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In the high-stakes world of B2B enterprise sales, account scoring proves to be a potent tool. It assigns a numerical value to potential customers to indicate their fit for your product or service. This score helps sales teams focus their efforts on the most promising leads. In fact, organizations using lead scoring experienced a 77% lift in lead generation ROI.
But here's the catch: the data deluge from ABM and Sales Intelligence tools can be overwhelming. Imagine wading through a sea of information, only to find yourself lost without a compass.
While this data holds potential, it often becomes overwhelming and lacks focus, hindering effective decision-making.
Account scoring is a method used by sales teams to evaluate and rank potential customers (accounts) based on various criteria. These criteria can include firmographic data (such as company size, industry, and revenue), engagement data (such as website visits, email opens, and content downloads), and behavioral data (such as buying signals and intent). Sales teams score accounts to prioritize high-conversion prospects.
Traditional account scoring depends highly on static data points and shallow algorithms. Sales teams assign scores based on criteria like the number of employees or annual revenue. While this approach provides a basic level of prioritization, it has several limitations.
Today, companies depend on sales intelligence tools for account scoring. However, we need to understand how sales intelligence tools rank the accounts. They use intent data relying on factors like behavior on the website – such as site visits, downloads, etc.
For instance, when you sell a finance platform, intent data might rank accounts based on how a software engineer from Meta’s California office interacts with your website. But how does this help you? How do you zero in on the software engineer from Meta’s California office? Determining the intention behind their interactions with your website becomes nearly impossible. Are they researching for a project, comparing tools, or casually browsing? Without understanding the specific context, this intent data can lead you astray, making it challenging to tailor your outreach effectively.
Let’s elaborate more on this and look at the limitations of traditional account scoring.
AI is transforming account scoring by automating data collection and analysis, considering a wider range of data points, and applying objective scoring models.
For instance, an AI product researches which companies align with your company’s value proposition. It does so based on a prospect’s prospects, and more documents of the same nature. Moreover, it also considers factors such as the number of connections you’ve with your prospect's company and how likely you are to get a warm intro to a C-level executive or a leader from that company.
AI crafts precise scoring models tailored to your business, using data like sales history, customer profiles, and market trends. These models dynamically adjust to real-time insights from news, social media, and financial data. In addition, AI predicts buying behavior, prioritizes high-potential accounts, and offers deep customer understanding. This data-driven approach eliminates guesswork and optimizes sales efforts.
The benefits of using AI for account scoring are substantial for revenue leaders looking to drive success in enterprise sales. AI empowers your sales team to make smarter data-driven decisions through advanced analytics and machine learning.
AI is revolutionizing B2B enterprise account scoring by addressing the limitations of traditional methods. It provides improved lead qualification, better resource allocation, increased sales efficiency, and stronger win rates. In the future, we can anticipate even more sophisticated models incorporating sentiment analysis and behavioral data.
Do you want to integrate AI in your B2B enterprise sales process. Leverage Vieu's AI to streamline your sales process and convert more high-value accounts.
