AI Articles and Analysis about Marketing and advertising

When managers and leaders think about machine learning in retail, they often imagine helpful in-store robots, automated processes like checkout or stocking shelves, or conversational agents that suggest products and answer questions for customers. While these applications should be considered as disruptive factors for retail’s future, it’s unlikely that these factors will be the first wave of AI’s impact on the sector.

An increasing number of online companies are utilizing recommendation systems to increase user interaction and enrich shopping potential. Use cases of recommendation systems have been expanding rapidly across many aspects of eCommerce and online media over the last 4-5 years, and we expect this trend to continue.

Episode summary: In this episode we talk to Lior Tasman who is the CEO of PredictiveBid, an Israeli-based predictive advertising optimization start-up. The team focuses on applying AI to some of the bigger issues in programmatic advertising to help draw out more ROI from ads. We discuss some of the challenges of programmatic advertising and what the future of programmatic advertising may look like from an advertiser’s perspective.

Episode summary: In this episode we talk to Adam Spector, the Co-Founder & Chief Business Officer at LiftIgniter, a company which provide a service which modulates website experience per users, for an array of different businesses. Adam and I discuss what the tech giants are doing to customize their business experiences, what data they’re using to continually alter user experience and what industries and sectors might be impacted by this aggregate trend as it moves forward.

Episode Summary: In this episode we speak with Co-founder and CEO Alex Holub of  Vidora about how businesses, particularly in the digital and B2C spaces, can improve marketing results with AI. Holub discusses the resources needed—time, money, in-house or outside expertise, calibration, and data—in order to leverage AI in a realistic way. It's safe to say that today, some businesses are not yet set up to be leveraging AI, while others should be seriously considering taking the leap to using machine learning in their marketing processes. Holub draws some firm lines as to what kinds of businesses are primed to take advantage of AI, and what it takes to flip the switch and make AI a useful and inspired revenue driver in the marketing domain. Many of Holub's useful insights are echoed in our machine learning in marketing consensus from last month, also worth reading if you're interested in additional first-hand perspectives from executives using AI in the marketing space. Alex was introduced to us by our friends are BootstrapLabs.

Episode Summary: Getting beyond the marketing and jargon on the home pages of AI companies and figuring out what's actually happening, what results are being driven in business, is part of our job at Emerj. Shaking those answers out of founders is not always easy, but we didn't have to do much shaking with Yohai Sabag, chief data scientist for Optimove, a marketing AI and automation company in Israel. In this episode, he speaks about what humans are needed for in the optimization process, and what facets can be automated or distributed to a machine. Sabag gives an excellent walk-through of how marketers can use the "human-machine feedback loop" for artificial intelligence in marketing optimization at scale.

Technology Provider: Optimove

User Company: Adore Me

Industry: Consumer Retail

When it comes to business applications of machine learning, marketing is always near the top of the list. Modern digital marketing offers a huge volume of quantifiable data for teams to work with, and marketing can be said to take precedent over other areas like customer service and business intelligence because of it's direct tie to driving revenue. Machine learning marketing applications are still relatively novel for most small and medium-sized business, but this may change drastically over the next five years.