Are you struggling to keep up with the evolving digital marketing landscape?
That’s where marketing technology – or MarTech – comes in. The right MarTech can help you automate tasks and streamline your workflow for better performance.
But how do you upgrade your MarTech stack to maximize campaign effectiveness?
On November 10, I moderated a webinar by iQuanti’s Vishal Maru, VP of Digital Solutions, and Shaubhik Ray, Senior Director of Digital Analytics, as well as Tealium’s Josh Wolf, Director of Partner Solutions Consulting.
Maru, Ray, and Wolf walked through some of the leading tools and solutions you should consider including in your MarTech stack for 2023 and beyond.
Here is a summary of the webinar. To access the entire presentation, complete the form.
Characteristics Of A Mature Martech Stack
A mature MarTech Stack should cover four categories:
- Data management.
The tools you select should deliver insights in each category to help you make informed decisions.
Key MarTech Stack Recommendations For 2023
Making critical marketing decisions will need to rely on data. But how do you distribute data client side and server side?
Enable A Consent Framework To Get Around Issues With Third-Party Cookies
From the point of view of privacy, you can set up a framework to support GDPR in Europe, CCPA in America, and all of the different privacy rules.
Using consent management to govern that data lets you do the right thing with your customers’ information.
Develop Universal Identifiers To Understand Each Member Of Your Target Audience
Universal Identifiers are identifiers created to identify an individual within or across ad networks.
Creating an individual’s unique profile helps to understand their needs and interests.
Use this information to deliver a customized message to everyone.
Use Cookieless, World-Proof Targeting Solutions
A couple of solutions are coming up to solve targeting issues that the deprecation of third-party cookies will cause.
You can use cookie-free predictive audiences to target mobile, desktop, CTV, audio, and YouTube users.
The other solution in this on-demand webinar enables you to get critical insights on existing customers and similar audiences on different platforms.
Leverage Artificial Intelligence & Machine Learning
A robust AI platform helps brands analyze and use larger volumes of data to personalize their customer experience.
You’ll be able to:
- Implement predictive analytics to extract more granular insights from data.
- Improve forecasting or segmentation precision.
- Scale marketing use cases across organizations.
Invest In Experience Analytics
Experience analytics platforms help you understand the “why” behind something that is or isn’t working through:
- Heat maps.
- Session recordings.
- Journey maps.
- Deeper insights into user behavior.
- Website troubleshooting.
Maximize Ads With AI-Led Creative Analytics
Creative quality determines 75% of ad impact, according to Nielsen.
However, there isn’t a solid analytical approach to optimizing creative performance.
Usually, people focus on bidding, but they’re not looking at how their creatives impact ad performance.
Some platforms are harnessing the power of AI to gather deeper insights into creative performance and drive better leads.
How To Get Started
Now that you know about all the platforms that you should explore and how an actual MarTech assessment looks, you can take those insights and build or enhance your stack accordingly.
To start, you’ll need to:
- Build cross-functional teams.
- Identify key business priorities.
- Conduct an assessment.
- Build a strategy.
- Identify investments.
[Slides] How To Build A Winning MarTech Stack In 2023
Here’s the presentation:
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Mozilla Acquires Pulse, A Hybrid-Workplace Collaboration Company
Mozilla, publishers of Firefox, acquired the team behind AI-based workplace collaboration product Pulse, announcing that they will work on Mozilla’s growing portfolio of products.
The Pulse workplace collaboration product helped teams collaborate better by automatically managing their Slack presence, creating “focus times” that allow users to work without interruption, and display color-coded do no disturb notices when team members were in meetings.
Pulse was a product created specifically for today’s hybrid workplaces.
According to the archived Pulse page:
“Adjusts with your work hours
Pulse puts an end to the ‘Always On’ culture by helping manage your team’s expectations around your availability — so teammates know when is best to connect with you and respect your boundaries.
Pulse uses AI to automatically display when you enter a focused state so your teammates know not to disturb.
You can also set calendar rules which change your status to show you’re focusing during blocked focus time or events marked as focus.”
The announcement did not hint at the future direction the new team will take within Mozilla.
However the fact that Pulse was a workplace collaboration product is notable.
It makes for interesting speculation that the acquisition may help Mozilla to begin introducing business-oriented products.
The quality that sets Mozilla apart from other companies is their commitment to creating products that don’t spy on or turn their users into a product to resell to marketers.
Free is increasingly common. Any product that can deliver quality at a free or near-free price and also respect user privacy would be increasing their value over more established products from companies like Google or Microsoft.
Google rapidly grew their email product by offering staggering amounts of storage space for free. Mozilla is doing with privacy what Google did with free, using it as a value-add that other companies do not offer.
And that edge is what makes the Pulse acquisition interesting because their machine learning expertise can be used to build privacy-forward consumer (and maybe business) products.
Ethical Machine Learning
The Pulse service used machine learning to help learn a user’s work patterns but in a way that respected their privacy, what Mozilla referred to as “applied ethical machine learning.”
According to Mozilla:
“Machine learning (ML) has become a powerful driver of product experience. At its best, it helps all of us to have better, richer experiences across the web.
Building ML models to drive these experiences requires data on people’s preferences, behaviors, and actions online, and that’s why Mozilla has taken a very cautious approach in applying ML in our own product experiences.
It is possible to build machine learning models that act in service of the people on the internet, transparently, respectful of privacy, and built from the start with a focus on equity and inclusion.”
First Project Announced
The first project the team will work on is improving Mozilla’s social sharing app called, Pocket.
Pocket is an app for saving content as well as sharing it with others. The app is available on a mobile device or desktop.
The author of the Mozilla announcement is Chief Product Officer, Steve Teixeira. He was hired by Mozilla in August 2022. Steve formerly worked at Twitter as Vice President of Product for their Machine Learning and Data platforms, and before that led the infrastructure Product Management, Design and Research team at Facebook.
Mozilla Chief Product Officer, Steve Teixeira, wrote:
“I’m particularly excited to enhance our machine learning capabilities, including personalization, in Pocket, a fantastic product that has only just scratched the surface of its ultimate potential.”
Mozilla offered no hint of future products beyond working on Pocket. They only published that they are looking forward to adding the Pulse team’s expertise to their growing suite of products.
“We are energized by the chance to work together, and I can’t wait to see what we build.”
It will be very interesting to see what Mozilla comes up with with the team acquired with Pulse.
Read the official announcement:
Featured image by Shutterstock/Kateryna Onyshchuk
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