Using generative AI to turbocharge
digital marketing
Received (in revised form): 26th September, 2023
Ian Thomas
Founder/Chief Data Officer, Yew Tree Data Consulting, UK
Ian Thomas cofounded one of the industry’s first Web analytics firms and has held senior data leadership
roles for Microsoft, Publicis Groupe and Omnicom. He has managed some of the world’s largest and
most complex datasets and built effective cross-functional teams to bring data to life. He now spends
time working with CDOs and other senior data leaders to help them transform the value they derive from
their investments in data, analytics and AI.
E-mail: ianth@outlook.com; Website: https://www.yewtreedata.com
Abstract AI has been used in marketing for some time to enable better targeting and
optimisation of messages, but some of the benefits of these approaches have been
limited by the ability to create personalised content at scale and the ability to measure the
effectiveness of this content in a structured way. Generative AI offers a way to address
these challenges and to facilitate integrations that make it easier to execute and measure
marketing in a fragmented ecosystem. However, the technology presents a series of
technical, privacy and copyright challenges that organisations will need to overcome in
order to use it effectively.
KEYWORDS: generative AI, OpenAI, digital marketing, GDPR, LLMs, privacy,
personalisation
GENERATIVE AI IN DIGITAL
MARKETING
The use of artificial intelligence (AI) is well
established in digital marketing. As the
availability of rich audience data grew in
the 2010s, advertisers and publishers used
this data to slice audiences into finer and
finer segments and delivered targeted
campaigns and creatives to these segments.
Machine learning (a key AI technique) was
used both to create the segments and
decide which messages to deliver to whom,
using a combination of classification,
prediction and recommendation algorithms
(Table 1).
As audience data has become harder to
acquire and work with, creative/message
optimisation has become more important.
Treating content as its own experimentation
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space and optimising variations in tone,
emphasis and message has the potential to
drive significant performance improvements
for marketing without the need to exploit
personal data. This is where generative AI
techniques have a valuable role to play.
How generative AI differs from other forms
of AI
Generative AI (GAI) builds on a set of
established techniques within the AI
discipline, most notably deep learning, but
has a unique emphasis: rather than aiming to
make automated decisions or predictions,
GAI focuses on producing original content.
GAI models are trained on very large
amounts of publicly available data. They
build a complex statistical model of the
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Using generative AI to turbocharge digital marketing
Table 1:
AI system types and their applications in digital marketing
AI system type
Example applications in digital marketing
Classifiers (‘what kind of thing is this?’)
• Audience segmentation
• Creative asset classification
• Creative whitespace identification
• Lead qualification
• Lookalikes
Predictors (‘what is going to happen?’)
• Target segment selection
• Campaign ROI prediction
• Media mix modelling
• Creative/keyword selection
• Bid selection
Recommenders (‘this is what you should do next’)
• Product recommendation
• Cross-sell/up-sell
Generators (‘Hello, Dave . . .’)
• Automated creative/copy generation
• Creative classification and feature extraction
• Chatbots
• Content localisation
• MarTech integration automation
characteristics of the input data, which
enables them to generate new content that
resembles that on which they were trained.
Although there has appeared to be a
sudden set of breakthroughs in GAI in the
last year, leading to its sudden explosion as a
topic of interest, the advances in this field
are based on a linked set of innovations
stretching back over the last decade. For
example, transformer architectures, a key
element of many generative models, were
first developed and explained in a paper
from Google in 2017,1 while generative
adversarial networks, which have been
essential to the development of image-based
GAI, were first introduced in 2014.2
For several years, as these techniques were
refined by organisations like Google and
OpenAI, progress was impressive but not
transformative. This changed in 2022 when
OpenAI released its GPT-3 large language
model (LLM), a transformer-based model
that was trained on 175 billion parameters,3
an almost hundredfold increase on the
previous version, which resulted in a
quantum leap in performance across a
number of scenarios such as translation,
summarisation and question answering.
Since then, GPT-4 has been released; the
number of parameters used to train it is
thought to be around a trillion,4 representing
another hundredfold increase from GPT-3.
