While there’s lots of talk about sales and marketing alignment, those of us in sales know that there’s another alignment challenge in most companies. There’s also a natural push and pull that exists between sales and finance teams, as sales leaders look for ways to meet their revenue goals, and finance leaders seek justification for headcount growth. Given this dynamic, every fast-growth company wants new ways to improve sales team productivity and shorten the ramp time of new reps. Thankfully, with new predictive analytics and AI solutions, it’s getting easier to foster sales and finance alignment by using data to show how quickly new hires make an impact on the top line.
Having worked in sales teams at various stages of growth, I’ve seen several approaches to the on-boarding process. Sometimes it’s raining leads, and every rep is spinning plates trying to keep up with prospects in each stage of the buying cycle, while for other companies, it’s all about finding ways to succeed at outbound prospecting without wasting time or budget. New reps, in particular, often find themselves flailing, just trying to follow up with everything while learning what makes a top prospect. It’s not a cliché that time is your biggest commodity as a sales rep, and time management needs to center around aligning effort to its potential impact on the business.
Here are some specific best practices that I’ve personally used to help boost sales productivity:
Depending on the size of your organization and your deals, there are a few different ways you should leverage predictive intelligence to optimize the learning process for new reps. One option is to give new reps only bad leads during their training period (i.e. those that your model categorizes as C- or D-Leads because they aren’t a great fit for your product). This may seem cruel at first, but it can actually help them build confidence in your product messaging with minimal pressure. In addition, it reduces the risk that newbies will inadvertently burn out good fit prospects while they’re getting their feet wet. Of course, in order to account for the lower quality of these leads, be sure to give new reps smaller quotas as they’re working their way through the initial set of leads.
Some of the companies I’ve worked for had enough leads that we were able to use the opposite approach. We assigned all our new reps good leads only. This gave them confidence that the prospect was already a good fit for our product, so they could focus more on triangulating account decisions makers and buying personas, and employing appropriate selling motions. As a result, reps felt confident and productive from day one, and more quickly absorbed predictive insights and signals for a better understanding of ideal prospects.
Another approach we use at Infer is to allow new reps to fish for their own leads. When someone is promoted from SDR to AE, we encourage them to drum up new prospects from unassigned leads in our database. By looking at timely predictive behavior scores, they can capitalize on recent account engagement by identifying new messaging or campaigns to send prospects who might otherwise have been overlooked.
As your company scales, you can expect more headcount scrutiny from the finance department. If you back into the math, you can determine – down to a science – how much additional revenue to expect in month one, month two, etc. from each new rep that is hired. With predictive scoring, you can increase the accuracy of these estimates by measuring reps’ time to first deal, and adding granularity in terms of how many A-Leads and B-Leads they converted to opportunities and then closed/won deals. This insight makes it easy to see when you need to add another headcount, and can help determine realistic quotas for new folks.
Once you’ve minimized ramp time for new reps, a great way to further improve productivity is to route low-scoring leads directly into nurturing queues. You’ll ensure your reps don’t waste time on the wrong incoming leads, and free up more time for them to go back to their highest potential prospects regularly throughout the quarter and year. Depending on their inbound volumes, smaller sales organizations can even use this automated approach to fill the role of an SDR team and save on headcount.
Filtering can also help you find the hidden gold from outbound prospecting list buys. That said, be cautious not to indefinitely neglect leads in your nurture pile. It’s crucial to regularly scan nurture databases for older leads and accounts that are showing fresh buying signals, and refresh target account lists accordingly. Even if you just find 20 new deals from prioritizing cold lists or archived leads, imagine the ROI you’ll get from reaching out to those high-potential leads that otherwise would have fallen through the cracks.
Regardless of which sales processes work best for your business, don’t forget that sales is truly a marathon, not a sprint. Once you’re optimizing sales performance with these predictive techniques, here are two longer-term best practices to keep in mind. First of all, take the time to share results with your reps and finance stakeholders, so they can see the impact of your prioritization efforts and learn to trust the models. Secondly, remind your reps that even top leads won’t always be low-lying fruit. In the B2B world, very few leads close themselves, so it’s important to continuously use and fine-tune proven selling motions.
What’s great about giving reps their time back is that they’ll be able focus much more on things like account strategies, finding new prospects, and working with the marketing team to keep a steady feedback loop going. And providing more flexibility to help them maintain a healthy work/life balance will garner loyalty and reduce team churn – something that can have a major impact on your sales organization in the long run.
Nate Gemberling is the Director of Sales at Infer. Nate joined as Infer's first sales hire in 2013 tasked with architecting the framework for Infer's go-to-market strategies. He helped secure early paying customers, contribute to product roadmap, and develop scalable sales processes. Nate joined Infer by way of Customer Analytics leader, Nice Systems (NASDAQ: NICE), where he was a part of the successful acquisition of Merced Systems. Nate graduated from the Ralph and Luci Schey Sales Centre at Ohio University, the nation's first and highest ranked college sales programs, with a degree in Marketing and focus in Professional Sales.