6 Reasons Why Marketing Leaders Turn to AI

AI and ML are technologies that have made marketing, both B2B and B2C, more efficient than ever before. In fact, they can provide a level of personalisation and speed which would be impossible using traditional methods or human beings alone. It's no surprise, then that the global AI market is predicted to reach $190.61B market value by 2025. 

marketers and AI

AI augmentation is predicted to have created $2T of business value by 2021. This value is equal to 6B hours of worker productivity globally. As a result, the adoption of this tech has accelerated exponentially.

Not bad for a bunch of robots tied to typewriters in a big warehouse. Wait, that's not how AI works?

These statistical showings are no different in the marketing industry. A Deloitte global survey of early AI adopters even showed that three of the top five AI objectives were marketing-oriented: enhancing existing products and services, creating new products and services, and enhancing relationships with customers.

So, simply put, AI marketing is all about leveraging intelligence technologies to collect data, and customer insights, anticipate customers' next moves, and make automated decisions that impact marketing efforts. It can especially be used in areas where speed is essential, boosting the return of investment in marketing. 

When all these elements are understood, marketers can understand their customers' behaviour deeply, and map out their actions and indications. This leads to being able to target the right strategy, to the right person, at the exact right time. Pretty nifty, right?

Although AI and Machine Learning might sound like an Electro-funk duo, in fact, they represent a new, competitive advantage for marketers. We've written about what AI can do for you before; DX Strategy, Email Subject Lines, Influencer Marketing, Data Management, and plenty of others. Go check them out!

Right, let's jump in. Why are marketing leaders turning to AI? 

Number One: Lower Costs, Boosted Productivity, Reduced Errors.

Now, that's a pretty attractive triple threat. In fact, AI is going to be pretty important in the next couple of years as:

  • Worldwide data will grow 61% to 175 zettabytes by 2025. This will make AI and ML vital for productivity and efficiency. 
  • AI embedded in analytics and other marketing software will free up more than one-third of data analysts in marketing organisations by 2022.  

So, it's no surprise, then, that 70% of companies that use AI reduced costs. This is because AI reduces costs and boosts your team's productivity by automating data-driven marketing tasks that personalise the customer journey and increase revenue. 

Human beings are naturally prone to making mistakes. AI provides a solution just by existing. The tech basically operates to avoid excessive human intervention, which virtually eliminates the likelihood of human error. Specifically, AI can help with human errors, especially in the most concerning aspect - data security. 

The common data security problems faced by businesses often highlight the lack of training of employees to safeguard customer data effectively. So, AI can help address this issue by learning, adapting, and reacting to the cybersecurity needed by a company. 

Using AI can also streamline other processes that could take a pretty long time, into seconds.

This means taking laborious work away from your more creative team members, letting them be more, well, creative.

Let’s take a specific example for, well, example. 87% of companies that have adopted AI were using it to improve email marketing.

It also means using the most effective results of past campaigns and using that formula again and again. I mean, AI tools can write email subject lines that are 98% better than humans, after all. 

JOANN even saw a 10% email open uplift, and a 57% email click uplift with Phrasee, an AI  language-optimisation tool.

Data published by the company has shown that multivariate testing email subject lines using a diverse set of subject line language can lead to an open rate uptick of 5% - 10%.

So using an AI tool can speed up this pretty complex and time-consuming data analysis. 

Two technologies that have driven AI progress, in order to get it to this point, are natural language processing (NLP), and natural language generation (NLG). 

NLP is when a machine 'reads' text. This then leads to the text being turned into a code the machine can process and understand. 

NLG is when the machine uses that code to generate its own words. Think NLP as the reader, and NLG as the writer. 

So, in this way, AI is better equipped to write email subject lines than us mere humans. Basically, it excels at analysing vast amounts of data, extracting insights from that data, and then writing mathematically-impactful subject lines from this data. Phrasee routinely uses 100,000+ emails as a dataset - that's a lot of data to learn from

Number Two: Data Management

Right, now you've collected all that data, you need to find a way to manage it.

