In this episode of Stack & Flow, we’re talking all about how to keep your sales and marketing stack agile. Evan Liang, CEO of LeanData, discusses the benefits of lead management in this endeavor and the impact it has on the customer experience. Evan also shares how he prioritizes agility within LeanData’s expansive martech stack, which includes technologies like Builtwith, Datanize, Yesware, Outreach, SalesLoft, Persist IQ, Trello, SFDC, GoToMeeting, WebEx, Zoom, Capterra, UberFlip, DocuSign and Marketo!
It’s time for content marketers to change their thinking. We all have heard the cliche “quality over quantity” message, but we’re always left wondering how exactly to execute on such a nebulous strategy.
Last month, we co-hosted a Meetup with DNN to talk with Bay Area marketers about how predictive analytics can better inform their content marketing strategy. We had a great time sharing simple tactics and strategies for how they can use predictive insights to gain instant feedback on their content marketing programs, and gather insights like:
- Am I reaching the right audience (i.e., those that are most likely to buy your product)?
- What are my top-performing content pieces?
- What are my best content distribution channels?
To learn more about how predictive analytics and content marketing go hand-in-hand, you can view the deck from the event below. Additionally, we sat down with Sean Zinsmeister, Infer’s Senior Director of Product Marketing, to find out how his marketing team uses predictive insights in their own content marketing strategy, his predictions for the modern marketing organization, and more.
Today, we are excited to announce that we are launching our very own podcast series called Stack & Flow. Hosted by Infer’s Sean Zinsmeister and EventHero’s John Wall, the podcast is designed to be a platform for up and coming thought leaders to share how they’re solving technology and data challenges in modern businesses. With the sales and marketing automation landscape being reimagined everyday, we aim to provide listeners with tangible insights that will enable them to implement innovative programs, tactics and best practices to fuel go-to-market success in real-world scenarios.
Each episode will discuss key news stories and events in the sales and marketing technology space, and feature interviews with the top practitioners, strategists and influencers who are helping to shape a vision for the sales and marketing technology stack of tomorrow.
One of the best parts of Dreamforce is getting to meet so many pros who are changing how their companies do business and engage customers. This year’s event was a great reminder of all the passion for sales and marketing innovation that exists within the Infer community of folks who we have the pleasure of growing with and working alongside everyday. These innovators are helping to build and align their businesses using predictive intelligence, and many of them joined us at our booth to share how they leverage Infer. They talked about how they’ve reinvented the way their companies identify and target their best prospects to increase conversion rates, boost average deal size, optimize marketing spend, and more.
When we decided to celebrate the Top Predictive Sales and Marketing Innovators, it was hard to narrow it down to just 25. However, the following group of Infer customers are truly leading the pack when it comes to predictive sales and marketing. Each of these predictive practitioners has their own unique story of how they’re driving real and tangible value across their businesses, and they share a spirit of innovation and dedication to building data-driven sales and marketing organizations.
This article was originally published on the AdRoll Blog by Sean Zinsmeister, Senior Director of Product Marketing at Infer.
Part 1 – Amplifying Your Customer Platform with B2B Retargeting
Part 2 – Infusing Predictive Insights into ABM Prospecting
During my B2B marketing career, I’ve noticed one universal truth time and time again. Very few companies want to be first in the pool when it comes to adopting an emerging technology. Given this environment, I’m passionate about customer marketing campaigns—they’re a vital part of the marketing mix for Software as a Service (SaaS) businesses like Infer.
We’re always looking for new ways to shed light on the growing community of innovators who use our predictive intelligence to supercharge their sales and marketing technology stacks. That’s where account-based marketing (ABM) and AdRoll’s retargeting platform come in. My favorite use case for AdRoll is building trust for new technologies like ours by amplifying real-world proof of customer success via online channels. I love getting new voices out there and helping to grow the predictive category by driving awareness of their winning strategies.
Moderated by Kerry Cunningham of SiriusDecisions, Infer and Lattice Engines joined forces last month to discuss the key strategies and use cases to get your business started with predictive marketing and sales. We had a great time discussing best practices for how to deploy a predictive marketing solution, and why real customers are achieving success by infusing predictive analytics into their go-to-market machines.
In this webinar replay, you will learn:
- Several different approaches to launching a predictive solution.
- The top use cases for getting started with predictive analytics.
- How to measure success.
We hope you enjoy the webinar, which you can watch here.
Yesterday, I wrote about the last mile of AI and building sticky business applications through predictive technology. My final post in this series explores the tremendous overhype we’re seeing around AI, especially as larger players like Salesforce enter the game, and touches on when we should expect mass adoption.
Vendors in our space often over-promise and under-deliver, resulting in many churn cases, which, in turn, hurts the reputation of the predictive category overall. At first, this was just a problem with the startups in our space, but now we’re seeing it from the big companies as well. That’s even more dangerous, as they have bigger voice boxes and reach. It makes sense that the incumbents want to sprinkle AI-powered features into their existing products in order to quickly impact thousands of their customers. But with predictive, trust is paramount.
Historically, in the enterprise, the market has been accustomed to overhyped products that don’t ship for years from their initial marketing debuts. However, in this space, I’d argue that overhyping is the last thing you should do. You need to build trust and success first. You need to under-promise and over-deliver.
Yesterday, I wrote about the timely topic of artificial intelligence (AI), and what it means to be a technology that’s built to be AI-First, as opposed to AI-Later. My next post in this series digs into the “last mile” problems of AI that too many companies ignore, and which are critical to making your solutions sticky.
How do you get regular business users to depend on your predictions, even though they won’t understand all of the science that went into calculating them? You want them to trust the predictions, to understand how to best leverage them to drive value, and to change their workflows to depend on them.
This is the last mile problem. It is a very hard problem — and it’s a product problem, not a data scientist problem. Having an army of data scientists isn’t going to make this problem better. In fact, it may make it worse, as data scientists typically want to focus on modeling, which may lead to over-investing in that aspect versus thinking about the end-to-end user experience.
To solve last mile problems, vendors need to successfully tackle three critical components:
AI is hot, I mean really hot. VCs love it, pouring in over $1.5B in just the first half of this year. Consumer companies like Google and Facebook also love AI, with notable apps like Newsfeed, Messenger, Google Photos, Gmail and Search leveraging machine learning to improve their relevance. And it’s now spreading into the enterprise, with moves like Salesforce unveiling Einstein, Microsoft’s Cortana / Azure ML, Oracle with Intelligent App Cloud, SAP’s Application Intelligence, and Google with Tensorflow (and their TPUs).
As a founder of an emerging AI company in the enterprise space, I’ve been following these recent moves by the big titans closely because they put us (as well as many other ventures) in an interesting spot. How do we position ourselves and compete in this environment?
In this series, I’ll share some of my thoughts and experiences around the whole concept of AI-First, the “last mile” problems of AI that many companies ignore, the overhype issue that’s facing our industry today (especially as larger players enter the game), and my predictions for when we’ll reach mass AI adoption.
Chris Orlob, CEO of Conversature, recently hosted Sean Zinsmeister on his “Inside Sales Gurus” podcast. As its name implies, the podcast aims to educate sales professionals through interviews with sales thought leaders on various topics that will help them to build, scale, and manage high velocity teams. In this special episode, Sean discusses product launches from a marketer’s “insider” perspective to help shed light on what sales teams should know about the process in order to maximize the success of the launch –and their quotas.
You can listen below, or on our podcast channel on SoundCloud.