, it offers limited configurability for its AI use cases and is not suitable for sales organizations that need to train on custom fields or data sources.
  • Pricing and cost: HubSpot has recently moved to a seat-based pricing model that eliminated the minimum seat requirement, but also increased the list price for its enterprise edition by 25%. Gartner has fielded complaints from HubSpot clients pertaining to cost for small and midsize organizations.
  • Guided selling: HubSpot possesses weak guided selling capabilities, it currently does not use AI-driven next-best-action recommendations, and the extent of guided selling is limited to rule-based workflows. Furthermore, its GenAI assistant ChatSpot, which may provide conversational recommendations, lives as a separate interface that is not part of the same sales hub module.


• Neocrm - Niche Player
  

  Pros:

  • AI/ML: Neocrm has improved its AI capabilities throughout its platform to support seller workflows, capture insights and strengthen the core pipeline management and forecasting capabilities of its platform. In its Niche Player part of the quadrant, these additions enable it to compete favorably with vendors across the market and service its regional customers.
  • Innovation: Neocrm has released a compelling set of features, such as AI conversation insights for analyzing intent, sentiment and context using GenAI, along with an AI email assistant for preconfigured or custom prompt building. It has also released a digital sales room, which is a tailored microsite with the ability to create personalized content for buyers.
  • Partner relationship management: Neocrm offers a partner relationship management (PRM) capability for its indirect sales model that supports the life cycle of partner interactions. This includes onboarding through co-marketing, sales transactions, sharing of leads, opportunities, collateral through partner portals, and integrated collaboration for social media via multiple access points. Neocrm's PRM functions monitor and manage incentive and rebate administration, budgets, settlement and reconciliation.

  Cons:

  • Activity management: The vendor's demo to Gartner revealed a reliance on marketing for sequences and cadences, and a lack of complex workflow automation for sales. Instead of demonstrating sales automation following an email association, it showcased marketing email templates and failed to show an email record triggering an automated sales activity. Additionally, the Outlook plug-in requires multiple scrolls and clicks for account overviews, logging contacts, and accessing records, hampering user experience.
  • Marketplace ecosystem: Neocrm has among the fewest number of third-party apps in its ecosystem. This is particularly important for sales organizations that are looking to complement their SFA platforms with tailored apps for specific verticals or business processes. All of its apps and website languages are for the Asia/Pacific region, with no English-based website.
  • AI/ML limitations: Neocrm has notable limitations in its AI/ML configuration capabilities. It allows users to create lead and opportunity scoring models by selecting fields and attributes for training. However, it does not permit adjusting predictor weights or explaining feature correlations to outcomes during setup. The model is trained solely on historical closed-won opportunity data, which is not a reliable predictor of future success. Although Neocrm offers predictive forecasting, it did not demonstrate the administrative setup for these models as it did for lead and opportunity scoring. Additionally, its response to Gartner's questionnaire did not indicate that administrators can build predictive forecasting models using ML.


• Vtiger - Niche Player
  

  Pros:

  • Activity management: Vtiger offers SMB clients useful AI- and rule-based tools to support seller/customer interaction summarization, and rating across channels like email, mobile, chat and voice. Tracking all interactions supports account, contact and deal scoring, and with AI-generated responses, can personalize customer interactions and automated seller data entry.
  • Collaboration and productivity: Vtiger offers internal and external collaboration tools, including a digital sales room, document co-editing, and communication tracking.
  • SMB market focus: Vtiger's always-free, self-signup version is designed specifically for small businesses initiating their CRM journeys, and offers a seamless transition to paid editions. Many of the standard features are included in the free edition, which allows sales users to trial a diverse range of capabilities, both SFA and non-SFA.

  Cons:

  • Forecasting: Vtiger's forecasting capabilities rank among the lowest of all vendors. For instance, its predictive forecasting is limited to AI scoring on individual deals. The platform does not support forecasting splits, offering only dashboard views and custom filters. Tracking changes in user-submitted forecasts is only available through the reports feature.
  • Lead management: Vtiger's lead management capabilities show significant weaknesses compared to similar market competitors. While Vtiger offers basic lead scoring and engagement scoring, it lacks AI-driven insights and predictive analytics that competitors provide. Vtiger does not support ML-based lead scoring natively, and requires professional services for setup. It falls short in offering AI-driven recommendations for next best actions, optimal contact times and channel selection, which are standard features in comparable platforms.
  • Customer success management (CSM): Vtiger's CSM has several weaknesses compared to similar competitors, lacking the depth and personalization of training and certification programs and limited role-specific sessions. VTiger's CSM metrics are limited, focusing on product usage and testimonials only, and lacking robust indicators like annual recurring revenue and customer churn rate. Vtiger's problem-solving and continuous-learning initiatives are less comprehensive and structured compared to other vendors in this Magic Quadrant.

Reference

• Gartner, Magic Quadrant for Sales Force Automation Platforms, 26-Aug-2024, ID G00802054

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