Artificially intelligent systems constantly work on the background of popular products and services such as Netflix, Amazon, and, naturally, Google. In the past few years, though, AI has paved its way deeper into marketing, helping brands to enhance every step of the customer journey. Moreover, tools previously available to enterprise level companies have become affordable and accessible to medium- and small-sized businesses.
Artificial intelligence marketing (AI Marketing) is a method of leveraging customer data and AI concepts like machine learning to anticipate your customer’s next move and improve the customer journey.
The evolution of big data and advanced analytic solutions have made it possible for marketers to build a clearer picture of their target audiences than ever before; and in this hotbed of advancement lies artificial intelligence (AI) marketing.
Armed with big data insights, digital marketers can greatly boost their campaigns’ performance and ROI, all of which can be achieved with essentially no extra effort on the marketer’s part. While this definition provides an overview of artificial intelligence marketing at the most basic level, there is obviously much more that goes into it.
AI takes conversion management solutions to the next level. Marketers can now compare sophisticated inbound communication side-by-side against traditional metrics to help answer difficult strategy questions. With AI marketing, there are no longer questions about whether or not a prospect is ready for a discussion, the data provides the answer.
With AI solutions, marketers know exactly what consumers are thinking, saying, and feeling about the brand in real time. Similarly, with the onslaught of social media available (and the AI to analyze it), marketers can truly understand what customers are feeling. Savvy marketers can harness this data in real-time and then quickly modify messaging or branding for maximum effectiveness.
While there are various ways to optimize digital advertising and account-based marketing, AI solutions help marketers take them a step further for deeper insight and analysis. AI can tap into the abundance of consumer data hidden in keyword searches, social profiles, and other online data, for smarter and more effective digital ads. The results are human-level outcomes and insights without the manual human labor.
This abundance of data can also help feed consumer profiles. AI solutions provide marketers with deeper knowledge of consumers and prospective clients, enabling them to deliver the right message, to the right person, at the right time. The secret to building a truly comprehensive profile lies in capturing data during every single consumer interaction. Marketers can use AI solutions to take these profiles a step further, refine marketing campaigns, and create highly personalized content.
One of the advanced capabilities of AI is its ability to analyze large blocks of open content and identify trends. This allows brands to interact directly with consumers in real time through online conversations or events. Communicating with consumers at the precise ‘decision-making moment’ can help directly influence buying decisions. AI helps marketers monitor these social conversations and other open forums to identify any relevant conversations.
AI has had a profound impact on the way that we search, and the quality of the search experience, that we often tend to take for granted in 2019.
Google first started innovating with AI in search in 2015 with the introduction of RankBrain, its machine learning-based algorithm. Since then, many ecommerce websites (including Amazon) have followed in Google’s footsteps and incorporated AI into their search engines to make product searching smarter.
With innovations like natural language processing and semantic search, search engines can determine the links between products and suggest similar items, find relevant search results, and auto-correct mistakes, helping consumers to discover products even if they don’t know exactly what they’re looking for.
Some online retailers and aggregators have discovered the extent to which machine learning can make the process of tagging and categorising products more efficient. Stuart McClure, founder of LoveTheSales.com, spoke to Econsultancy editor Ben Davis to explain how and why the company uses AI for product categorisation:
“One retailer might give us amazing data, and another could give us the same set of products but with awful data. We use a text-based classification tool, training various models with both positive and negative examples.”
This means that even if different language is used by different retailers to describe the same product – for example, “trainer”, “basketball shoe” and “sneaker” – the algorithm is able to understand that the products are the same and tag them accordingly. This can be so effective as to allow the algorithm to correctly identify a product based on nothing but context:
“The really cool thing is, we’ll have examples, loads of them, where you’ll get say 100 shirts and there’ll be a piece of data that has nothing in it at all to say it’s a shirt, but the model has classified it correctly as a shirt because of the surrounding context,” McClure told Econsultancy.
There are a few key elements that make AI marketing as powerful as it is today, including bid data, machine learning and the right solutions.
