Data-driven optimization: The critical role of AI in martech

30-second summary:

• The application of algorithms, machine learning, and AI to solve major marketing challenges – for example, attribution, intelligence gathering, predictive workflows, and campaign suggestions – will enable the industry to market better, for less money, more success, and happier customers.
• AI’s ability to understand data at global, regional, and local levels, as well as the most functional kinds of campaigns for different types of businesses, is fundamentally important to delivering optimized results and creating less waste across channels.
• Data collection, aggregation, and warehousing is not a problem that marketers need to solve – leave this to the software companies. The bigger issue is analyzing and identifying key trends from these channels and their data.
• This comes in a two-step process – first, determining which solutions offer a quick, affordable way to bring the necessary data together, and second, forming the market vision to know where trends are emerging and the savvy to know how to communicate them to stakeholders.
• Once you realize that you can bring the same level of insight, analytics and intelligence to the table using AI’s widely accessible to the everyday marketer, it levels the high-value marketing playing field in ways that promote the agility, effectiveness and marketing savvy of independent consultants and agencies as well as small and midsize brands, franchises and media companies alongside their enterprise counterparts.

In an increasingly data-driven world, there is tremendous value in capturing and making sense of marketing data, including information on users, accounts, contacts, purchases, downloads, link clicks, form submissions, video plays, transactions, and so on. While this top-level, event data may seem like everything a savvy marketer would need, it is the metadata – data about the event data – that gives it the most valuable context. Metadata can be more revealing than event data itself when collected and analyzed in aggregate. But there is so much data available these days that it can cause paralysis. This is where AI comes in – marketers need to become increasingly adept at using advanced technologies to make data functional.

The rise of AI in marketing

We have been seeing a steady rise in the use of artificial intelligence across industries – marketing is no different. Some of the world’s largest companies rely on AI for all sorts of reasons, but in martech, it holds promises that will bring even more disruption to the industry.

The application of algorithms, machine learning, and AI to solve major marketing challenges – for example, attribution, intelligence gathering, predictive workflows, and campaign suggestions – will enable the industry to market better, for less money, more success, and happier customers.

Two years ago, when the volume of data generated worldwide was estimated to be a staggering 2.5 quintillion bytes of data a day, the industry projected that by this year, 2020, every individual on earth would be generating 1.7 MB of data every second of every day.

While we don’t know where that number actually stands today, it’s likely been driven even higher as a result of the global pandemic. What we do know is that legacy analytics tools are not capable enough to ingest the amount of data being created in today’s martech stacks to make sense of it.

There are more than 8,000 different companies developing software in the space and all the data to go along with them. While the growth of the ecosystem has been empowering, it is also a curse.

Which is why a premium should be put on data integration and management solutions. For much of the industry, one of the fundamental issues is bringing data together efficiently and effectively.

In a multi or omnichannel marketing environment, how you develop actionable insights from a range of different marketing campaigns is one of the things that separates a good marketer from a great marketer.

A great marketer knows how to optimize campaigns, how to leverage historical data, and how to use marketing intelligence to map where to spend their next dollar for optimal impact.

AI in cross-channel optimization

Data density is an important component of artificial intelligence.

While Big Tech companies have enough data density to build predictive algorithms, smaller companies similarly need to be more resourceful to follow suit. Collecting enough data will lay the groundwork to start building their own marketing optimization algorithms.

However, the real challenge is not in-channel optimization. Rather, it’s everything else, like cross-channel optimization, which is a much more interesting problem to solve. And metadata plays a big role.

The ability to understand data at global, regional, and local levels, as well as the most functional kinds of campaigns for different types of businesses, is fundamentally important to delivering optimized results and creating less waste across channels.

Leveraging AI tools to automate data

Combining data from all marketing channels – social, email, mobile, location-based, app-based, targeted or retargeted, PPC or SEM – and tapping into data management capabilities that can help organize, analyze and create intelligence from these channels is a critical step in developing a functional, unified marketing stack.

Data collection, aggregation, and warehousing is not a problem that marketers need to solve – leave this to the software companies. The bigger issue is analyzing and identifying key trends from these channels and their data.

This comes in a two-step process – first, determining which solutions offer a quick, affordable way to bring the necessary data together, and second, forming the market vision to know where trends are emerging and the savvy to know how to communicate them to stakeholders.

There are ways to automate integrations as well as data warehousing, creation, and hierarchical management tasks in minutes.

Regardless, there are companies still trying to solve this problem on their own, in a slow, clunky, expensive, old-fashioned, and error-prone way – doing their own integrations and sometimes hiring another company to create their data warehouses.

That approach won’t sustain a competitive advantage. You don’t have to be a mega-corporation to understand this or to benefit from the insights that can be driven from campaign metadata.

Once you realize that you can bring the same level of insight, analytics and intelligence to the table using technologies widely accessible to the everyday marketer, it levels the high-value marketing playing field in ways that promote the agility, effectiveness and marketing savvy of independent consultants and agencies as well as small and midsize brands, franchises and media companies alongside their enterprise counterparts.

Daryl McNutt is Senior Vice President, Marketing for TapClicks, with responsibility for development and execution of growth initiatives for the company’s marketing operations platform. A seasoned, dynamic and well-accomplished senior executive with over 20 years’ experience in digital technology and advertising, Daryl brings to TapClicks a combination of talents across marketing, analytics, research and business intelligence and a history of leadership at innovative startups, large agencies and top consumer brands.

source https://www.clickz.com/data-driven-optimization-the-critical-role-of-ai-in-martech/261818/