451Research’s Voice of the Enterprise AI and ML 2H 2018 survey found that 68% of respondents already had or were actively planning machine learning projects, and 92% of those in the production or PoC stages have positive opinions about the performance of their ML projects to date.

Plus, an August 2019 survey by the American Marketing Association even found that implementation of AI had jumped 27% in the previous year and a half.

This is almost certainly due to the ability of AI to speed up cumbersome, time-consuming processes. 

 “AI algorithms can analyse data sets that would take humans years or longer. Take, for example, the human genome project,"  says Mike O'Malley, SVP of SenecaGlobal.

"Scientists started the project in 1990 and finished in 2003. It took thirteen years to create a remarkable advancement in the science of fighting genetic disease.  Data scientists using AI can now repeat that process in 24 hours.”

In marketing, AI uses technologies to make automated decisions based on data collection, data analysis, and all sorts of audience and trending observations . This is all based on the need for speed. Ka-chow. 

AI is used by marketers to augment marketing teams, or perform tactical tasks that don't require human nuance or refinement. This means effective messaging and tailored marketing, without much intervention, and with maximum efficiency. 

The growth of digital media brought with it lots, and lots, and lots of data. This provided marketers the opportunity to understand not only their customers, but their own efforts. It also means being able to attribute value across channels.

But, on the other hand, it has also meant the over-saturation of data, as marketers can struggle to determine which data is valuable, or which data sets are worth collecting. 

AI and ML are defined as "disruptive enablers", or an innovation that significantly alters the way that consumers, industries, or businesses operate. They turn tech that is basically just low-level automation and streamlining, into systems which can act intelligently on what they've previously learned. So, AI's need for vast amounts of data can't help but transform data management. 

Plus, adaptive automation and ML will allow data management software and platforms to act in smarter ways, by observing and leveraging patterns - something that would previously be time-consuming, and run the risk of human error. But remember, AI is only as good as the data it's provided with. 

Number Three: CX at Scale 

Specifically, it is human-centred AI provides the potential to develop next-level human and customer-centred marketing and business models. 

Human-centred AI is focused on creating an ethically designed algorithm, which has learnt from human input and collaboration. 

“Businesses are needing a deeper understanding of customer activity and the life events that are happening for them, such as purchasing a home or car or having a child, that is consistent with the privacy rights and expectations of their customers yet allows for better and more proactive customer service,” explains Luis Chiang, Salesforce Innovation Unit leader for IBM EMEA.

“It's about starting with a ‘design thinking’ user-centric process that gives IBM real empathy for what our client is trying to do for their businesses, how they're trying to do it, and then building the technology around their interactions.

This can help them serve their customers in more immediate ways with a lot deeper intelligence and personalisation.”

This approach to AI means delivering solutions which meet evolving employee and customer needs and expectations.

“The appearance of artificial intelligence in our daily lives constitutes a unique opportunity to focus on the essentials of women and men, and of our society. What skills are we going to develop? What society do we want? These questions are underpinned by a reflection encompassing each citizen on the common values that we want to carry.“

– Nathanaël Ackerman, AI4Belgium Lead and AI expert at SPF BOSA

With  65% of customers saying a positive brand experience is more influential than great advertising, providing great CX is vital. 

However, it is easy for marketing to become intrusive, which can damage customer and user experiences. In fact, 32% of customers say they will walk away from a brand they love after just one negative experience. 

This is where human-centric AI can step in. It can allow marketers to deliver the best customer experience to each distinct client and then allow you to scale this experience. 

First up, it shows you whom to target. This means gaining insights into how to segment customers, and knowing where they are in their self-directed customer journey. Then, you can gain information on what to say in your messages, and whether you need to include an offer.

Then it's all about when to engage. When will someone be responsive to your messaging? What digital channels should you be connecting with them on? With the amount of offline and online channels, it's often difficult to decide. Human-centred AI helps with all of these insights and decisions. 

