Digital MarketingDisplay AdvertisingCognitive marketing: The impact of AI on advertising

Cognitive marketing: The impact of AI on advertising

Brand strategies used across various channels, especially digital and linear TV, have long focused on grabbing attention with “over the top” tactics: sometimes goofy, sometimes entertaining, but they are all in an effort to make an impact.

Most would define this approach as traditional advertising. These campaigns are usually designed with eye-catching banners, fast-paced TV commercials or gimmicky slogans (and most likely include an attractive woman or man to bring the consumer in).

Interestingly with Gen Z, or as they like to be called, “Founders”, the younger audiences growing up in advertising-free environments, that traditional advertising approach to reach the consumer, influence consideration or building awareness is falling flat on its face.

Let’s take the ads from this year’s Super Bowl: most, if not all, advertisers pulled out all the stops showcasing a famous actor, went to great lengths to be entertaining or outlandish and some just put animals at the forefront of their message.

This left most of the audiences dazed and confused as to what a flying piano or face licking monkey-baby had to do with the USP (Unique Selling Proposition) of the products promoted.

Over the last 10 years in both digital and other media, we’ve begun to reverse engineer the brand story, starting with the point of sale.

Now, we capture loads of information about the last mile to purchase as well as the purchase itself. This has encouraged the industry to build ad tech around last touch attribution and create models that focus on targeting the purchasing customer.

We focus on those whom have already bought a product or has shown explicit intent to buy via keyword search, visiting a product page or engaging deeply in the sales funnel.

“Successful” digital marketing programs now contain a significant portion of budget on what we call retargeting.

New approaches to extend the value of these audiences revolve around more sophisticated approaches to move up the sales funnel in order to find audiences that show a relation/correlation to the small but valuable intender audiences.

These modelling techniques used to build broader segments of audiences are very successful and are at the heart of what most trading desks and programmatic buyers leverage to create value for their clients.

The modelled approach is highly effective, and now, most companies are looking to add more data sets into the mix to create bigger, and ultimately better, audience segments that are predictive of future customer behavior.

Anticipating what audiences are likely to do in the future gives marketers an advantage: buying media based on where the audiences will go, as opposed to where they’ve been. Other approaches in using data to increase campaign performance lay in the creative itself.

Companies are heavily investing in DCO (Dynamic Creative Optimization) to create multiple versions of the message, color or picture of the creative, and then determining which combinations are working, testing new variants and repeating until the best performing creative aligns with the target audience to hit maximum performance.

When you combine predictive audiences with dynamic creative, layering in the actual media placements and location targeting the ad permutations become mind numbing for humans to grasp and effectively execute on.

This is where I see MarTech hitting the scene and rendering AdTech news.  Generally, AdTech is taking basic tasks and automating them, and in many cases, increasing complexity through varied connections, processes and services.

MarTech and companies like IBM, Oracle and Salesforce are trying to take a more customer-centric approach to marketing and using data from all facets of the value chain to create pre-optimized audiences, end-to-end sales enablement, media projection and full-service optimization from within the enterprise.

What I find fascinating about this approach is that by truly considering the consumer, their emotional and physical state, which is now largely possible thanks to IoT, Beacons and mobility, we can identify how consumers emotionally connect to products and then adapt messaging to fulfill those specific needs.

Humans are relentless in wanting to be liked, loved, acknowledged, healthy and generally accepted, so when systems and services begin to identify with their emotions, media and advertising will occur.

Native is no longer just about tricking consumers into thinking an ad is content; Now, it’s enabling advertising to become part of the consumer’s social fabric while triggering a reaction to brands unlike anything we’ve ever seen.

Making this type of marketing connection to consumers is paramount. As I mentioned earlier, we are losing Gen Z audiences to walled gardens, and we need to effectively reset the ad clock and start anew.

Marketers today have 10 seconds or less to connect with a consumer, make them feel in control of the relationship, enable them to feel as if they “found” the connection themselves, and give them the opportunity to stand out and feel unique and accepted within a crowd of their peers.

The Super Bowl challenge for marketers is, “How do you build a unique community at the scale of 100 billion consumers across all digital connections?”


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