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AI Primer: The 2019 Guide Marketers Need to Read

The access to lots and lots of data have changed Marketing.

Today, marketers do not only have to be empathetic wordsmiths but also analytical data scientists who track consumer purchasing habits and behavior across all platforms and channels and measure the efficacy of their marketing efforts.

With ever-changing consumer trends and expectations, keeping up is turning into a herculean task for any marketer.

Artificial Intelligence (AI) promises to help marketers make sense of all that data and finally realize the long-held goal of building individual relationships with each customer:

  • deliver highly personalized interactions based on customer behavior
  • understand context and deliver better engagement experiences
  • predict what a particular customer is likely to buy
  • eliminate ‘marketing waste’ and cut through the noise

It’s not surprising that the search for “Marketing AI” has seen a spike this year, in April 2018 to be exact.

marketing ai search volume

The hype surrounding AI for marketing applications will continue in 2019 and it is widely recognized that AI solutions will dominate marketing in the years to come. Chatbots and digital assistants, the most visible representation of AI, are getting most of the attention right now — both from consumers and marketers. And we are seeing them dominate discussions and proliferate the digital space.

As a marketer, you don’t need to get deep down to the technical nitty-gritty, but you should have a reasonable understanding to join the conversation. With this AI primer, we want to provide you with a quick crash course in All-Things-AI.

Let’s have a look at what you need to know.

AI Terms Every Marketer Should Know

It’s good to become familiar with the following AI-related key terms:

1) Artificial Intelligence

AI enables machines to perform tasks that would otherwise require human intelligence. This includes activities such as learning, reasoning, decision-making, and problem-solving.

It uses techniques like machine learning to spot patterns in heaps of data and recognizes what’s happening before any human ever could, helping them make better decisions.

 

2) Machine Learning

Machine learning is a subdiscipline of AI that’s often used synonymously with AI. It allows programs to absorb and learn from data over time.

For example, imagine a chatbot interacting with a customer. With every interaction, which is essentially training data, it can learn which type of engagement works and which doesn’t, detect patterns and adapt their behavior to improve future responses and interactions.

 

3) Neural Networks

Neural Networks are key to machine learning as they build its foundation. A neural network is the combination of algorithms (i.e. for different tasks) that feed into each other and work together to process data, find patterns and learn. It is modeled in a way to mimic how the human brain processes information.

Like with the human brain, the more experiences (data) they are exposed to, the better they become. The benefits are that neural networks learn “organically” and can self-repair or correct what they have learned.

 

4) Deep Learning

Deep learning is an advanced sub-category of machine learning. With it, you can process huge volumes of abstract, scattered data and can find super complex patterns and correlations.

For example, if you take a consumer’s interaction with a website. It typically consists of multiple events such as searching, clicking on product pages, adding products to a cart etc. With deep learning algorithms, you can capture all these events and assess the customer’s journey to understand their context and work out the intent behind each interaction. The more events and interactions you consider, the “deeper” the learning, the better the insights and the more accurately can you determine what steps you should take next to turn this prospect into, for example, a long-term customer.

subfields of ai
Machine learning and deep learning are subfields of AI (source)

 

5) Natural Language Processing (NLP)

NLP allows machines to understand the context and meaning behind what humans are saying, be it in text or by voice.

It’s the holy grail of AI and researchers have been working on the problems of text and speech recognition for decades as quirks in the human language such as sarcasm, cultural context, sentiment, and other subtleties are generally difficult to detect. But it’s in use today if you think about Amazon Echo, Siri or conversational chatbots.

 

6) Analytics

Analytics is the process of extracting insights from data. In combination with AI, you can think of it as “analytics on steroids”. It has traditionally not been predictive, but it is now that AI is here.

You can not only draw insights and uncover trends from historical data (such as a consumer’s spending habits or your sales over a set period) but can also build models that predict what will happen in the future. Predictive analytics can be very useful for marketers who want to build more effective marketing campaigns.

 

AI has the potential to transform the practice of marketing in significant ways.

Here are some tips to get started:

  1. Identify the business problem: determine the mission-critical issues you want to solve with AI-based solutions. Start with the customer, their pain points and the experience you want to deliver. Too often, people start with the wrong end of the problem and limit themselves to data, processes, technology, and information they already have. Use your imagination – Think about the what first, and the how second.
  2. Apply the right technology: Most companies can’t afford huge development teams to set up an AI infrastructure and build, train and deploy their AI applications. But due to the fast-growing AI-market, there are several technologies on the market that can help you fast-track your AI efforts. Do your research and explore.
  3. Think big, start small, scale fast: Remember that it’s only the beginning of the AI revolution. Don’t panic if something doesn’t work exactly how you want it to today. As time goes by, you and your systems will learn and get smarter.

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