This AI also helps when developing personalised customer experiences. When customers are provided with a good technological experience, such as a helpful chatbot, a personalised email, or a website feature which works perfectly, they'll associate positive feelings with the brand which provided it. Or they'll not notice. But they definitely will notice when something doesn't work.

However, personalisation can only happen in this way when human needs, wants, emotions, and behaviours are taken into account during the development of technology. So, basing AI development on human psychology and behaviour leads to products which offer a satisfying, enriching, and fulfilling customer experience. 

Number Four: Granular Personalisation

A highly granular level of personalisation is expected by pretty much every customer nowadays. 

With the help of new technologies (like AI), marketers have been able to take a step past simple personalisation, and jump into the era of granular personalisation.

This begins with micro-segmentation which is all about narrowing down into smaller, niche pockets of micro audiences. Granular personalisation and personalised targeting have already been adopted by big companies like Netflix and Amazon, both known for their great customer experience. 

So, we understand that marketing messages should be informed by a user's intent, purchase history, location, past brand interactions, and a number of other data points.

AI helps marketing teams do this, by going beyond the standard demographic data to learn about consumer preferences on a granular, individual level. This helps companies create curated experiences based on a customer's unique tastes.

Another trend based on AI-enable personalisation is atomic content. This is where AI learns the customer's preferences and pulls pieces from a library of content to create a customised email or offer for a client featuring relevant images, videos, or articles. 

Number Five: Digital Asset Management (DAM)

DAM stands for Digital Asset Management and it pretty much does what it says on the tin: It’s a piece of software that helps you manage all your digital assets.

43% of organisations currently use or plan to build/buy AI and ML systems with a primary goal of increasing operational efficiency and effectiveness. 

Plus, according to an Aberdeen study, 55% of companies are using 11 channels to interact with customers. So, it's vital that these experiences are stitched together. When an omnichannel customer experience programme is used, customers report a 2.4X annual increase in satisfaction. Getting an intelligent DAM to drive this level of personalisation and customer focus makes this a hell of a lot easier. 

The most advanced DAM solutions include AI in their capabilities. 

That might be NLG, computer vision, OCR, sound indexing, etc. in order to identify text - either what is written, or the meaning itself. 

From this, AI can do all the stuff a human could do, without all the human stuff. You know, eating, sleeping, making mistakes, all that nonsense.

Your AI could create chat boxes on your site and converse like a human. It could generate subtitles in a number of different languages, and add keywords or categories of elements in an image or video to enrich metadata and asset clarification. According to Wedia Group:

"By means of a specific learning mechanism (Machine Learning), users themselves create their own sets of keywords or categories, teach AI and improve the indexing capacity of the DAM media library."

Across channels, consumers respond to different messaging. ML and AI can track which messaging they have responded to, and create a more complete user profile. From there, your marketing team can create more customised messages to users based on their preferences. For example, Netflix uses machine learning to understand the genres a certain user is interested in. It then customises the artwork that user sees to match up with these interests.

On the Netflix Tech Blog, they explain:

Let us consider trying to personalise the image we use to depict the movie Good Will Hunting. Here we might personalize this decision based on how much a member prefers different genres and themes.

Someone who has watched many romantic movies may be interested in Good Will Hunting if we show the artwork containing Matt Damon and Minnie Driver, whereas, a member who has watched many comedies might be drawn to the movie if we use the artwork containing Robin Williams, a well-known comedian.”

Number Six: Sales Forecasting

61% of marketers are planning to use artificial intelligence in sales forecasting. 

knowing what to do next, and then completing it in the right way is vital to meeting customer expectations and earning more sales. So, the application of AI in marketing makes it easier for marketers to understand customers and participate in their actions based on the data collected on their contacts and past purchases, as we've mentioned before. 

Through this system, it can be predicted what customers will buy next, and the quantity of a product sold. this can help you define which products to promote, and whom to promote to. 

This form of creating business intelligence also helps you to avoid overselling or selling out-of-stock products by balancing inventory. 

So, AI makes life easier, simple as. In fact, how do you know an AI didn't write this article? Help, I'm still tied to this typewriter!