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.
Barb Mosher Zinck’s interview with Infer’s VP of Product Marketing, Sean Zinsmeister, originally appeared on Diginomica.
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.
Many of our customers come to us with a common problem: they have no good way to differentiate best-fit prospects from the tire-kickers, and are often left to rely on “gut instinct” when it comes to prioritizing who they should target. This was a particular pain point for UserVoice, who needed a way to more efficiently prioritize lead flow so their reps could focus their effort on those prospects with the most revenue potential. Additionally, both the sales and marketing teams wanted more transparency into what attributes defined an ideal customer profile so they could personalize and prioritize high-value outreach to these buyers.
UserVoice deployed a fit-based Infer Predictive Scoring model, and is now able to identify and prioritize leads based on how likely they are to purchase the company’s product management software. Armed with new predictive insights, the company saw a 2x increase in conversion rates and a 37% increase in marketing-qualified leads.
Connor Fee, COO at UserVoice, recently joined us to share his company’s predictive intelligence story, and how Infer has become a core technology in their sales and marketing stack:
This article was originally published on the Hubspot Blog by Infer customer, Nicholas Heim, Director of Inbound Marketing at Hotjar.
Hypergrowth SaaS businesses like my company, Hotjar, are often faced with the happy problem of too many free trial leads flooding into HubSpot. Thanks to lots of word of mouth buzz around our mission to democratize user analytics, and some clever advertising, we took off fast a couple years ago.
I wasn’t around for the early “fresh-out-of-beta days,” but as a newer team member, it’s quite nice to stand on the shoulders of an amazing product and founders who are true visionaries. When I joined around nine months ago, it had become challenging for our team to vet which leads (out of around 600+ new users each day) to target for premium business and agency plans.
While we get tons of value from using HubSpot for both our CRM and marketing automation needs, we couldn’t properly segment and personalize messages for our highest potential users out of the gate.
That is, until we added Infer Predictive Scoring to the mix. Now, we have a custom predictive model that works with HubSpot and our Intercom customer messaging platform to provide accurate data-based predictions of how well each lead matches Hotjar’s ideal customer.
Here’s what we’ve learned by using predictive intelligence to inform more advanced sales and marketing tactics.
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.
We’re back with another episode of Stack & Flow! This week we’re joined by Top Predictive Innovator Carolyn Wellsfry Cheng from Shoretel to discuss the differences between account-based marketing and selling, which strategy she predicts is here to stay, and why buying flashy tools isn’t always the best choice.
This week, we’re joined by Justin Norris, Solutions Architect at Perkuto to talk about how his experience with hundreds of Marketo-based sales and marketing stacks has given him a unique perspective on sales automation, lead routing, and architecting solutions that string together many technologies and systems to do awesome things. Justin also chats with the hosts about his thoughts on B2B advertising and why ABM is making outbound cool again.
It’s always a pleasure to uncover a new predictive innovator in our customer community, especially when they tell us that they were able to make an impact within just one week of adopting Infer. For example, content management software company DNN saw a 25% jump in its lead-to-opportunity conversions with Infer, along with a 75% jump in conversations to MQLs for its top group of leads. This success was the result of using Infer’s predictive models to find DNN’s highest revenue potential prospects, and gaining clear visibility into which sources and marketing channels generate the very best leads.
We recently had the pleasure of sitting down with Franck Ardourel, DNN’s director of marketing, who elaborated on the impact Infer’s predictive sales and marketing platform has had on his organization:
Additionally, you can download the full snapshot to learn more about how DNN is using Infer Predictive Scoring to:
- Identify the leads most likely to convert to customers.
- Improve sales prioritization, and increase new business opportunity conversion rates.
- Optimize lead gen acquisition programs in order to consistently produce higher quality leads and increase ROI.