This week, we’re joined by Jeff Canada, Global Marketing Operations Manager at Quantcast to talk about the greatest challenges to the modern marketer: personalization, localization, the death of the newsletter and more!
The more data-driven marketing becomes, the easier it is for CMOs to attribute closed deals directly to their marketing programs. But with so much information available they encounter a new challenge: knowing which tactics and strategies to prioritize when each bad decision can cost thousands in missed opportunities.
Predictive analytics is surging in popularity among marketing leaders. It combines several components of artificial intelligence (AI) to predict which prospects are most likely to become customers. This technology eliminates a great deal of manual and redundant work from marketing and sales analytics, letting reps spend more time on high-value outreach and lowering chances that a calculation error will cost the company important deals.
You can use predictive analytics to identify your most promising prospects, build hyper-targeted segments, and personalize outreach at scale—often resulting in significantly increased conversion rates on inbound and outbound campaigns.
But not all predictive technology is equal. As more companies adopt it for marketing, the competitive edge shifts from whether you’re using it to how.
This article was originally published on MarTech Advisors by Sean Zinsmeister, Senior Director of Product Marketing at Infer.
Sean Zinsmeister, Senior Director of Product Marketing at Infer discusses five technology trends which can be expected for CMOs in 2017.
As another year comes to a close, it’s always fun to reflect on the changes our industry has seen and how it’s likely to evolve even more over the next twelve months. The topic of marketing and sales technology has been hotter than ever as many new tools have emerged in areas such as account-based marketing (ABM), many applications have faded from the limelight, and the big vendors have all made big artificial intelligence (AI) moves.
Here are five specific trends I think we’ll see in 2017 as the martech landscape continues to advance:
We are so excited to bring you a new episode of Stack & Flow with our latest guest, Adrian Chang. He is the Director of Customer Programs at Oracle Eloqua, where he has worked for nearly ten years, and is a marketing automation pro. Obviously no stranger to the ever-evolving world of martech, Adrian helps the hosts break down this year’s Gartner Hype Cycle for Digital Marketing and Advertising, including why companies are investing more in technologies that will help marketers deliver the best of the brand with customer data. He also shares how to get started with machine learning, and what trends will be big in 2017.
This article was originally published on MarTech Today by Sean Zinsmeister, Senior Director of Product Marketing at Infer.
The sales and marketing industry has been abuzz with talk of predictive analytics, machine learning and artificial intelligence (AI) this fall, especially on the heels of a flurry of AI updates from Microsoft and Oracle, Salesforce’s recent Einstein announcement at Dreamforce, and Google’s unveiling of its efforts in machine learning and AI yesterday.
In all of this hype, I’ve encountered several conflicting definitions and explanations of what AI really means.
Those of us close to the space know that AI, at its core, is actually foundational technology that’s been around in the consumer world for over a decade.
Think of the amazing intelligence behind Google Photos, which uses facial recognition technology to organize your images for you, as well as the highly accurate music recommendations that you get from Pandora based on your likes and dislikes. We see similar examples in major league baseball (remember Moneyball?) and, of course, the fast-evolving world of Uber, Waze and self-driving cars.
As AI enters the enterprise realm — in what Constellation Research predicts will be a $100 billion market by 2025 — it’s important to shift our focus away from science fiction perceptions, and instead look toward the specific business outcomes that AI can produce.
To help cut through the noise, I’ve outlined below four key roles that analytics plays in the sales and marketing landscape. Keep in mind that each of these approaches delivers insights based on sophisticated data processing, modeling and other scientific techniques — all of which are important aspects of AI.
G2Crowd is disrupting the traditional analyst quadrants. The online B2B software review company has built an impressive community made up of over 100,000 crowdsourced reviews from real-life users, then turns those thousands of data points into trend reports, a la Gartner and Forrester. In this episode of Stack & Flow, G2’s Chief Marketing Officer, Adrienne Weissman, shares her thoughts on how consumer review sites have helped to shape the B2B software buyer’s expectations, what the relatively young company is doing to drum up demand generation, and why good project management helps align their sales and marketing team for better ideation and execution.