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.

 

Introducing Similar Won Accounts for Infer Glance Sales Intelligence

Similar Won Accounts is the newest enhancement to the Infer Glance Sales Intelligence suite of products. With Glance Similar Won Accounts, sales and marketing users can gain immediately insights when researching prospects. This new feature analyzes closed won historical data from a customer’s CRM, and instantly identifies which current customers are most similar to the company being researched by the user. This information can then be used to deliver highly personalized and relevant messaging to prospects while also reducing the amount of time required for reps to craft their outreach. All of these benefits come together to ensure increased rep productivity, higher prospect engagement rates, and greater opportunity pipeline.

Infer Glance Sales Intelligence now includes Similar Won Account data to enable reps to make informed prospecting decisions

The Glance “Similar Won Accounts” feature bases its findings on the following criteria:

  • Competitors – Find current customers who are direct competitors to that prospect
  • Industry – Identify current customers in the same industry as the prospect
  • Revenue – Compare each prospect to current customers with similar annual revenue figures
  • Geography – Establish credibility with each prospect by referencing current customers headquartered in the same city
Similar Won Accounts provides analysis in four areas of similarity: Competitors, Industry, Revenue, and Geography

 

If you are interested in learning more about the new Glance Similar Won Account offering, please contact sales@infer.com.

Word on the Street: Predictive Advice from Infer Customers

Earlier this month, G2 Crowd sat down with Infer customers InsightSquared and Yesware to ask about how they use Infer’s predictive analytics and AI platform, what benefits they’ve seen, and their recommendations for other early adopters.

First up was Matthew Bellows, CEO of Yesware, a long-time customer of Infer. During the interview, he shared some helpful tidbits about how his sales team uses predictive to automatically qualify the best prospects from their high-volume lead flow, focus on the right accounts, and increase overall sales velocity and performance:

For more helpful advice from Yesware, check out this recent webinar with their director of demand gen, and learn how she was able to build a revenue-centric funnel with Infer. As a result, the company eliminated wasted sales efforts and won more deals.

G2 Crowd also chatted with Joe Chernov, VP of Marketing at InsightSquared, about how the company’s go-to-market teams use Infer to build alignment around the best accounts, and drive engagement as part of their account-based sales and marketing strategies.

Read our full snapshot to learn more about how InsightSquared is using Infer Predictive Scoring to make their marketing and sales machine much more efficient by identifying high-value leads and accounts, increasing conversions from top leads, and reducing overall cost-per-lead.

And for even more “word on the street” comments from other customers, browse Infer’s many reviews at G2 Crowd.

Account-Based Sales Development In A Predictive World

This interview was originally published on the Outreach blog by Jeremy Garbutt, Sales Development Manager at Infer.

Like any lean, fast-growing company, our sales team at Infer struggles with the age-old conundrum of quantity vs. quality. However, thanks to Outreach’s automation, we’ve been able to avoid spreading ourselves too thin. By using this great solution alongside our company’s own predictive and profiling platform, we’re tackling personalization at scale and really honing in on our highest quality leads.

Our sales development team lives in Outreach today. Whether we’re working inbound or outbound leads, Outreach makes it easy to automate what used to be manual workflows and sales sequences. We make this orchestration even smarter by adding a layer of predictive intelligence about both the fit of each account (i.e. how good a match are they for our product?) and the behavior of each lead (i.e. are they engaging with us enough to indicate that they’re likely to buy in the next three weeks?).

For lower quality inbound inquiries at the tail end of our lead universe, Outreach lets us fully automate all follow up and use email templates to save tons of time for our reps. As a result, we can now follow up with our very top quality inbound leads in less than 5 minutes. After our first contact, we use Outreach to cycle through different communications sequences (Email > Call > Social > phone > etc.) and make sure we’re staying in front of our best prospects with custom messages.

The difference between these two approaches is significant – our top leads get on average 10X more custom touches than our automated sequences for bottom leads. And this narrowed focus is paying off. We’ve boosted our conversion rate by 35% as a result of training our reps to prioritize quality accounts because they are accelerating follow-up, preparing targeted conversation points and sticking with these leads longer.

We also track the full spectrum of behavior we’re seeing from each individual lead in our Salesforce CRM and Pardot systems, so we can make sure that no missed opportunities fall through the cracks. Since we include predictive behavior scores as a distinct data point to inform our sales priorities (in addition to our less dynamic fit scores), we’re able to surface leads and accounts that are currently showing clear buying intent. Once a lower fit scoring lead responds to a sequence or crosses a behavioral threshold from our nurture programs, the account owner gets an alert that they’re in market to buy, and will start to pursue them more aggressively.

AdRoll Uses Infer to Increase Sales Performance Management and Marketing Effectiveness

AdRoll is a leading performance marketing platform with over 25,000 clients worldwide, and receives hundreds of thousands of inbound leads every year. To maintain its amazing growth trajectory and stay one step ahead of the competition, AdRoll has instilled a culture of data-driven decision making.

AdRoll uses Infer’s Predictive Platform to qualify and prioritize its best fit leads so that sales reps can focus their energy on the “fireballs” that are most likely to convert. The company also uses Infer to measure marketing effectiveness and efficiency by identifying which marketing channels and campaigns are driving the highest quality prospects. Not only has predictive intelligence helped AdRoll to fortify sales and marketing alignment, the company has also increased sales performance management with a 15% increase in deals per seller over their flourishing global org.

AdRoll’s Jessica Cross, Head of Customer Lifecycle Marketing, and Chris Turley, Global Head of Revenue Operations, sat down to share how they’ve incorporated Infer inside the organization.

Can predictive bring sales and marketing together?

Barb Mosher Zinck’s interview with Infer’s VP of Product Marketing, Sean Zinsmeister, originally appeared on Diginomica.

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Knowing which prospects and customers to focus time and effort on is critical for marketing and sales success. You can’t hit everyone; you have to hit the right ones. Predictive and AI can help.

How is the technology adapting to support sales intelligence? I spoke with one predictive sales and marketing platform vendor to get a feel for how the market is evolving.

In my discussion with Sean Zinsmeister, VP of Product Marketing at Infer, he talked about three main issues sales and marketing face.

The Inbound problem

Lead generation is the implementation of strategies to capture the attention of prospective customers. The goal is to get contact information to pass on to Sales for follow up and hopefully conversion. Successful lead generation can yield a lot of contacts, but not necessarily a lot of qualified leads.

So what happens when you are getting way too many leads coming in from Marketing? How do you know which ones to focus on? Which ones are the right ones?

Zinsmeister gave the example of one company that had too many leads pouring in, and it was taking Marketing 100 calls to generate one marketing qualified lead (MQL – a lead that’s most likely to buy). This company adopted predictive scoring and profiling to help it narrow down the best-fit prospects to follow up with and reduced the number of calls to 12 per MQL.

How does predictive scoring help? Not only does Infer look at a contact in terms of their interactions with your company (by looking at your CRM and marketing automation), it also mines the Web and other third party data looking at potentially thousands of data points, each weighted specific to the company’s requirements. Put all that profile information together, and score it and you have a better idea of which prospects are engaging more with your company at the time when they are ready to take the next step.

In the next 5 years, we are going to see a reimagining of automation through AI: An Interview with Sean Zinsmeister

This interview was originally published on TechSeen by Sean Zinsmeister, Vice President of Product Marketing at Infer.

While Artificial Intelligence was one topic that was covered ad nauseam in 2016, Infer, a predictive sales and marketing platform predicts that technology is going to evolve even faster from here on out. And this also bears an impact on martech solutions as organizations strive towards better accuracy, targeting, performance and engagement.

Sean Zinsmeister, VP, Product Marketing, Infer in an exclusive interview with Techseen discusses the growth of modern sales and marketing technology and how AI is all set to make a mark in 2017 too.

Techseen: While some predict mass unemployment or all-out war between humans and artificial intelligence, others foresee a less bleak future. What’s your take?

Zinsmeister: I think Google CEO Sundar Pichai had the best analogy, that AI is the new mobile. Given the saturation of smartphones and mobile apps, it would be insane not to include mobile in your digital strategy, whether for product development or go-to-market. Companies need to start thinking the same way about AI, and little by little add predictive and prescriptive analytics into workflows to increase the quality and productivity of everyday work.

With any new technological innovation, there is always some vocational disruption, but the upside is that AI has the ability to remove redundant tasks, increase productivity, and improve overall quality of work. It’s less about the removal of jobs and rather the evolution of jobs.

For example, sales and marketing professionals are overwhelmed with the amount of data they are faced with everyday. Sales reps need to know who to sell to, and marketers need to know who to market to, but there’s no way to employ enough humans to accurately answer these questions. AI helps companies automatically sift through troves of data to make sure they are picking winners and spending time in the right places.

4 Easy Tactics for Infusing AI and Predictive Analytics Into Sales Processes

This article was originally published on the Salesforce Blog by Sean Zinsmeister, Vice President of Product Marketing at Infer.

Unless you were hiding under a rock this year, you probably heard a thing or two about the rise of artificial intelligence (AI) for sales. As machine learning and predictive analytics technologies have rapidly matured, a whole community of forward-looking sales and marketing leaders are emerging as predictive innovators. Rather than relying on human intuition to inform their processes, these early adopters are leading the arms race for data by reinventing how their businesses operate based on intelligence that’s generated by AI and other related data science techniques.

