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As customer acquisition costs continue to soar, demand generation teams must look for more sophisticated approaches to evaluating the quality of the leads they generate.
Gone are the days where you score leads and opportunities only based on data coming from your marketing automation platform.
Enter cross-cloud scoring.
Cross-cloud scoring is a scoring method that considers a range of data sources, such as CRM, marketing automation platform, product-usage analytics, and even support ticketing.Â
This scoring method enables marketing teams to evaluate and rank prospects in a far more accurate and holistic way than simple rule-based scoring methods bundled with your email marketing solution.
This article highlights six reasons why cross-cloud scoring outshines its one-dimensional counterparts, to enable go-to-market leaders to enhance their prospect qualification method.
One-dimensional, rule-based scoring systems often fall short due to their limited scope.Â
Cross-cloud scoring integrates data from multiple sources, providing a comprehensive view of lead behavior.Â
By combining insights from CRM, marketing automation, and product-usage analytics, revenue leaders can make more informed decisions on lead quality.
With cross-cloud scoring, the quality and accuracy of your lead scoring is significantly greater.Â
By harnessing the power of diverse data sets, businesses can create more robust models that accurately reflect a lead’s probability to convert.Â
This nuanced approach reduces the risk of false positives and negatives, enabling teams to focus their efforts where they matter most.
In the ever-shifting landscape of B2B SaaS, relying on static, one-dimensional scoring models can be downright risky.
What if a new product feature is released?Â
Wouldn’t you want to consider the usage of this feature in your scoring?
What if a new marketing channel or asset is introduced?Â
In the old world, you needed to remember to update your scoring model, so it would consider the new changes – but no more.
Cross-cloud scoring introduces adaptability, automatically considering fresh data from various sources, and incorporating it in the model, if it has high correlation to whatever it is you’re trying to predict.
This agility ensures that your scoring models evolve with market dynamics, keeping companies ahead of the curve.
One-dimensional scoring often overlooks crucial customer touchpoints, limiting the depth of insights into their behaviors and preferences.Â
Cross-cloud scoring provides a more holistic understanding.Â
This depth enables revenue operations leaders to tailor their engagement strategies, fostering stronger, more personalized relationships to lead to meeting go-to-market targets.
In an era where customer acquisition costs are skyrocketing, relying on one-dimensional scoring poses significant risks.Â
Cross-cloud scoring mitigates these risks by offering a more nuanced evaluation of leads.Â
The diversified data sources act as a safeguard against overreliance on single-channel data, reducing the chance of misjudging lead quality.
Cross-cloud scoring encourages collaboration by breaking down silos between departments.Â
When data from CRM, marketing, and support are considered collectively, sales, marketing, and customer support teams gain a shared understanding of lead quality.
This collaborative approach fosters better communication and alignment, ultimately driving more effective revenue strategies.
For go-to-market and RevOps leaders seeking to improve efficiency and performance, the choice between one-dimensional and cross-cloud scoring should be clear.
Cross-cloud scoring offers a more nuanced and comprehensive view of your prospects’ quality.
As customer acquisition becomes more expensive than ever, adopting cross-cloud scoring is not just a strategic move – it's a necessity.Â
Go-to-market operations leaders must look into this holistic approach to stay ahead in today’s competitive landscape and ensure that every lead and account is a step closer to a valuable customer.
