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The latest economic climate and the evolving shift to Data-Driven management have impacted the way we make business decisions.
We can no longer rely on gut feeling or guesswork but rather on data and certainty.
Competition is ever-growing, and you must ensure you utilize your data better than the next software vendor, so you can keep your sales team on their toes, ready for any opportunity coming their way.
Many software companies built well-oiled marketing machines that carry out various marketing activities whose primary purpose is to generate marketing qualified leads (MQLs).
Typically, MQLs need further qualification before they're handed to AEs and the sales team. SDRs/BDRs often do This qualification.
Now, here’s a fact - most of the leads SDRs will receive when they start their work day will NEVER convert into SALs or SQLs.
In reality, the majority of the SDR team's time and efforts is invested in inspecting leads that will never turn into revenue and trying to qualify them.
In the last decade or so, lead scoring has been a top revenue contributor, helping teams achieve a higher lead qualification rate, increased lead gen ROI, and increased close rate.
The problem is that these scoring tools are mostly static, manual-based, and were built for a different ball game.
They were not built or designed for the current dynamic marketing environment, where various campaigns and activities change frequently.
These tools are largely dependent on demographic and firmographic models with a fixed score per factor, ignoring a lot of data sources and hidden yet highly relevant user touch points.
For example, what if the prospect visited the pricing options section within your product? Do you factor in these product-usage events in your scoring? In our experience, 9 out of 10 companies don’t.
Moreover, traditional scoring tools are complex to set up, involving internal stakeholders and gatekeepers.
It's no wonder many marketers we talk to admit they neither update their scoring thresholds, nor change it to support all the campaigns and marketing assets their prospects engage with.
What if you could be alerted very early in the funnel on all the leads that will not be converted to pipeline, and why?
Imagine you have a little genius helper on your shoulder that could point you to the leads that will be converted to opportunities and fuel your revenue KPIs - wouldn’t that be awesome?
Wouldn’t it be incredible to identify customers likely to churn and take the best next action to reduce risk?
The new generation of scoring solutions is operations, meaning it’s designed to enable each of our reps to be data-driven and take actions with confidence based on your historical data.
And this new approach allows reps to drive your business forward without having to depend on internal gatekeepers like analysts, BI experts, and integration developers.
Unlike descriptive analytics, which focuses on what happened, predictive analytics focuses on the future and provides insights into scenarios like “who is likely to convert (or churn) and WHY!"
Machine learning evaluates thousands of factors and user touch points, matches them against past performance (i.e., conversions/churn), and predicts what is likely to happen while providing the reasons for it.
In a world flooded with data, it’s a no-brainer to use a solution that can leverage big data to help us connect the dots and extract next-best-actions to improve business KPIs.
Taking the leap to operational predictive scoring requires changing our mindset and corporate culture. Still, such a shift can pay high dividends, as it can dramatically increase revenue and combat churn.
The most innovative GTM leaders have realized that current scoring methods have blindspots and adopted this approach to help their teams get their heads out of the sand, gain a deeper visibility into prospects, and ultimately hit their targets faster.
Let us show you how operational predictive scoring can help you achieve faster sales velocity, so you can get ahead and step forward to accelerate growth – even when uncertainty is high.