One way marketers can get a more clear view of their customer’s journey is by creating a data warehouse that centralizes marketing data across various channels, said Chris Wexler, senior vice president of media and analytics at ad agency Cramer-Krasselt.
The push for more effective ad targeting remains one of marketers’ chief occupations. More than half of client-side marketers surveyed by Econsultancy and Adobe said leveraging data for more effective segmentation and targeting is among their top three organizational priorities this year.
Identifying the best channel metrics that align with companywide key performance indicators is a challenge at a time when marketers are overloaded with data and their companies are questioning those results.
In this Tech Talk Tuesday presentation, Marketo’s Head of Commercial Demand Generation, Mike Madden, breaks down the powerful use cases of triggered campaigns, from scoring specific actions and prioritizing leads for sales to creating timely emails from website visits.
The belief that consumers crave more targeted, personalized ads has become a digital advertising mantra. But it’s not entirely true.
The New York Department of Financial Services has launched an investigation into Facebook’s reported collection of data from third-party apps. According to The Wall Street Journal, the social media platform has been using partnerships with third-party apps to collect personal information on both Facebook and non-Facebook users.
Because of in-app ad spend's recent surge, getting accurate in-app viewability measurements is a big deal for mobile marketers. We forecast that $77.03 billion will be spent on in-app advertising in the US this year, up 25.1% over 2018.
Training an artificial intelligence (AI) algorithm requires data—lots of data. But staying GDPR-compliant while acquiring that data can be almost impossible.
Technology is the means to transformation, not an end in itself. Rigid internal structures impede many organizations' digital transformation efforts.
There's a lot of potential for programmatic advertising in account-based marketing, but a foundation must be put in place first.
Government regulation is the top obstacle threatening marketers’ data projects this year, according to a recent survey of US marketers by Winterberry Group and the Interactive Advertising Bureau (IAB).
Having too many data management systems in use is a daily challenge for 40% of the decision-makers and data managers Vanson Bourne and Veritas surveyed. A similar number of respondents said there are too many data sources to make sense of.
Half of US internet users have concerns about facial recognition, according to data from The Brookings Institution.
Data science and analytics will be the technical skills most needed at ad agencies in the next two years, according to a poll by Marketing Land.
Companies may know that more advanced attribution practices are needed to prove marketing value in today’s complex media world, but that doesn’t mean they understand, or easily embrace, these practices.
We spoke with Grégoire Baret, general manager of omnichannel experience at shoe retailer Aldo, about how his team works in collaboration with IT to roll out a new marketing technology.
In a poll conducted by ad measurement firm Integral Ad Science (IAS), 69.0% of agency executives say that fraud is the biggest hindrance to ad budget growth, compared with more than half (52.6%) of brand professionals who said the same.
Some marketers turn to data scientists as they look to improve their ad measurement and digital attribution capabilities.
One of the biggest trends in advertising this year will be consumer privacy and security concerns, which has forced marketers to get their data houses in order.
As use of AI grows (27% of executives in a PwC study have already implemented AI), so do calls for ways to interpret how AI models make decisions. This has given rise to a new buzzword: explainable AI, which refers to algorithms that make decisions humans can explain. PwC, for example, says it “integrates risk mitigation and ethical concerns into algorithms and data sets from the start.”