In this environment, I’ve noticed four easy ways that smart sales leaders are hacking their team workflows to insert valuable data signals and key insights into day-to-day tasks—saving vast amounts of time and making sure all of their rep’s hard work is tightly aligned with the impact it delivers.

1. Use analytics to inform sales follow-up

There’s no doubt that confident and focused reps bring more opportunities into the pipeline. But it’s hard for them to feel confident when they’re given sparse lead records with little or no information about key buying signals – like a prospect’s fit for your product, or their likelihood to make a purchase soon based on marketing engagement. In order to avoid wasting hours every week researching leads, many teams are leveraging the latest predictive scoring and profiling technologies to create a habit of fast and efficient follow-up. When it’s easy for reps to prioritize the right prospects and plan their outreach, they follow-up more consistently, and as a result are more likely to hit their numbers each month.

For example, Shoretel is a company with a huge influx of leads, which market development reps individually call in order to qualify opportunity-ready MQLs. After adopting predictive analytics, the team started prioritizing their best-fit leads to qualify first, and MDRs went from having to call 100 leads to find 1 MQL, to just 12 calls per MQL – a huge productivity improvement.

With detailed information about each prospect, sales reps can also personalize every conversation for better engagement. By using advanced profiling techniques to create highly-segmented lists of prospects based on specific attributes and data signals (such as “VPs of Sales, in California, who use Salesforce, and have interacted with one of our marketing campaigns in the past 6 months”), reps can quickly sort out the best way to approach each group. For instance, that might send a particular piece of content or invite the prospects to a local meetup. Some tools even let you set up alerts for important events, auto-assign tasks to reps in Salesforce, and get recommendations powered by machine learning on which segments to invest more time into.

The Buyer’s Guide to Artificial Intelligence Software For Sales

This article was originally published on the Hubspot blog by Sean Zinsmeister, Vice President of Product Marketing at Infer.

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Salespeople have never had so much technology at their fingertips. Some of the latest — and possibly most promising — tools for sales teams use predictive analytics, a form of artificial intelligence technology that can optimize decision making around sales efforts. But with all the products promising to tell the future, it’s hard to discern which can actually deliver.

Top players like Salesforce and Microsoft have rolled out AI-driven tools over the last year. Investment in AI startups is at an all-time high. This type of software uses techniques that gathers customer and prospect data from multiple sources, runs it through machine learning models to predict which leads are most likely to convert, and present the findings to Sales in the form of top-scoring prospects and accounts.

So how do you know which tool, if any, is the best bet for meeting your sales goals?

What to Look For in a Sales AI Vendor

Predictive analytics for Sales truly can change the way you run your sales operation. AI-driven software can eliminate a great deal of manual work, helping you make decisions on how to approach prospects, personalize your conversations, and most importantly, focus on the leads that deserve extensive follow-up. For initiatives like moving up-market or adopting an account-based strategy, AI might be the only scalable way to do it.

There a few aspects which each vendor should be evaluated on to ensure you receive a strong return from your investment. Vet each vendor with the following five criteria before making a purchase.

Leveraging AI for Sales & Marketing – Beyond the Hype

This article was originally published on the OpenView blog by Sean Zinsmeister, Vice President of Product Marketing at Infer.

The hype around AI technology is at all-time high, with the market forecasted to reach $37 billion by 2025. In sales and marketing, the potential for 10x conversion rates and accelerated growth from AI-driven predictive analytics is enticing. But what’s really possible and what’s still in the distant future?

Sales and marketing are ranking in the top three markets most heavily affected by the boom in AI investment, according to sources like CB Insights and O’Reilly Media. Industry giants are announcing more AI plays—Salesforce acquiring Metamind and launching Einstein, or Microsoft rolling out Dynamics 365 with AI technologies Cortana, PowerBI, and Azure Machine Learning.

However, there’s a disconnect between what AI can do and what customers and investors expect. Buyers of AI-powered tools aren’t always familiar with the underlying technology, nor do they know which vendors can deliver on promises of accurate and useful predictions.

Predictive Analytics vs Other AI Technology

In sales and marketing, predictive analytics is a type of AI that has gained impressive momentum in recent years. Companies use this technology to predict which leads will become customers based on traits or behavior that indicates their likelihood to buy, then make decisions on how best to pursue those opportunities.

Predictive analytics isn’t the only type of AI used in sales and marketing applications, but it is where a majority of innovation is happening today. Although it’s easy to be enticed by the idea of fully-automated AI workflows or robot virtual assistants that are powered by deep learning and natural language processing (NLP), those technologies aren’t yet ready for implementation on a large scale. Predictive analytics offers tools that work now, making decision-making easier for startup sales and marketing teams who are increasingly inundated with data.