Artificial Intelligence Has Become the ‘Big Brother’ to Data-Driven Marketing

Marketing is revolutionizing. Processes that work today may not perform tomorrow. Giants like Facebook (META) announce changes to their newsfeeds and Google (GOOGL) rolls out another update to its billion lines of code. How are we able to keep up? Over the years, we’ve followed the latest trends and you know that many of us have been talking about data-driven marketing. We try using it strategically and often boast about it during our chats with the C-Suite. In fact, it’s become somewhat of a hiring commodity lately. This approach has pushed its way into our jobs, allowing us to better understand people and their consumption behaviors.

Data-driven marketing has become an integral part of the marketing process. The main goal is to improve consumer relationships by understanding their needs and tastes. This allows us to create campaigns and program meaningful content for them. Further, we use this approach to make decisions about what marketing campaigns to run and which products to create demand for. It’s gained much popularity among startup marketers.

Clusters in Data-Driven Marketing

The most common types of information used are engagement, behavioral, and attitudinal data.

Behavioral Data

Behavioral data includes history on a sale or website as well as user activities. It is also information about product usage and attention-related data.

Engagement Data

Engagement includes web and mobile app interactions. It is information on page visits, app stickiness, and user flows. Engagement also details how consumers arrived on your site and how they engage with your content and paid ads.

Attitudinal Data

Attitudinal data is generally supported by the feelings and emotions of your customer. It shows whether your customers are happy with your product or service. Moreover, it tells us about their preferences, motivations, and challenges.

Using Data to Deliver Your Product or Service

Marketing teams have gathered data despite having proper systems in place to support such data clusters. With tons of information existing on consumers, it’s become harder to target our messages more effectively. Further, it’s been a cat-mouse adventure interviewing and onboarding talented digital marketing specialists. Yet to make matters worse, we risk draining our budgets with the costs of partnering with agencies to manage our data.

So how can we resolve this challenge?

Improvements to technology have taught us to optimize our marketing. As a result, we spend less money and deliver higher conversion rates. Some solutions make it so we can elicit an action based on real consumer behaviors rather than guessing at trends. Most recently, modern marketing leaders have addressed this by using Artificial Intelligence (AI).

Data-Driven Marketing and Artificial Intelligence

Over the next decade, we’ll likely see more companies using data analytics and AI to help marketers make smarter decisions. Modeling enough data, our enhanced strategies will lead to better returns on our marketing dollars.

A common question about using AI in marketing is whether we should collect a large amount of data before trying to apply it. Truthfully, I recommend starting with the information you have on hand.

As more companies begin to embrace AI, marketers will have to adapt their strategies accordingly. Most businesses already sit on a wealth of information and have been actively investing in this technology. With the help of AI, preferences about customers, potential clients, and competitors can all be managed. It also helps with predicting trends, personalizing ads, and delivering recommendations with accuracy.

Another common question I am seeing across this topic is wouldn’t too much data cause problems and slow down the process of developing a model?

My advice: Start Now and Build.

While I am not an AI expert, I would say that in theory yes, inputting more data can slow down the process. On the flip side, I would argue that more data also means a better-developed model. The answer to this debate depends on the specific problem being solved.

Marketers are still struggling with understanding the basics of artificial intelligence. AI is a term describing technologies that mimic human thinking, judgment, planning, and learning. In the context of marketing, AI refers to algorithms. It is also machine-learning models (ML), natural language processing (NLP), and deep learning (DL). With the recent demand for smart home devices, AI also exists in voice recognition systems that are able to learn from customer data. Think Amazon’s Alexa or Apple’s Siri for example. It is digital marketing processes that intelligently adapt as circumstances change.

What makes AI so powerful in the marketing realm is that it allows for greater flexibility, speed, and customization. Unlike traditional methods, the end result often leads to more targeted messaging with less wasted effort.

There are still some drawbacks to using artificial intelligence tools. For example, AI models may not have access to all the data you need to create a successful campaign. On top of that, a team of humans may still be needed to make sure your message gets through to your target audience.

One thing to keep in mind is that AI doesn’t replace an understanding of what your consumers are feeling. For now, that is…

Using Artificial Intelligence (AI) in my Data-Driven Marketing Plan?

The key to creating great campaigns using AI is to first get a solid foundation in understanding what data you should collect. This will vary on your business goals and objectives. Once you have a good understanding of the information you’ll likely need, begin organizing it into categories. The final (ongoing) step to leveraging AI in your marketing is to create models that will learn from your data over time.

The future of marketing isn’t just data-driven as we’ve come to understand over the years. It’s more like storytelling combined with intelligent modeling. It is important to note that as the idea of data-driven marketing evolves, we marketers have a big job ahead of us.

Let’s continue to test new technologies and automate tedious processes. Let’s shift our focus away from data-driven to AI-driven. In the end, we’ll grow our revenue much faster than ever before, while delivering quality experiences to real people.