How to Adapt Your Marketing Strategy for a Longer Path to Purchase

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5 Remarkable Ways Marketing Automation Is Being 'Geekified'

Marketing Automated Geekified

It's a good time to be a geek. Superhero movies dominate the box office, practically everything cool in society is technology-based, and even sports rely on insights provided by statistics for game decisions.

Anyone familiar with advertising has seen similar trends sweep across the industry. While your mental image of an agency might still include smoke-filled conference rooms and bottles of bourbon, modern marketing is much more geeky and impactful thanks to technology such as AI and machine learning.

To that point, there are a handful of examples that best demonstrate the power machine learning infuses into marketing automation to create cost-efficient, personalized, highly engaging, and, most importantly, converting strategies. AI-fueled geekification has hit advertising with the blunt force of a locomotive and the entire industry can be better because of it.


1. Always Sound like a Local

Historically speaking, a highly segmented marketplace with different demographics, affinities, and demands would exponentially increase the amount of money, time, and effort needed to successfully communicate and engage with those segments. However, with a variety of different machine learning platforms specifically designed to help advertisers transform static content into virtually any language or dialect, content no longer has to be completely reinvented to speak with a new or expanded customer base.

This technology, commonly known as Natural Language Processing (NLP), offers advertisers a scalable solution that can lend far higher levels of cost efficiency and engagement without investing the enormous resources that previously would've been necessary. NLP leverages machine learning to allow copy to speak in a natural manner according to language specific to a particular region, country, or even neighborhood in some circumstances.


2. Know Your Audience

Working as a companion technology to NLP, machine learning can also provide the data needed to create personalized content across multiple market segments through automated market research. Given the vast amounts of consumer-based data just floating around in the vast digital environment, the ability to harness that data to best understand consumer expectations and demands would be far too demanding of a task without machine learning.

For instance, with the importance of social influence in any modern marketing campaign, machine learning-based platforms can automate market research by scouring the digital environment for product and service reviews, questions, and comments in real time to provide marketers insight into what their target audience honestly thinks of their brand and product. Advertising campaigns can be altered on the fly according to the results provided by the technology.

With 70% of the coveted Millennial demographic influenced by peer reviews and recommendations in their buying decisions, brands would be well-served by leveraging the power of social influence rather than simply being reactionary towards it. Given the sheer scope of the random, unorganized data, such abilities are only made possible by automated machine learning.


3. Better Bidding

Another example of the automated benefits provided by machine learning is in the complex world of ad auctions. With an extraordinarily nuanced market requiring extreme levels of agility, maximizing efficiency within the bidding process can be enormously complicated due to the countless different signals needed to optimize bidding.

Several machine learning-based platforms now exist that can help buyers optimize their bids according to each auction, integrating a seemingly endless number of factors like affinities, location, and even device to maximize the effectiveness of their ad campaigns.


4. Organize Your Channels

An effective and thorough advertising campaign must typically rely on a number of different channels to maximize reach and impact according to a consumer's preferences. The problem, of course, is taking an organized approach to an omnichannel strategy that relies on email, social media, print, and many other channels to achieve that reach and impact. In other words, staying organized and fully informed can be quite a headache when using an omnichannel approach.

Machine learning offers another convenient solution for advertisers needing to use multiple channels with multiple variants of content within a single campaign. AI-driven platforms can now consolidate the performance data and metrics for those many channels into an individual platform, allowing advertisers to gauge campaign performance in real time across as many channels as they see fit. According to a recent study from Adobe, 44% of advertisers work with 3 or more analytics platforms and 38% with multiple media planning suites. Obviously, the need for consolidation and organization has never been greater.


5. Always Catch Your Audience's Gaze

A message and, ultimately, a brand rely on an advertisement's ability to catch the attention of a target audience in order to engage them and create some semblance of an emotional or psychological bond. Countless studies have shown that conversions can drastically increase when those bonds are formed. Therefore, the aesthetics of an ad are critical in catching the ever-fickle gaze of a consumer base.

Machine learning has once again given advertisers new abilities to better hone the visual aesthetics of ad content to maximize impact on the audience. By automating the process of sorting through millions of different images, videos, objects, and layouts, advertisers can use the most impactful assets as determined by any number of filters.

For instance, if a campaign is targeting college-aged consumers in the Pacific Northwest for a new VR platform, machine learning platforms are now capable of combing through the millions of combinations of images, videos, and layouts to find the ones that would have the most impact according to the affinities of that particular segment. When 74% of all online customers feel frustrated when digital content is not personalized to their own interests and tastes, catering to those affinities becomes critical in expanding conversion rates and getting greater Return on Investment (ROI).

As useful as machine learning has already proven to be for advertisers - including in our own platform - the best is yet to come as technology will continue to provide automated efficiencies for campaigns to navigate through a complex, crowded, and segmented marketplace.

Also read:

The Truth about Your 5 Greatest Fears of Marketing Automation

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Russell Chua
Russell Chua
Content Marketer at Creadits

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