Training these models with such a large
amount of data has been very expensive: it is
believed that GPT-4 cost US$100m to train.
This leads to a key difference between GAI
models and other kinds of machine learning
models, which is that most organisations
cannot afford to train their own GAI model
from scratch. Instead, the usage pattern is for
organisations to make use of so-called
‘foundational models’ and to tune (or guide)
these models to be able to apply them to
specific situations.
GENERATIVE AI MARKETING
USE CASES
A lot of discussion on GAI in a marketing
context has focused on its ability to
accelerate and automate the creative/content
generation process, but there are a number
of valuable use cases beyond this.
Creating scaled content variation
The rich availability of audience and
contextual data has enabled the creation of
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Thomas
sophisticated dynamic creative optimisation
(DCO) solutions which combine creative
elements to create almost limitless variation
in creative. Despite these tools’ power,
however, they still mostly rely on the raw
components and their variants being created
manually, and they work better for
advertising use cases than, for example,
e-mail. GAI can provide additional
capability here by creating content variations
(especially textual ones) which convey the
same information but vary in their tone,
length and emphasis of key points.
One of the key limitations here for GAI
solutions is that GAI models struggle to
maintain fidelity to reference assets such as
brand imagery, and they cannot be relied on
not to violate brand guidelines without
supervision.
For example, Stability.ai’s DreamStudio
tool can take an image and generate variants
of it using the Stable Diffusion algorithm.
Starting with an image of the Nike logo, and
using the prompt ‘the nike logo in beautiful
colors surrounded by exotic flowers’
generated the outputs shown in Figure 1.
As can be seen from the generated
images, the model has modified the Nike
logo — even the third example is slightly
modified and would therefore not pass
muster from a brand perspective.
For this reason, GAI functionality is
likely to be incorporated into more
structured DCO and content generation
toolsets, such as Adobe’s Firefly technology,
which is used to add generative capabilities
to tools like Photoshop and Illustrator.
Given the (comparatively) high cost of
generating creative variants with GAI
(versus just using combinatorial
techniques), it is likely that a blended
approach will be most valuable, where GAI
is used for message ideation and a more
workaday combinatorial approach used for
variant generation. This approach is
exemplified by Adcreative.​­ai, an online
creative generation tool primarily aimed at
small to medium businesses who need to
create new campaign messaging, and
variants on that messaging, quickly. Larger
advertisers (and their agencies) may use
separate toolsets for these two parts of the
creative generation process (Table 2).
Unsurprisingly, the major players in the
advertising industry have announced that
they will be providing GAI-enabled creative
tooling for their platforms. Meta has
announced5 that it will be debuting
generative capabilities in its ad platform by
the end of 2023, and Google has also trailed
similar functionality in its platform.6
Creative classification and feature extraction
As automated creative generation has taken
off, one of the challenges it has created is in
measurement — if many variants of an
advert are used in a campaign, it is essential
to know which ones the audience is
responding do. Historically, creative agencies
Figure 1: Nike logo variations generated by Stability.​­ai
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Using generative AI to turbocharge digital marketing
Table 2:
GAI/DCO strengths and weaknesses
Generative AI
DCO
Good for
• Accelerating creative origination using
prompts/reference material
• Generating natural language variants that are not
template-based
• Identifying ‘rich context’ — eg parsing web page
content and using this to customise advert copy
• Creating structured variants on core
creative
• Generating variants for segmentation/
targeting (either at user level or contextual level)
• Brand safety (working with and staying faithful to existing brand assets)
Less good at
• Working with/recombining existing creative
assets
• Generating structured data for analytics
• Coming up with ‘new’ ideas
• Incorporating rich context
and media agencies have not worked
together well to create this layer of metadata
that is necessary to categorise creative
variants, especially along multiple
dimensions, which makes it harder to
understand which creatives worked well,
which did not and why that might be.
One of the key features of GAI models
is their ability to create a simplified
representation of complex textual, image or
video information. This technique can be
used to take a set of creative assets and
create structured metadata about them that
can be used to classify them, aiding in
subsequent performance analysis. Even
better, such attribute extraction can be
done on a post-hoc basis using categories
that were not thought of when the creative
was created.