Big data is a pretty straightforward concept. It refers to a marketer’s ability to aggregate and segment large sets of data with minimal manual work. Marketing teams can then use this data to ensure the right message is being delivered to the right person at the right time, via the channel of choice.
Machine learning platforms come in handy when marketers try to make sense of this huge data repository. They can help identify trends or common occurrences and effectively predict common insights, responses, and reactions so marketers can understand the root cause and likelihood of certain actions repeating.
Artificial intelligence marketing solutions truly understand the world in the same way a human would. This means that the platforms can identify insightful concepts and themes across huge data sets, incredibly fast. AI solutions also interpret emotion and communication like a human, which makes these platforms able to understand open form content like social media, natural language, and email responses.
Predictive analytics, the practice of extracting information from data sets to predict future trends, can be used to great effect in improving customer service and customer experience.
Predictive analytics are a revolutionary capability of AI because it was previously only possible to retroactively determine trends from data sets. Thanks to artificial intelligence, things that could once only be determined retroactively can now be reliably modelled, and decisions made based on those models.
Predictive analytics can be used in ecommerce to analyse customers’ purchase behaviour and determine when they might be likely to make a repeat purchase or to purchase something new. Using predictive analytics, marketers can “reverse-engineer” customers’ experiences and actions to determine which marketing strategies resulted in a positive outcome.
Companies like FedEx and Sprint are also using predictive analytics to pinpoint customers who are “flight risk” factors and may defect to a competitor.
In customer service, predictive analytics can be used to anticipate high or low call volumes and ensure that phone lines (and other outlets) are staffed sufficiently.
Over the past several years, voice-activated devices and their potential have become the talk (ha) of the marketing industry.
This is possible due to advances in speech recognition technology, as well as things like natural language processing. In 2017, Google’s level of speech recognition accuracy reached the coveted 95% threshold, while in the same year, Baidu claimed to have reached a 97% accuracy rate with speech recognition – and is aiming for 99%.
While speech recognition is only one component of a good voice experience, it does play an important rule in making sure that voice interfaces and voice interactions function smoothly, and that users’ requests are interpreted correctly.
AI enables marketers to fulfill a dream previously considered impossible – to engage with every individual customer in a personalized and meaningful way.
When looking to integrate AI to your content marketing stack, consider these three points:
Below is a structured approach as to how to apply AI
Thus, it is critical to tackling a small problem which has a high return on investment, especially if you are a small business. Adhering to this approach proves fruitful when you are struggling with the amount of money you are willing to invest in AI.
Here’s a compiled list of some of the most popular AI platforms that your business could leverage:
Released in 2015, TensorFlow is one of the most well-maintained and extensively used frameworks for machine learning.
Developed to fuel its research and production objectives, TF is now widely used by numerous companies, including Dropbox, eBay, Uber, Twitter, and Intel.
Keras is notorious for its user-friendliness, modularity, and ease of extensibility. The standout feature that makes Keras unique is that it could be applied as a bolt-on and stand-alone software as well. Keras was designed to simplify the creation of deep learning models.
Being written in Python, it can be deployed on top of other AI technologies such as Tensorflow, CNTK, and Theano.
The platform provides a comprehensive machine learning library that allows for,
Developed by Apache, Spark MLlib is a machine-learning library that supports Java, Python, Scala and even R. Spark MLlib is specially designed for processing large amounts of data and could quickly be deployed across various SMEs like manufacturing, finance, healthcare and many more. The tool also provides interoperability with NumPy in Python and R libraries.
Released in 2017, Convolutional Architecture for Fast Feature Embedding often abbreviated as Caffe, is a machine learning framework that primarily focuses on expressiveness, speed, and modularity. This open source platform is written in C++, and also comes embedded with a Python interface.
Created by Facebook’s AI Research (FAIR) lab provide word embeddings and text classifications. It is available across all major platforms like Linux, MacOS and even Microsoft Windows.
Written in C++ and Python, fastText allows users to create unsupervised or supervised learning algorithms for obtaining vector representations for words.
You’ve been affected by AI in your content marketing for years, now it’s time to intentionally apply additional AI products to deliver personalized experiences and beat your competition.