Integrating AI and Big Data for data-driven Marketing

Have you ever wondered why so many businesses have strict data monitoring policies? Why does every company try to reassure consumers that their data is safe? And what do businesses even do with so much consumer data anyway? So many questions, but there’s only one answer: data-driven marketing. 

Businesses understand that modern-day marketing is different from what it was a hundred years ago. It’s even different from what it was at the beginning of the 21st century. 

That’s because consumers and their needs have evolved rapidly over time. Since marketing is primarily concerned with consumer needs, so it makes sense for it to grow too.

However, technology brought the real shift in needs and subsequent marketing techniques. With the digital revolution, businesses got to know consumers better than ever before. 

This development brings us to the present day when the internet is exploding with an abundance of consumer data. From social media posts to historical purchase behavior, data is oozing out at an unprecedented rate.

This trend makes data supremely valuable for businesses operating online (which is all businesses). Hence, marketing relies on consumer data now – therefore, it’s data-driven. 

But what did marketing rely on before? And is the new form of marketing better?

To answer that, let’s look at some basics.

Difference between Traditional & Data-Driven Marketing

Today, traditional marketing means offline marketing. This marketing includes print, broadcast, mail, phone, radio, and good old-fashioned outdoor advertising. 

But the real difference between traditional and data-driven marketing is in the approach. Traditional marketing relies on deeply researched methods that often involve trial and error. 

Understanding consumer needs, assuming habits and preferences, and then devising the target audience for marketing is all a traditional approach. But this approach is obsolete in the modern world because marketers don’t have to assume anything; they know. 

Consumer data is readily available every time consumers come online. By acquiring legal and ethical consumer data, marketers can devise better marketing strategies. Hence, a more modern approach for a contemporary world. 

The other defining difference between the two types of marketing is how they reach consumers. Traditional marketing allows for mass coverage by extending the same message and manner of delivery to millions. 

Data-driven marketing, on the other hand, allows businesses to reach individual consumers with a personalized message. This message is then delivered in a customized way, thereby revolutionizing business-customer interactions.

But with billions of users involved in billions of activities online, how do businesses keep track and analyze it?

That’s where big data comes in.

The Advent of Big Data

Big data is the storage and processing of vast amounts of consumer data. This field took off in the early 2000s and since then, has become indispensable for marketers worldwide. Mainly, it incorporates the three Vs.; volume, velocity, and variety.

Each V refers to the nature of consumer data. Volume is the amount of data collected from different sources, such as business transactions. Velocity is the speed with which data is being collected, and variety is the multiple forms of data from numeric to unstructured.
In any case, the advent of big data has revolutionized inbound and outbound marketing strategies. As marketers know more about consumers and can process the data quickly, so marketing strategies change frequently.

Big Data – Big Change

So what exactly has been the change in marketing thanks to big data? For the answer to that, we turn to the new data-driven marketing strategies employed by marketers. 

These strategies are focused on the experience of consumers as a whole. The benefit of knowing the habits and preferences of consumers is that marketers can perfect the way they offer products and services. That has revolutionized the way marketers go about driving traffic to their websites.

Target Audience Clarity

The main benefit of data-driven marketing is the clarity of who to go after. As mentioned before, the whole ambiguity in traditional marketing was whether or not the target audience was right. But with loads of consumer data at their disposal, marketers know who to target.

So data-driven marketing allows marketers to reach the right people at the right time. For example, data from a consumer’s purchase history shows they are an avid traveler. The data also indicates that the consumer travels every year between September and February. 
This data provides a brilliant opportunity for businesses offering travel bags to pitch ads to this consumer. Thus, data-driven marketing has changed the game for marketers in modern times by pinpointing who to target.

Meaningful Business-Consumer Relationship

The benefit of knowing consumer habits and preferences is that businesses can create a long-lasting relationship with them. In the past, traditional marketing did not cater to specific individuals. That’s not the case anymore because the data for each consumer is available for businesses.

This availability of data allows businesses to give a personal touch to their offerings for each consumer. The crudest example of this is the customizable handbags and wallets that local online businesses handcraft. On a more elaborate scale, B2B firms utilize consumer data to configure service packages to their client’s needs. 

So business-consumer relationships today outlast those in the previous century. With data-driven marketing, it’s easier for businesses to stay connected. They keep giving consumers a reason to stick around, whether through personalized promotional offers or customized offerings.

Customizing Consumer Experience

Marketers use big data to boost integrated marketing campaigns (IMCs) that enhances the consumer experience. By using data interchangeably between different sources, marketers can identify future trends. This predictive modeling is the ultimate tool for consumer experience. Let us explain with an example.

Imagine you accidentally break your phone. Now, you need a new one. So what do you do? Maybe you ask a friend, perhaps you visit a physical store, but you Google it. And that’s where big data kicks in.

By monitoring your search trends, data analysts can identify whether you have an intent to purchase. In this scenario, you need a phone urgently. So that translates into frantic searches to online stores. 
Based on the last phone you bought, its characteristics, meaning price, brand, storage, etc., Google can offer up models, it thinks you would find appealing. That is all part of improving the customer experience through the analysis of consumer data.

So What’s AI got to do with it?

By this point, you’re probably asking, “what’s AI got to do with any of this?”. 

The short answer is: big data is hard to analyze, so it’s easier, faster, and more efficient to use AI. It’s still billions of bytes of consumer data, and machine learning employed by artificial intelligence is the best way to analyze it.

In today’s fast-paced world, speed is the difference between success and failure. So online businesses need to act quickly on the insights revealed by big data analysis. They can’t do that if it takes too long to analyze the data. 

That’s where AI and machine learning come in. Artificial intelligence makes big data analysis less labor-intensive and more valuable. Businesses can gather better and faster insights through the predictive analytics of machine learning. 
As a result, firms are continually coming up with newer ways to analyze content thanks to artificial intelligence. AI is so useful in the analysis of consumer data that it can pinpoint the customer life cycle stages. This information enables businesses to modify their promotions and messaging to suit the life cycle stage.

Keep the Endgame in Mind

Finally, here is a word of advice for all those who use AI as the gateway to big data analysis. Even though data-driven marketing has revolutionized the way businesses and consumers interact, some things are still the same. Chief among them is the process by which companies make use of consumer data. 

It doesn’t matter how much, or even what kind of consumer data is at your disposal—as a business utilizing big data analytics, keeping the endgame in mind is most important. The ‘endgame’ refers to what you want to achieve through data-driven marketing.

This trend has stayed unchanged throughout time, despite the shift in data-driven marketing trends. Even for traditional marketing approaches, businesses established specific goals first. This step allowed them to know what they were looking for before they came across consumer data. 

That’s where some modern businesses falter. With so much consumer data at their disposal, companies fail to prioritize. This mistake is a detrimental one because it corrupts the process of data-driven marketing as a whole. 

To establish goals before you dive into the realm of data-driven marketing is essential. After all, data-driven marketing trends are determined by how businesses approach the analysis of consumer data. 

The best strategy is to plan when it comes to big data analytics. It’s important to remember that no matter how sophisticated AI is right now, it won’t set your targets. That’s why keeping appropriate measuring objectives is vital for long term success.

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