Earlier this year, we joined with our colleagues at Selligent partner agency DAD Direct and marketing competency development platform Wednesday Relations to present a webinar on How to Improve Your Marketing Automation Strategy, Using AI-Driven Personalization. In this webinar, we shared trends and challenges brands are currently facing with marketing automation, with tips and techniques for how to improve omnichannel engagement using AI-driven personalization in your marketing automation strategy.
We’ve had a great response from those who participated in the webinar, and many inquiries from others who missed the event. So we wanted to share a few highlights here.
It’s no secret that consumers today expect relevant communications and cohesive experiences from brands. Artificial Intelligence (AI) makes it easier for marketers and brands to build strong and long-lasting relationships with their consumers, by engaging them in a personalized way across their favorite channels, sending relevant content at the right moment, or helping marketers create segments which will generate the highest performance in their next campaign.
Last year, the pandemic brought so much change in consumer behavior and forced companies to move even further and faster into digital transformation, focusing more on things like mobile, e-commerce, and elevating voice and video service to consumers.
Every decision going forward must be based on customer insights, in order to deliver the kind of customer experience (CX) expected today. To drive engagement, satisfaction and customer value, brands must rely more and more on delivering personal offers, recommendations, and next-best offers. Offering personalized CX at that scale manually or through rule-based systems would be too complex and time-consuming. This can only be done through applying AI on consumer data. Marketing automation platforms such as Marigold Engage and its Marigold Recommendations AI can help you build a highly engaging CX through real-time personalization.
At Selligent, we provide a platform that allows you to build lasting relationships with each individual consumer, by delivering relevant communications. You can create segments and build sophisticated journeys very quickly. You can also design very complex and personalized messages, based on user interests and behavior.
When we talk about the traditional way of automating your marketing, standard platforms are built and operate on a “mass marketing” principle. In other words, you aggregate your data, start segmenting, create content, launch your campaign, run A/B tests, then send it as a mass blast to consumers – and they either engage or not. Everyone is doing at least this level of email marketing today to get their messages to consumers.
But the trend now is evolving to real-time interactions and decisioning. Consumers have many complex behaviors across many channels. When you start adding channels, to deliver omnichannel messaging, it gets a lot more complex. And so the evolution must be from mass marketing of large, single blasts, to a constant stream of personalized messages across channels. Increasingly, companies are trying to use as much data as possible to make predictions and make sure each consumer gets a relevant experience. Looking to the future, the movement is now towards making these decisions in real time using the most recent data generated by each consumer – and this adds even more complexity.
Marketing automation has evolved from the old batch-and-blast type of sending from 10 years ago to “consumer-focused marketing,” which automates the use of omnichannel data through AI, and moving more towards real-time marketing to individual consumers.
To really address every individual user in real-time, there are technical prerequisites which involve ingesting, aggregating, and structuring consumer profiles in a way that makes this data available to your campaign execution engine independently from the channel in use for a specific user at a specific moment in time. This data also needs to be made available to AI-driven tools, which constantly train their models and make predictions based on this data. This is what we mean by “next-generation marketing automation”: when you start using AI to take your marketing to a whole new level.
When we say AI, for us at Selligent, it’s a shortcut to saying we use machine learning algorithms and data to make predictions. For example, take the selection of content for a message you are about to send to a segment: based on data collected and stored in the Selligent platform, a machine learning-based feature in Marigold Recommendations (our powerful AI engine) would take the profile of an individual, the customer’s context, maybe the channel, and score the content you have available against it. It does not require setting up any complex rules or logic: Marigold Recommendations uses the data to generate scores for pieces of content and uses these scores to base its decisions on.
The Marigold Recommendations AI engine includes the following features:
Because these AI features allow marketers to create more highly targeted campaigns, there is also a need for marketers to “protect” their customers from receiving too many messages overall. Cadence management helps avoid marketing fatigue brought on by consumers receiving too much communication from a brand.
As we continue to move away from batch-and-blast marketing automation to real-time predictions, we’re helping brands to shift from mass marketing, even beyond target marketing, to true consumer-first marketing.
For marketers looking to better understand AI technology and how to adopt it into their workflows, we have created a free eBook: The Artificial Intelligence (AI) Revolution in Digital Marketing. Available for download now, it delivers an overview of how AI and machine learning came to raise the bar in engagement marketing – to the point of becoming must-have tools for all marketers.
Marigold: where relationships take root.