Tips for Rapidly Turning New Headcount into Revenue

This article was originally published on the Salesforce Blog, by Nate Gemberling, Infer’s Director of Sales

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.

Time-saving tips for sales teams

Here are some specific best practices that I’ve personally used to help boost sales productivity:

1) Get creative with new rep training

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.

2) Quantify ramp time

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.

3) Filter inbound & net new lookalike leads

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.

 

Why Can’t All Companies Operate with the Same Data-Driven Intelligence as Google or Amazon.com?

Salesforce_Logo_2009 (1)Originally posted on PandoDaily by Jamie Grenney

When I joined Salesforce.com in 2002, the question we were trying to answer was “Why isn’t all enterprise software as easy to use as Amazon.com?” That simple idea gave rise to a billion dollar business.

The cloud-computing model was so disruptive because it dramatically reduced the risk and lowered the total cost of ownership for software. For the first time, companies of all sizes were able to successfully adopt CRM systems. Today I believe we are on the precipice of another disruptive shift. One that is going to unfold quickly and unlock huge productivity gains for companies.

Why Most Product Managers Suck

Originally posted on TheNextWebbrainstorm-idea-730x503

The first product manager (PM) is a crucial unicorn hire that no startup should compromise on. The reason is simple – your PM is responsible for managing your team’s most precious resource: time.

Unfortunately, nearly everyone seems to think they’d make a great PM (engineers, consultants, you name it), but the reality is that most folks just can’t hack it. I’ve worked with countless PMs at huge companies like Yahoo and Google, and over the past two months have interviewed over twenty PM candidates.

Top 20 Sales and Marketing Thought Leaders Who Are Influencing Predictive Scoring

img_top20-color

As we’ve been building Infer’s predictive lead scoring engine over the past three years, we’ve also been following many brilliant thought leaders who are contributing to a range of discussions near and dear to our hearts. So in the spirit of Valentine’s Day, we thought it’d be fun to survey our team on their favorite influencers in the space, and send some love their way. We’ve compiled part of that list below — spanning experts in the realms of CRM, marketing technology and general marketing and sales best practices. Our list of top data science and predictive analytics experts is published here.

What will CRM look like in 2015?

GigaOmWhen I joined Salesforce.com in 2002, the question was, why isn’t enterprise software as easy to use as Amazon.com? That simple idea transformed an industry and gave rise to a $24 billion dollar business. With enterprise cloud computing, the technical barriers to CRM adoption were overcome, providing a clear path to CRM success.

A little more than ten years later, I believe we are on the precipice of another disruptive shift. The question we are asking today is, why aren’t companies able to operate with the same data-driven intelligence as an Amazon? As I see it, there are at least two major obstacles holding us back.

Continue Reading on GigaOm

Customers are the New Oil: Beyond Sales Wildcatting

“Data is the New Oil” has become a pretty tired phrase, and a strangely circular one — nowhere has advances in data practices and technology had more impact than in the search for oil. Maybe “Data is the New Oil for Finding Oil?”
For those of us in B2B Sales and Marketing, a more interesting analogy is between Oil and Customers. Customers are what we seek, our goal, the thing that — when we find them — pays.  For us, Customers are the (New) Oil. 

The early days of the American Oil industry was dominated by Wildcatters, folks who acted on hunches or rules of thumb and drilled speculative wells. If a well was dry, they tried again somewhere else, continuing until, hopefully, they eventually hit a gusher.