AI Articles and Analysis about Marketing and advertising

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.

Episode Summary: In marketing, there are lots of applications in AI and machine learning (ML), from recommendation engines to predictive analytics and beyond. At the company Albert, there are even more ambitious projects underway - like automating the process of marketing altogether by having a machine run and generate ads, or test and spend the marketing budget of a company. Or Shani, CEO of Adgorithms, focuses on the quantitative aspects and optimization of marketing, using algorithms to improve advertising processes. In this interview, Shani talks about how Adgorithms' smart marketing platform "Albert" meshes with humans in marketing, and also discusses how these roles might change over the next 5 to 10 years as we move towards an ever more automated marketplace.

Episode Summary: A medium-size business with a $20m marketing budget can run into issues when aiming to track an attribute, what marketing dollars brought in customers, etc. But when you're managing $90B for customers all over the world and working in every conceivable channel, things get all the more complicated. Josh Sutton, global head of Data and AI at Publicis.Sapient, speaks in this episode about the future of advertising attribution with machine learning. Specifically, Sutton discusses how his team of publicists is working on managing, tracking, and determining cohorts and attribution across more channels and numerous clients, and touches on ways that the company is applying ML to make sense of marketing data and spend marketing dollars more effectively.

Busy and multitasking are understatements for today's executives and entrepreneurs. Machine learning has the potential to help make businesses more efficient, competitive, and profitable, but learning how it works and finding the resources to implement this technology takes time. Where to apply machine learning when first getting started is dependent on a number of factors - industry, structure, current problems - but having an idea of which solutions have proved most efficient for others and derived maximum return on investment is a helpful jumping off point.

Episode Summary: We've interviewed a number of guests on Emerj, but very few who have had a serious part of their career in selling automobiles. But Michael Perry did just that for 5 years before founding Kit, his third startup - an AI-powered Virtual Employee that works in marketing for small businesses and was acquired by Shopify in April 2016. In this episode, Perry speaks about how Kit and Shopify leverage AI on a daily basis, and how a “non-tech” person with no formal background in AI or data science can build a team for an AI project.

Predictive analytics for marketing would have been adopted years ago - if only the compute power were more ubiquitous, the data were more accessible, and the software were easier to use. Now "predictive analytics" itself is almost a buzzword, after nearly 30 years of backward-looking marketing tracking.
Today, well over 30 years after the founding of Lotus Software, even medium-sized businesses are often still operating their marketing "scoreboards" in Google Sheets or One Drive... "throw it in a spreadsheet" still works.
But businesses with an eye on the future want to know more than just what happened in the past. "Scoreboards" (most analytics tools and tracking) don't tell you what the score will be. Some of our recent "AI for marketing" articles have gained readership because more and more executives are searching for ways to look forward with their numbers, not just back. SAS defines the term well: