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.

 

Guest Post: How to Use AI to Ramp Your Sales Team by Ben Daters

A rep’s first 30 days can define their career.

If a rep has a successful on-boarding and ramping process, the world is their oyster. If their onboarding and ramping experience is inconsistent and they aren’t given the tools to succeed, the rep will likely fail.

Twenty years ago, sales reps were given a phone, a phone book and a conference room, as part of their ramping process. And many managers felt like they were managing a revolving door.

Today, managers take a different approach to hiring reps.

Managers are using tools like People.ai, Infer, Outreach.io and others to ensure their reps are successful from Day 1. By using these tools, managers can on-board and ramp their reps based on proven success (no phone books here).

Next week, Michael Raab, Head of SMB at Lyft, Nate Germberling, Director of Sales at Infer and myself, will be discussing 3 Ways to Quickly Ramp Sales Reps Using AI on a live webinar. This won’t be your run of the mill, death by PowerPoint webinar but rather a lively conversation on what we’ve seen work (and not work) in our combined 28 years of sales leadership.

Take a moment and register now!  

About Ben:

Ben Daters it the VP of Sales at People.ai. During Ben’s time at Marketo, he learned all areas of the sales function and quickly mastered complex solution selling in an extremely competitive market landscape. Ben’s passion for coaching and leadership helped drive the company through an IPO and acquisition. He was a key member as the company grew from 50M to 250M in annual revenue.

Dr. Massimo Mazzotti of The University of Berkeley – Algorithmic Life [Podcast]

Guest Bio:

Massimo Mazzotti’s research interests lie at the intersection of the history of science and science studies. He is especially interested in the historicity and situatedness of mathematics, logic, and deductive reasoning, and in the social processes that can make them universally valid. He is also interested in using technological systems and artifacts as ways of entry for the explorations of specific forms of social organization and power distribution. His past and present research projects have focused primarily on the early modern and Enlightenment period, with significant incursions into the nineteenth and twentieth century.

Episode Breakdown:

In this special episode, Dr. Mazzotti talks about Algorithmic Life and:

  • The changing definition of intelligence
  • Political affects of algorithms
  • An historical perspective on AI
  • The Clock as technical disruption

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We’re always looking for inspiration, so please drop us a line if there’s any topics you’d like us to cover or any guests you’d like to see interviewed — please feel free to comment below or fill out the suggestion form on the Stack & Flow website. If you love what you hear, please leave us a review on iTunes.

Oh How the Tables Have Turned: What B2C Marketers Can Learn From B2B

This co-authored byline was originally published on CMSWire by Sean Zinsmeister, Vice President of Product Marketing at Infer, and Adrian Chang, Director of Customer Marketing at Oracle Marketing Cloud.

Business-to-business (B2B) sales and marketing are entirely different from business-to-consumer (B2C) tactics — or that’s the general assumption.

One is relatively low volume and high budget, with lengthy, consultative selling processes and lots of personal relationships at play, while the other usually means huge volumes and low price points, with fast, direct sales processes.

B2C relies on consumers, transaction events, impulse buys and coupons. B2B is all about prospects, “journeys” and reaching people through content in context.

But more and more, we’re seeing these two universes converge.

Although we often perceive B2B companies as one step behind their B2C counterparts when it comes to adopting the latest sales, marketing and advertising techniques, that is quickly changing. B2C marketers have naturally excelled at bringing as many people as possible into the top of the funnel, but B2B companies have perfected the use of intelligence and personalization to move multi-stakeholder buying committees through non-linear customer journeys.

Today, many B2C businesses are finding that at a certain threshold, the price points and buying cycles of a considered consumer purchase are beginning to look almost like a B2B deal.

In this environment, B2C companies are turning the tables and adopting the latest B2B marketing approaches and technologies, to great benefit.

4 Ways B2C Can Benefit From a B2B Martech Stack

1. Adopt Hyper-Segmentation for a Better Understanding of the Ideal Customer

Most B2C technologies focus on point-in-time transactions across a massive prospect universe. But they aren’t great at capturing detailed profile and activity data on each of those customers.

While they ingest plenty of basic demographic signals from search patterns and lifestyle purchase history, much of that information is anonymous. Anonymization makes it challenging for marketers to understand and analyze the data, since they can’t link consumer patterns with internal data from custom forms or other identifiable insights.

B2B systems, on the other hand, track a complex web of detailed attributes and match activities to purchases across each customer at every stage in the buying journey. They are well-equipped to help marketers segment their total addressable market deeply and precisely across all types of customer signals and behaviors.

A consumer tech or financial services company might want to segment its customers based on their travel profiles (many of which overlap), or a home rental agency might consider not only what types of properties a consumer is interested in, but also what city they live in or which industry they work for.

B2B predictive platforms can deliver valuable insight into these situations by producing easy-to-understand customer predictions that inform the segmentation process so marketers can determine which profiles should receive high-touch, personalized outreach verses low-cost, low-touch campaigns.

2. Automate Customer Journeys to Reach Buyers at Every Stage

As opposed to the point-and-click purchases of the consumer world, in the B2B model, buying processes tend to be more intricate and involve multiple people. B2B marketing automation platforms support a more complicated funnel that helps marketers plan, prioritize and execute campaigns knowing that certain interactions are bigger influencers to winning a customer than others.

Think about the evolution fast-food chains like McDonald’s have undergone over the past few decades. Rather than organizing their prep staff to assemble Big Macs and other menu items based on a rough estimate of what most people usually order and when lunch and dinner rushes occur, most of these businesses have switched to made-for-you systems in which food is assembled as it is ordered for better quality.

B2C companies are poised for a similar transition away from one-size-fits-all marketing as they adopt B2B martech systems to better orchestrate end-to-end, dynamic automation across their many channels and segments. Predictive technologies inform these tailored workflows by showing which assets, campaigns or channels reach a company’s best-fit customers or profiles.

3. Increase Personalization to Boost Advertising ROI

While B2C marketing can rely heavily on brand advertising to push decision makers over the line, in B2B, brand awareness might help get a vendor into consideration, but won’t close the deal alone. Because B2B systems collect all kinds of customer intelligence, they do an excellent job of helping marketers go beyond general brand-building through profiling, predictive modeling and personalization at scale.

B2B martech stacks bring better data and richer profiles to the task of executing hyper-segmentation strategies, workflow automation and dynamic content — all of which combine to deliver both higher short-term conversions and greater customer loyalty over the long-term.

For example, Dell leverages the same marketing automation platform to power both the B2C and B2B sides of its business. As a result, its marketers have a better understanding of their ideal customers and can draw these prospects into more intimate, personal conversations with the brand.

4. Use Full-Funnel Insights to Drive Engagement

With all of the apps launched during the recent martech explosion, integration causes major headaches, leaving dangerous blind spots between some go-to-market workflows. It doesn’t help that many B2C interactions happen offline, compounding the challenge of collecting and interpreting data across the entire customer funnel.

The good news is B2B systems generally “play together” better than B2C tools because they offer more open APIs, and were made from the get-go to deliver end-to-end insights to a sales audience.

As opposed to fueling the sales and marketing divide by keeping people siloed in different B2C systems, seamlessly integrated B2B platforms can bring all of the functions together. For instance, predictive platforms can learn from historical sales data to model what a good customer looks like, and apply that intelligence to the top of the funnel, which then cascades through marketing automation programs and all the way down the funnel to help customer success teams load-balance customers.

B2C companies using this type of intelligence throughout their business will grow consumers’ loyalty, increase retention and find new ways to delight people.

It’s Time for B2C to Flip the Funnel

Over five years ago, Joseph Jaffe started a B2C conversation about “flipping the funnel” (a term which has since been hijacked and distorted by the ABM community), yet few B2C companies have fully adopted the mindset. In comparison, B2B marketers inherently follow Jaffe’s approach, because it is built into the very way they sell.

As more noise infiltrates a consumer’s purchase decision, it’s becoming harder for B2C marketers to drive conversions with mass-messaging. It is time for B2C to take a cue from B2B and finally double down on one-to-one relationships.

AI 101, Part II: How to Deal with Data Preparation

This article was originally published on MarTech Series by Sean Zinsmeister, Vice President of Product Marketing at Infer.

My first post in this series covered what marketers and sales leaders need to know about the four main phases of building predictive models. The second of these steps – data preparation – tends to be the least understood part of AI and predictive analytics in marketing. In this next post, I’ll dig deeper into key considerations surrounding this process, namely related to data volume and data quality. When my company introduces our predictive platform to companies, two of the biggest concerns we hear are: (1) Do I have enough data? and (2) Is my data “clean” enough?

Read This: AI 101, Part I: What You Need to Know about Predictive Models

HOW MUCH DATA IS NEEDED FOR MACHINE LEARNING?

