Albert™ Webinar Recap: Individualization and the Escape from Traditional Personalization
More consumer data is available than ever before — so why are marketers still struggling to achieve truly personalized campaigns? We answered this burning question (and others) in our most recent webinar, and we’ve summed up the key insights for you here.
This past Thursday, Albert™ hosted a webinar entitled “Individualization: Escaping Traditional Personalization“. For those who weren’t able to join in, we’ve covered the most important takeaways from the lively discussion. Read on to learn more about how to simplify your campaign process and start engaging with your consumers on a truly 1:1 level.
Feeding the Masses
As marketing and technology continue to evolve, we’ve increasingly come to think of personalization as being dependent on quality data that allows us to hyper-target increasingly narrow audiences. But the amount of data available on consumers is only growing larger, meaning that marketers need to find faster, better ways to transform that data into actionable insights for our consumers.
So when it comes to individualization, we need to embrace non-conformity as the new norm. The “cookie-cutter” consumer of the past pales in comparison to the complex, “cutting-edge” consumer of the present, who demands experiences tailored to his interests, desires, and lifestyle habits. Marketers must identify what motivates each consumer to buy, then use that knowledge to offer experiences that deepen your brand’s personal connection to that consumer.
Obstacles in the Way of True Personalization
As we increasingly understand just how much of our data is available for public use, we also develop higher standards for our consumer experiences. The time when marketers could use “not having enough data” as an excuse for impersonal ad campaigns has long since passed. So what’s standing in our way? The most common obstacles to personalization are misconceptions, over-generalizations, and misrepresentations about the concept of personalized marketing itself.
In our discussions with marketers, all too often we find that they conflate highly-targeted campaigns with truly contextual marketing. They tend to focus their efforts on highly segmented audiences at the “awareness” stage of the process, believing this stage is the most important for individualization. But such a narrow focus on gaining consumers’ attention is preventing these marketers from paying attention to key business building blocks like purchase choice and loyalty that don’t just gain consumers’ attention, but their trust as well.
Confusion also exists around the concept of segmentation. When you think about the term, one of three words probably comes to mind: gender, age, or location. But this very traditional concept applies an overly simplistic framework to an extraordinarily complex group of consumers. Companies that follow this segmentation model fail to communicate with most of their consumer base because they only engage with it on a surface level. For your brand to truly resonate with your consumer, you must look beyond the trends and consider how your consumer thinks.
Finally, our research shows that another major problem with marketers’ approach to segmentation today is that there’s too much data. For the data to be effective, we need to structure it, commingle it, and above all, make sense of it. These things become significantly harder as the number of data touchpoints grows.
How do we strike a balance between man and machine? The first step is to understand man and machine’s disparate responsibilities: while man fulfills creative, intuitive storytelling roles, AI platforms fulfill data-centric, machine-oriented roles that are fine-tuned to address some of the problems we’ve highlighted. With both man and machine working together in perfect synergy, companies can capitalize on their goals — and discover new ones.
AI is inherently holistic, built to balance and adjust information across different channels at unparalleled speeds. Because AI is primarily concerned with sales and optimizations — not simply credit and value — it ensures that the right tools and systems are being used in the right moments. The data points emerging from these environments are far richer because we’ve centralized and optimized them before making our first strategic human decision with them.
What’s more, AI removes the need for information subdivisions, or separate teams and silos to deal with specific tasks. AI can deal with huge volumes of information from any number of marketplaces in a seamless way, so technology like Albert can make tactical use of this data to make decisions. As companies continue to see a massive influx of data, it’s safe to say everything’s getting a little messier — and marketers need a simpler process to clean up that mess. With the help of AI and its operational capabilities, marketers can take a few steps back and focus on creative strategy and content.
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