Predictive Dialers vs. Predictive Analytics
Some organizations have turned to dialing technologies like predictive dialers, which work to determine how many lines to dial in order to get a response. Unfortunately, predictive dialers are a 30-year-old, outdated technology suffering from many limitations and even strict FCC regulations.
That’s why a new class of technology, powered by predictive analytics, is catching on so fast and transforming the industry.
Instead of dialing as many lines as possible, predictive analytics works to predict who to call, when to contact them, and what to say.
In fact, according to a study by IBM, CIOs rank analytics as the No. 1 factor contributing to an organization’s competitiveness.
But what is predictive analytics? How does it compare to old-school predictive dialers? And why does any of this matter?
The problem with predictive dialing
Despite their futuristic name, archaic predictive dialers don’t have the power to magically lead your sales reps toward golden opportunities that are eagerly awaiting your calls.
Actually, the only thing predictive dialers predict is how many dials to make simultaneously in order to get somebody to answer.
These dialers make three, four, five or more simultaneous dials, hoping someone will answer one of the lines. Once someone does answer, the dialer patches this person to an available agent.
While this increases dial volumes, it does little to improve your relationship with customers.
Predictive dialers offer an outdated approach to sales prospecting.
Worse, predictive dialers are plagued with problems, like call abandonment and their inability to prepare reps for calls. This makes traditional “1.0 dialers” unsuitable for most professional selling situations.
The power of predictive analytics
In order to meet the demands of a modern sales team, organizations need to adopt sales technology fueled by predictive analytics.
Predictive analytics has the power to uncover hidden insights. This can come in the form of learning more about customers, identifying additional cost savings, or increasing the efficiency of internal operations.
For example, online retailers like Amazon use predictive analytics for buying recommendations based on a user’s previous purchases. In other cases, health organizations use data analysis and predictive analytics to track the spread of disease and anticipate a possible outbreak before it happens.
Simply put, predictive analytics uses current and past information to create predictive models for the future. It helps you anticipate potential outcomes and make more informed decisions, which ultimately helps you sell more.
Predictive analytics and sales
In order to fully understand predictive analytics and how it relates to sales, we must take a step back and understand the data itself.
Data is more than counting how many calls reps are making every hour or which time of the day people are most likely to answer their phones.
When it comes to sales, every piece of information is valuable, like where people live, what their title is at work, if it’s sunny or raining, and whether or not their favorite sports team won last night.
All of this information can influence your prospects’ buying decisions.
Fortunately, we now have the ability to capture and store data like never before. We also have the ability to analyze and process large amounts of data almost instantly, surfacing actionable trends and insights in real time.
This forms the basis of predictive analytics. Using this data, analysts and machines can make informed decisions based on whatever information is available.
The more data points we collect, the clearer the picture and the better the prediction.
How predictive analytics fuels technology
Predictive analytics helps reps work smarter. Unlike predictive dialing technology that just looks to increase dial volumes, predictive analytics can increase contact rates, conversations, opportunities and deals.
For example, when data science is integrated into sales acceleration technology, it can work to dynamically sort targeted contacts based on contactability and probability to close. This process maximizes sales results automatically throughout the day and week for each of your reps.
Predictive analytics can also determine the optimal day of week and time of day to contact each prospect in a call list, dramatically improving contact rates.
Finally, predictive analytics helps your reps remain in control of every call from start to finish. Sales reps can properly prepare for each call before even picking up the phone.
Predictive analytics informs reps of pertinent information about a prospect, allowing them to quickly review each record prior to calling.
Instead of relying on canned, generic messages, reps can use this data to craft personalized, tailored pitches. With solutions specific to each customer, the chances of closing the deal soar.
Companies that put predictive analytics at the center of their marketing and sales decisions improve their ROI by 15% to 20% and are more than 2 times as likely to outperform their peers, according to studies by McKinsey & Company, MIT and IBM.
See for yourself how predictive analytics can dramatically improve your sales results by downloading the free ebook below.
Free eBook: The Science of Lead Scoring, Prioritization & Sales Success
Use proven science to maximize results from your most valuable resource – your sales team.
Image credit: Steven Mileham