{"id":10378,"date":"2020-12-02T08:47:57","date_gmt":"2020-12-02T16:47:57","guid":{"rendered":"https:\/\/www.vtiger.com\/blog\/?p=10378"},"modified":"2020-12-02T08:47:57","modified_gmt":"2020-12-02T16:47:57","slug":"ai-based-deal-scoring-with-vtiger-calculus","status":"publish","type":"post","link":"https:\/\/www.vtiger.com\/blog\/ai-based-deal-scoring-with-vtiger-calculus\/","title":{"rendered":"AI-based Deal Scoring with Vtiger Calculus"},"content":{"rendered":"

Does your organization use a deal scoring process where you manually assign a probability to forecast deal closures? Then you know the process can be flawed and how \u2018unpredictable predictions\u2019 can be.
\nEvery business needs a reliable way to predict the outcome of a deal. Knowing the likelihood of winning a deal is useful for two reasons:<\/p>\n

    \n
  1. Take corrective actions – Sales reps can take actions to increase winning chances if the likelihood is low.<\/li>\n
  2. Increase reliability in forecasts – Use dependable scores to build better forecasts.<\/li>\n<\/ol>\n

    Deal Scoring<\/b><\/l>
    \n
    While each deal is different, there are many factors like client and contact needs, behavior, and demographics that influence the outcome of a deal. For example, the industry in which your client operates is an essential factor.
    \nThen there are also dynamic factors such as engagement patterns. If client engagement is high, then it usually leads to a higher conversion rate.\u00a0Just remember that while you might have a higher conversion rate in one industry, the same factors or formulae might not work for another.
    \nBased on these factors, you can calculate the likelihood of winning a deal using one of the following ways:<\/p>\n

      \n
    1. Traditional Scoring<\/li>\n
    2. AI-based Scoring<\/li>\n<\/ol>\n

      <\/br>
      \nTraditional Scoring<\/b><\/l>
      \n
      The traditional approach uses rules (or conditions) configured in the CRM to assign scores.
      \nConsider these examples:<\/p>\n

        \n
      1. You can assign 5 points if a client is from the \u2018Manufacturing\u2019 industry and 7 for a client in \u2018Telecom\u2019. Similarly, you can award 3 points for each email and 6 points for each call. <\/li>\n
      2. You can set up a rule to directly assign a probability to the deal stage. With each advancing stage, the probability goes up. For example, a deal in the \u2018Value proposition\u2019 stage could be 50%, and in the \u2018Agreement review\u2019 stage, it could be 80%.<\/li>\n<\/ol>\n

        With traditional scoring, the responsibility of determining the scoring rules lies with the Sales leader. Besides being tedious, the process is also error-prone and usually leads to inaccurate scores. Administrators usually end up spending time reviewing the results and going back to modify the rules.<\/br><\/br>
        \n
        AI-based Deal Scoring<\/b><\/l>
        \n
        Vtiger has applied AI-based learning models to overcome the problems of traditional scoring.\u00a0AI-based models determine scoring rules based on historical data. And the scoring process improves over time as the data used for training grows.
        \nWe recommend AI-based scoring over others any day. We can start with the cliche \u2018data doesn\u2019t lie\u2019. But,\u00a0here are the actual reasons.<\/p>\n

          \n
        1. AI can detect patterns in the data that might not be observable by a human.<\/li>\n
        2. AI predictions are based on data, not emotions. Hence scoring figures are accurate. (However, we can also make the AI consider sentimental data.)<\/li>\n
        3. AI scoring keeps getting better with time as historical data increases.<\/li>\n
        4. AI scoring doesn\u2019t need any manual intervention; hence no \u2018external influences\u2019 will disturb predictions.<\/li>\n<\/ol>\n

          Recommendations to Improve Deal Score <\/b><\/l>
          \n
          Deal scores are helpful to sales reps only when the system can provide recommendations to improve the score.
          \nHere are a few examples of recommendations your sales reps will see when equipped with Calculus<\/p>\n

            \n
          1. Reminder to perform actions such as identifying decision-makers.<\/li>\n
          2. Recommending documents that have previously helped move similar deals forward.<\/li>\n
          3. Reminder to follow up when the time is right.<\/li>\n
          4. Suggesting the best time to contact to improve win-rates.<\/li>\n<\/ol>\n

            <\/br>
            \nReliable Forecasts with AI-based Deal Scoring <\/b><\/l>
            \n
            Accurate deal scoring aids sales managers with reliable forecasts. The forecasts get better with time as the deal scoring itself keeps getting better with more data.\u00a0Managers can rest easy because the forecasts are not just based on the sales rep\u2019s \u2018gut feeling\u2019. Instead, AI-based forecasts are based on available signals that influence the deal\u2019s outcome.
            \n<\/br>
            \n
            Try Vtiger Calculus – It is time to put AI in the hands of your sales team.<\/b><\/br><\/p>\n

            Deal scoring, Deal recommendations, Email assistant, Call analysis, Coaching features are all a part of Vtiger Calculus.
            \nVtiger Calculus is an add-on available for Vtiger Sales and Vtiger One editions (Professional and Enterprise tiers).<\/p>\n","protected":false},"excerpt":{"rendered":"

            Does your organization use a deal scoring process where you manually assign a probability to forecast deal closures? Then you know the process can be flawed and how \u2018unpredictable predictions\u2019 can be. Every business needs a reliable way to predict the outcome of a deal. Knowing the likelihood of winning a deal is useful for… .AI-based Deal Scoring with Vtiger Calculus<\/span><\/a><\/p>\n","protected":false},"author":16,"featured_media":10416,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","neve_meta_reading_time":"","_themeisle_gutenberg_block_has_review":false,"_ti_tpc_template_sync":false,"_ti_tpc_template_id":"","footnotes":""},"categories":[3],"tags":[],"class_list":["post-10378","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all"],"yoast_head":"\nAI-based Deal Scoring with Vtiger Calculus - Vtiger CRM Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.vtiger.com\/blog\/ai-based-deal-scoring-with-vtiger-calculus\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI-based Deal Scoring with Vtiger Calculus - Vtiger CRM Blog\" \/>\n<meta property=\"og:description\" content=\"Does your organization use a deal scoring process where you manually assign a probability to forecast deal closures? 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