There’s a rule of thumb for how much data you need in order to be successful with a predictive model, and the most important number is the amount of positive signals or “good” examples there are in your data set. In the case of historical customer data for lead or account scoring, this would be how many total opportunities or closed/won deals you have in your CRM database.

Of course, these positive signals exist among other negatives. Make sure your positive is defined as a relatively significant achievement in the pipeline. For example, the creation of an opportunity is a meaningless milestone if it happens for every single free trial that comes in. Instead, consider going further down the funnel to find a tougher hurdle that really points to lead quality. infographic clean

Predictions will be most accurate when you have around 400 to 500 of these positive results. In that range, they can be randomized and split into two proportions (60% and 40%) for model comparison. If you have fewer than a hundred examples to test your model over, your results won’t be quite as precise as you might want (until you add more data over time and refresh the model).infographic via Sean Zinsmeister

HOW AI SOLVES THE DATA HYGIENE PROBLEM

The truth is that no business has perfect quality, complete data, but that’s okay. Modern data preparation techniques are built to work around that very problem, so there’s no need to delay AI initiatives while you wade through cumbersome data clean-up projects. If you do, you’ll just leave revenue opportunities on the table. By matching whatever limited lead data you have with hundreds of external signals from the web, predictive platforms like Infer can build a complete picture of each prospect or customer. In fact, our algorithms can produce lead scores with nothing more than a company name or an email address. That’s thanks to advanced data science approaches like Natural Language Processing (NLP), which can bridge gaps in your data by looking for patterns in the web crawls, performing title normalization and doing spam analysis on form input.

TITLE NORMALIZATION

Anyone who has sold into IT or the sales and marketing industry knows that job titles are all over the place (or sometimes not included in the data at all). Title normalization techniques tend to be especially important for lead fit models because you need to know that “Marketing Director” might be equivalent to “demand gen lead,” or that “IBM” and “International Business Machines” are the same company. NLP essentially splits out each word that exists across all of your records and uses an algorithm to assess related patterns and find the words that show up most often in positive outcomes for a particular data set.

SPAM ANALYSIS

Another sophisticated feature to look for is spam analysis – something that’s often used in consumer search algorithms like Google. By analyzing the number of capitalized characters and key input for a name, company, title or email, you can assess the likelihood that each data point is a legitimate input. For example, the way a person’s fingers traveled across the keyboard (i.e. the number of row switches, etc.) often indicates whether their entry is legitimate. An email like asdf@ggg.com doesn’t travel very far and is probably not a real address. Machine-learning can perform these checks on every single record, regardless of whether or not it matches a known website domain.

As you can imagine, NLP alone can help you immediately improve your data hygiene. That’s why, instead of doing months of data cleansing first in hopes of being able to get better intelligence, later on, it’s smarter to get your predictive and AI initiatives started now, with the data you have. There’s no sense in spending time and money augmenting fields and cleaning up data that isn’t helpful for your models anyway. Rather, use machine-learning to figure out what your most important data points actually are, and then focus your data cleanup efforts there as needed.

It’s so important to understand common data science methodologies like these as you move forward, even if you never intend to work with the algorithms yourself. This knowledge will help you spot any flaws, unrealistic expectations, assumptions, and missing pieces in predictive and AI solutions so that you can thoughtfully evaluate them. In my next post, I’ll expand further on basic model types and more problems sales and marketing teams can solve with data.

Stella Garber of Trello – A Fully Distributed Global Team, Marketing a Horizontal Productivity Tool, and Kanban vs. Post-its [Podcast]

Guest Bio:

Stella Garber is Product Marketing Lead at Trello. Used by millions around the world, Trello is the visual collaboration tool that gives people perspective on projects. Stella built out Trello’s marketing team and scaled it from day one to Trello’s acquisition by Atlassian earlier this year.

Before Trello, Stella was CEO and cofounder and cofounder of matchist, a marketplace for freelance developers which was acquired in 2015. Stella also ran marketing and was on the founding team of FeeFighters, a venture backed payments startup acquired by Groupon 2012.

Episode Breakdown:

In this episode, Stella tells us about the rise of Trello, including:

  • Being fully distributed and global
  • The challenges of selling a horizontal solution
  • How Trello uses Trello
  • Building corporate culture through communication channels

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We’re always looking for inspiration, so please drop us a line if there’s any topics you’d like us to cover or any guests you’d like to see interviewed — please feel free to comment below or fill out the suggestion form on the Stack & Flow website. If you love what you hear, please leave us a review on iTunes.