4 ways AI has changed marketing

Modern computer hardware is faster and smarter than ever; even consumer-grade processors today have parts that can speed up AI workloads. This rise in computing power in the last two decades has finally enabled AI to process today's abundance of data in astonishing ways.

And there's plenty of data to go around. To convey its scale, former Google CEO Eric Schmidt said that from the dawn of civilization until 2003, humankind generated five exabytes of data. Now we produce five exabytes every two days.

Staggering, isn't it? And this is just the beginning. According to?Deloitte, data volume is expected to reach 175 zettabytes by 2025.

Driven by the fundamentally human pursuit to understand the things, events, and the world around us, that insatiable curiosity--accompanied by the will to innovate--will always be there. Thus, data volume will continue to grow along with the technology used to process them.

From the dawn of civilization until 2003, humankind generated five exabytes of data. Now we produce five exabytes every two days.

--Eric Schmidt, former Google CEO

With so much data to feed on, AI has transformed how organizations gather and analyze data, and we’re only starting to tap into its potential.?

Why use AI in marketing?

When used correctly, AI can significantly enhance the marketing professional’s workflows and the entire organization. The tech has had a transformative impact on virtually all industries. Marketing is just one of them. Here are four ways AI can lead to better results and value for customers.

1. Content generation

Example tools: Grammarly, Jasper.ai

Natural language and image processing are two of the most developed fields in AI. Their advent has spawned a range of tools that aren’t specific to marketing but certainly improve it. For example, some AI writing tools can generate entire sentences and paragraphs based on the sentence preceding it or even with just a headline. Often, these AI writing assistants can also detect tone and emotional responses to make context-specific suggestions. They can help craft more personalized, relatable content for clients and customers faster.

Content generation isn’t limited to text. In recent months, we’ve seen the emergence of image generation too. Text-to-image AI tools such as Google’s Imagen and OpenAI’s DALL·E have substantial implications on how marketers obtain original, context-specific images. There are still limitations in its current form, but those obstacles are being rapidly removed.

2. Research and analysis

Example tools: Pattern89, Albert

To ensure a brand thrives, marketing professionals must conduct thorough research, understand its competitors, and strategically execute campaigns based on a cohort of signals like urgency and demand. AI has expedited this previously tedious and time-consuming process.

AI isn’t only helpful for creating personalized, targeted information, it can also profile people of interest, or even run full campaigns using learned actions. Some AI tools can generate personalized pitches and a list of potential leads for whom it will likely be suited. Other AI tools can analyze content--texts, images, and how they’re structured--to provide recommendations for SEO, keyword research, and competitive research, saving hours of manual work.

3. Automation

Example tools: Mailchimp, most major CRMs

Autonomizing repetitive, mindless sequences is a goal in all industries, marketing included. According to Cflow, 31 percent of businesses have fully automated at least one essential business function. Moreover, the workflow automation and related technologies market is growing at 20 percent per year.?

Automating marketing operation processes isn’t new. Cflow reported that automation in sales boosts productivity by 14.5 percent and brings down marketing costs by 12.2 percent. But AI has injected such a degree of intelligence that solutions appear almost clairvoyant. Using AI-based marketing tools can improve the quality of leads, client interactions, and has been estimated to increase conversion by 77 percent. Given its clear benefits, it should be no surprise that organization budgets for AI technology have increased by 55 percent year-on-year.

4. Human interaction and personalization

Example tools: Brandwatch, Remesh, most CRMs,

This last point feeds into all the points mentioned above. Whether it’s a mega-conglomerate or an independent coffee chain, gaining insights into your consumer base is the foundation of their marketing strategy. AI is changing the way marketers interact with their customers.

For example, look no further than the most popular customer relationship management (CRM) software today: Salesforce, Microsoft Dynamics, Hubspot, Zoho and others have all integrated AI components into their offerings. Salesforce Einstein, Microsoft AI and more have given incredible predictive capabilities, helping marketers understand emerging market trends and build new sales strategies. The industry is taking note; Sugar CRM reported that 91 percent of organizations say they plan on increasing the use of AI in the next year.

That’s no surprise, considering that according to a report by SugarCRM, 58 percent of sales and marketing leaders think their current CRM is a waste of money. The report added that 56 percent of marketers say they are missing data for their marketing campaigns. They’re asking for unified CRMs that can converge sales, marketing and services teams to better understand their customers.

AI is also assisting in the initial interaction. AI marketers are always available to answer questions and recommend products to customers. They provide that sense of immediacy and are useful in warming up a lead before passing them onto a human.

What’s next?

The still nascent field of AI is growing at a blistering pace. New advancements, even upon well-established models, will vastly exceed the capabilities of the bleeding-edge tools today. To give just one example of how quickly new developments are being made, OpenAI’s GPT-4 language model will soon replace GPT-3, one of the largest, most complex neural networks created only two years ago.

And that’s without touching on the possibilities of unsupervised machine learning. One subset of unsupervised learning, called reinforcement learning, can train itself using just a set of rules. Since it only needs a small pool of data to produce results, its sole constraint will be processing power. While it's still a ways off from being used in mass-market solutions, Google’s AlphaGo, AlphaStar and AlphaZero have given us a glimpse at their incredible potential. There’s plenty to look forward to–and the world has its eyes peeled.