Lead qualification can be a challenging and time-consuming task.
Whilst lead generation may thrive on quantity, good lead qualification is all about quality. It generally involves many follow-up calls and conversations to determine if a raw lead is worth pursuing and, in many cases, those calls can lead to dead ends. This traditional lead qualification process is slow and laborious, and can really hit your ROI.
So, what if there was a better way of qualifying leads? A Harvard Business Review study found that AI-powered systems could double lead generation while cutting qualification call time by as much as 70% and reducing costs by more than half. Imagine what impact that would have on your pipeline.
Understand and predict customer behavior with deep-learning AI
Pairing the right AI with the right data will enable you to better understand customer behavior – and with a greater level of customer insight, you’ll be able to predict how your customers will behave in the future, and their propensity to purchase.
Not all AI technology is able to undertake the depth of analysis required for this intelligent, predictive insight. The most accurate results can only be achieved by using a system that can digest multiple data sets, including live or time-stamped data, and cross-referencing them simultaneously to find the true picture of your opportunity landscape.
The Cloudapps deep-learning AI platform is one such solution and is transforming how data is gathered and analyzed. It uses live observational data alongside CRM and other static records to provide real-time insights, continually learning and adapting its algorithms to produce the most relevant, precise information. And it will evolve as your business adapts and grows, giving you the insights you need to qualify opportunities quickly and accurately.
Identify the traits of a genuine lead
Identify the patterns in customer behaviour and account records that indicate propensity to purchase.
Qualify leads quickly and accurately
Use AI-driven opportunity scoring to provide a quicker, more accurate means of qualifying leads.
Forecast future sales with accuracy
Gain a more accurate view of your pipeline allowing you to predict what percentage of leads will convert.
Achieving real value
Many marketing and sales programs can clean up data, provide missing contact information and cross-reference leads across multiple campaigns. However, it takes smart, deep-learning algorithms to provide context and intent to determine whether a lead is a prospect worth pursuing. And once you have that information, your sales team can focus on converting the leads with the greatest chance of a conversion.
Lead qualification with Cloudapps deep-learning AI brings:
Focus on your sales team
Working on pre-qualified leads means that they can make better use of their time and efforts. Customers using Cloudapps for opportunity qualification are unlocking up to 60% more selling time.
Faster sales conversion
Using Cloudapps AI to identify and qualify opportunities considerably shortens the sales process, enabling greater focus on the opportunities that matter. This focus is already bringing Cloudapps customers an average of 20% increase in win rate.
More accurate forecasting
Having a clear view of qualified opportunities allows for a more informed view of your pipeline, allowing for improved sales predictions. 95% more accurate, in fact.
Opportunity qualification is just one aspect that can benefit from this next-generation technology Find out how the Cloudapps AI deep-learning platform can transform your sales and deliver on your objectives.
Sometimes, it’s the small things that bring about the biggest change.
Take the case of our customers, who on average have increased their win rate by 5.6%, augmented average deal size by 5.3% and added 4.1% more opportunities to their pipeline.
These numbers may not sound like much in isolation, but when combined, they created a 15% spike on their revenue.
For an organisation of £100M revenue that’s an additional £15M – without changing sales process, technology stack or personnel.