Artificial
Intelligence in digital marketing is a concept many B2B marketers struggle to
get their heads around. However according to our latest research Squib.Media;The B2B Content
Intelligence Report, 67% of senior B2B marketers perceiving a high
threat level from new market entrants leveraging AI. Such findings flag the
dangers of lagging behind…
We’ve put together this informative guide to artificial
intelligence (AI) including a jargon busting glossary of key concepts you’re
likely to see joining the marketing vernacular.
Reading time: 5 minutes
What is artificial intelligence and how is it being used in
B2B marketing?
Put simply,
artificial intelligence (AI) is intelligence generated by machines which allows
us to efficiently and effectively delve into all available data. AI has the
ability to provide far greater amounts of more relevant data which, along with
the best B2B digital marketing activities to support this, can lead to
outstanding results.
A computer or
robot will think and work like a human, performing tasks which normally require
the natural intelligence element that they were once perceived not to hold. AI
has overcome this and can provide a real competitive advantage through
algorithms, machine learning, natural language processing (NLP) and it can even
provide insight into the best account-based marketing lookalikes thanks to its ability
to use data mining in ways you might not even have realized were possible.
The impact of AI in Digital Marketing
In the
ever-evolving B2B digital marketing world, marketers are able to use AI
applications for numerous B2B marketing activities such as optimizing demand
generation campaigns, purchasing ads and managing buyer journey interactions.
The opportunities are endless; machine intelligence extends our capabilities to
deliver seamless content experiences by providing the potential for personalized
content creation across multiple channels while simultaneously gathering a
greater understanding of target audience segments.
Marketers are
therefore freed up, giving them more time to focus their creative skills on
seeing B2B digital marketing reach new levels.
AI Threat Level
As
previously mentioned, two thirds of senior B2B marketers perceive a high threat
level from new market entrants who use emerging AI to identify and apply better
intelligence to gain competitive advantage. This is according to our research
published in our latest Squib.Media;The B2B Content Intelligence Report. Interestingly, only 4% felt
that this posed a low risk. These stats alone reveal a clear emphasis on the
importance of AI for digital marketers going forward, while highlighting the disadvantaged
position of those currently not utilizing it.
To help you
master the world of AI, here’s a list of the most common key terms, concepts
and acronyms that you’ll likely come across with increasing frequency, as more
businesses integrate AI elements into their B2B marketing strategies…
Algorithms – a list of rules or
instructions that must be followed in the right sequence in order to solve a
problem. They are given to intelligence machines to help them learn on their
own; clustering, classification, regression, and recommendation being the four
most popular types.
Artificial Intelligence
(AI) – the ability of a machine, computer, or robot to perform tasks
and make decisions that require and stimulate human intelligence, behaviour and
discernment.
Artificial Neural Network
(ANN) – brain-inspired learning systems created to replicate the way
humans learn, with the ability to solve tasks which are too complex for
traditional computer systems to understand.
Augmented Reality – an interactive and
enhanced experience of the real physical world through superimposing objects,
sounds or other sensory stimuli into it via technology.
Big Data – extremely large,
complex or fast sets of both structured and unstructured data, that are
consequently impossible to process in traditional ways. Computational analysis
and mining can reveal key trends, patterns, and information for machine
learning.
Chatbots – a computer program
purposely created to simulate a conversation with human users through
text-to-speech and on-line chat or voice commands, aiming to replicate an
actual human conversation.
Classification – enables machines to
identify and assign a category to a new data point based on the training
dataset which contains data points and their corresponding labels.
Cloud Computing – using a network of
internet hosted remote servers to manage, store and process data whenever,
wherever.
Cluster analysis – an unsupervised
machine learning task used to interpret and analyze data to find similarities
for natural groupings or clusters.
Clustering – machines will divide
data points into groups with similar characteristics.
Convolutional Neural Network
(CNN) –
a type of artificial neural network used
to identify and make sense of imagery.
Data Mining – analyzing and drilling
down into large data sets to discover patterns and gain new information and
data.
Data Warehouse – a type of data
management system used to perform analysis and queries on large volumes of
historical data.
Decision Tree – a tree-like structured
model, used to visually map out decisions and their potential outcomes.
Deep Learning – machines autonomously
replicate the human brain thought patterns through artificial
neural networks made up of vast layers of information.
Genetic Algorithm – an algorithm based
on natural selection and genetics, used to search for optimal and near-optimal
solutions to solve highly complex problems.
Logic Programming – a programming paradigm
written in a logic programming language to express knowledge based rules and
facts about specific problems or areas.
Machine Intelligence – an umbrella term
encompassing deep
learning, machine learning and classical algorithm output.
Machine Learning – a focus on computer algorithms which
allow machines to improve and learn automatically through experience and data
rather than needing to be programmed to do so.
Machine Perception – the ability for a
computer system to interpret and receive data in a way which is similar to how
humans use their senses to do the same, typically using attached hardware.
Natural Language
Processing – the capability of a computer program to recognize, process and
analyze human communication i.e. natural language data, how it is intended to
be understood
Python – an interpreted
language which is simplistic, concise and with a readable code, eliminating the
need for it to be composed into machine language before the developer can run
the program.
Recurrent Neural Network
(RNN) – a type of neural network which makes sense of and recognizes
patterns in sequential data, and then creates results and outputs accordingly.
Supervised Learning – a form of machine
learning which uses labelled datasets to train a machine
into generating desired algorithms based
on learning to match a specific input to a specific output.
Swarm Behavior – the collective behavioral output of individuals following
simple rules in a decentralized manner, often interacting with limited
intelligence.
Unsupervised Learning – a type of machine
learning algorithm which learns patterns from unlabeled
input data, in the hope that this will in effect teach it to produce
imaginative outputs.
As you can
tell, AI is ever evolving and so is its pivotal role in helping B2B marketers
achieve their marketing objectives. While it is seen as a threat to businesses
not currently executing it, AI has the proven ability to increase a company’s
available reach to gain a true competitive advantage.