Sales prospecting based on guesswork is not only tedious but also expensive. Experienced salespeople can expect to spend 7.5 hours of cold calling to get ONE qualified appointment, according to a Baylor University study.
By trusting data instead of your lucky star, you can significantly increase your outbound sales prospecting quality and efficiency and improve your call-to-meeting hit rate. But it can only be done if you have a deep understanding of your ideal customer profile.
To succeed with data-driven outbound sales prospecting, you'll need a scalable solution for collecting data about your current customers and prospects. Then you'll have to apply machine learning to it to generate useful insights about the companies. Machine learning, a sub-category of Artificial Intelligence, is basically an enormous correlation engine.
Data-driven Sales Prospecting help you Increase Conversion Rates
Today, for most salespeople, the majority of their sales prospects do not become qualified leads. This reflects the reality that the vast majority of all their cold calls and cold emails do not result in the prospect progressing to the next step of the sales process.
When salespeople target the wrong prospects, as much 80% of their work in the earliest stage of the sales process can be wasted.
Most sales organizations are used to the fact that over 80% of their salespeople's work in the earliest stage of the sales process doesn't result in a desirable outcome. Managers refer to sales as a numbers game, encourage productivity and tell their salespeople that to make it in the business, they have to "love the grind." The reason sales look like this is that most sales professionals in the majority of sales organizations reach out to the wrong prospects to begin with.
There's some loss, some mistakes made, and mismatches, in every industry. Sales is not an exception. But the percentage of prospects that don't convert into a lead can be brought down significantly with - you guessed it - data-driven sales prospecting.
In this article, we'll share four tried and tested tips for how data can help you improve your sales prospecting.
#1 Improve your Methods for Collecting Data
Typically sales organizations rely on their reps to enter their data into the CRM system. Letting your salespeople be responsible for adding all necessary data to your CRM is a horrible way of capturing the truth.
Neither you nor any technology, no matter how smart, can create valuable insights from scattered data. Therefore, you should start by overseeing your current methods for data collection. This is irrespective of if you're looking to improve upon an already existing data-driven sales prospecting framework or if you're just about to start adding data to your sales prospecting equation.
Neither you nor any technology, no matter how smart, can create valuable insights from scattered data.
Instrument your sellers with the right technical tools to get better data. The best way to go about this is to automate your data collection. Instead of having your salespeople use their desktop phones, have them use connected systems to make calls. Instead of letting emails go back and forth under the cover of night, make sure you use a system to track them. (If you're active in the European Union, depending on the situation, GDPR that came into play in May this year might demand you have a clear consent to do this. Make sure the tool you're using is GDPR compliant.)
The reason to arm your sales team with sales efficiency tools is that the overall goal is to help them be more productive. Sales tools in this category incorporate tricks, hacks, and features that allow salespeople to, for example:
- deliver pre-recorded voice messages with just a click.
- make calls that appear to the receiver as if they're made from a local number, regardless of where you're calling from.
- create templated emails for different situations
- send automated emails
You can further improve your automatic data collection by investing in a sales intelligence or a company data platform like Vainu that provides you with detailed company data and integrates with your CRM tool. Platforms like Vainu collect, read and categorize myriads of data every day. With more up-to-date data about every company out there you can make sure you always base your sales prospecting on companies' current situation.
#2 Find out what Distinguishes a good Prospect from a Bad
You can only fully start to realize the benefit of the tip above when you start leveraging all your data's context. Applying machine learning to data about your customers, prospects, activities and the overall situation in which these activities took place will help you discover shared characteristics and patterns between companies you have a good track record with.
With Artificial Intelligence-powered technology, you'll be able to see patterns: "These calls at these times to those target titles / industries / areas lead to success. And those calls at those times to those target tiles / industries / areas don't lead to success. Do more of the former and less of the latter.
By looking at the data insights and listing both your required and wished characteristics for a potential customer you'll know what companies to look for when conducting outbound sales prospecting. You'll also know what companies to avoid. You can now more easily identify your ideal customer profile, (also frequently named buyer persona or target market) or improve upon your existing description of your ideal customer profile.
Understanding what actions to what type of companies and contact persons yield the biggest reward for you at a given time will not only help you target better prospects. It will also help you prioritize customers based on their size of the opportunity or their potential lifetime value.
Once you know what distinguishes your dream customer and ideal prospect, use a sales intelligence or sales prospecting to filter companies matching that description perfectly.
Understanding what actions to what type of companies and contact persons yield the biggest reward for you at a given time will not only help you target better prospects.
#3 Track and act on Buying Signals
With a sales intelligence or sales prospecting tool you'll get access to not only companies' firmographic and technographic data but also to data about events in these companies. Machine learning can then help you make sense of what type of company events, buying signals, will open up a window of opportunity in one of your target accounts. It works similar to when you (or rather technology) find patterns for what results you're likely to get when treating a company with a specific set of characteristics in a certain way.
This will take some legwork from your end. Begin by listing all possible behaviors leads can engage in and then use data insights to determine what behaviors correlate with a prospect progressing all the way through the sales funnel and converting into a revenue-generating customer. Once you've analyzed your data, make sure to include buying signals in your ideal customer profile.
Now, there's a better way to track companies' buying signals than to make it your life mission to read every piece of printed media out there, scan all social media feeds and subscribe to every company's newsletter. Sales prospecting tools, Vainu included, allow you to subscribe to updates about your target accounts. You'll get an email or in-app notification with information about all relevant changes in companies matching your ideal customer profile either in real time or on an hourly, daily, or weekly basis.
#4 Personalize Outreach and stay Persistent
If data clearly shows that cold calls at 8 to 10 am and 4 to 5 pm have the biggest success rate, call at these times and block out time during other parts of the day for administrative tasks, meetings, and follow-up calls. If data tells you that VPs in industry X appreciate calls on Mondays, I think you know what to do with that insights. That's right, call on a Monday.
Some sales intelligence and sales prospecting tools are even explicitly built to help salespeople adapt their communication style based on the buyer's personality. CrystalKnows, for example, suggest a suitable outreach for every contact after assigning it one of the DISC assessments four primary personality types: dominant, influential, steady, and calculation to the person. The solution gathers data from emails and people's social media accounts.
If your contacts don't respond right away, they may be interested but busy. I'm not saying: don't take no for an answer. I'm just saying: be persistent and know that the numbers are on your side if you forge ahead. The average sales rep only makes two attempts to follow up with a prospect, yet it takes an average of eight follow up attempts to actually make contact.
Conclusion
The data that the sales process generates needs to be fed back into planning and used to identify potential improvements. There's no way to acquire and manage the large volume of data you'll have at your hands without using artificial intelligence. You might be an exception if you're working with a very small number of accounts.
According to HubSpot, 50% of sales time is wasted on sales prospecting. One of the best ways to ensure you're not falling into those statistics is to continually focus on quality over quantity when you do outbound sales prospecting and never underestimate the power of data insights.