Localisation
The current generation of large language
models are very good at machine
translation across the more popular
languages, with an increasingly impressive
grasp of idiom, which makes them practical
for localisation of advertising creative at
scale, especially in more utilitarian contexts
(where the language is relatively simple).
Additionally, image GAI models can also
create localised variants of visuals for
adverts to align with local culture and
preferences. Combined with the ability to
easily create multiple variants of creative,
this means that brands can scale their
campaigns internationally much more easily
and select the creative that works best in
each market or region.
Recommenders and personalisation
The core techniques used in GAI models
makes them quite well suited to
recommendation tasks, especially over
relatively bounded sets of options. Again,
the technique of creating embeddings,
which is foundational to GAI models, is of
great use in recommender scenarios since it
enables identification of items (such as
products) that are similar to each other across
a range of dimensions (which can include
the characteristics of people who buy those
products, or other products that are also
bought alongside).
Although it is possible to build a
recommender system on top of a
commercial large language model (such as
GPT or PaLM) by feeding information
about the user and the available options/
products into the prompt, this is likely to
be a very inefficient way to address this
need at anything below low volumes,
and it runs into significant privacy issues.
A more compelling use case for LLMs here
is to combine the recommendation with
content generation — for example,
weaving recommendations into a
personalised marketing e-mail alongside
other content.
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MarTech platform integration
A major use case for large language models is
the generation of code. Commercial
platforms like GitHub are already integrating
‘copilot’ functionality that can generate code
based on textual prompts and to complete
existing code. This dramatically reduces the
friction of creating new code, especially for
‘utility’ scenarios such as the creation of
integration code that uses a published API.
The fragmented nature of the
MarTech/AdTech ecosystem means that
integration of disparate platforms is a major
challenge for organisations, so the
automation of this process by GAI tools is
likely to have a major impact. Low-code
integration vendors like Zapier are already
taking advantage of GAI technology in their
own platforms.7
Natural language analytics
A special case of text-to-code generation is
the creation of SQL (or Python) code that
can be used to retrieve and return data to
answer a specific question. This has the
potential to transform how individuals
interact with data and may be the tipping
point for driving much greater data literacy
and usage within the organisation. However,
these tools still make both obvious and more
subtle mistakes in the code they generate,8
which creates significant risk that non-data
specialist users will accidentally create
misleading or inaccurate numbers with these
Table 3:
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kinds of tools. Specialist analytics personnel
will probably still be needed for some time
to come to prevent these mistakes from
occurring.
GENERATIVE AI IMPLEMENTATION
MODES
Organisations that wish to utilise GAI
models have three major options available at
present, which provide differing sets of
benefits:
• direct usage of a public model (eg
ChatGPT) via interface or API;
• usage of a dedicated instance of a
foundational model, hosted in public cloud,
with or without model tuning;
• usage of an on-prem or private cloud
instantiation of a foundational model, with
or without tuning.
Public model usage
The buzz about GAI has been created by the
public availability of several tools that have
been built on GAI models. Some of the
most popular tools at present (at the time of
writing, July 2023), and the models they are
based on, are shown in Table 3.
In addition to their web/mobile
interfaces, several of these services also
provide public application programming
interfaces (APIs), enabling developers to
build applications that incorporate GAI
Current popular generative AI tools and models
Tool
Type
Model
ChatGPT
Text to text
GPT-3.5/GPT-4
Bing Chat
Text to text
GPT-4
Google BARD
Text to text
PaLM 2
Anthropic Claude
Text to text
Claude 2
Midjourney
Text to image
Midjourney v5
Bing Image Generator
Text to image
DALL-E 2
Dream Studio
Text to image
Stable Diffusion
GitHub Copilot
Text/code to code
OpenAI Codex (based on GPT-3)
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functionality very easily. However, there are
several serious limitations with using these
tools, as set out below.
Data security/privacy
When ChatGPT launched, information sent
in prompts was used to train the models,
meaning that confidential information
provided as part of prompts could find its
way into responses. Since March 2023,
OpenAI has changed its policy — prompt
data is only used for training if the user opts
in. Nevertheless, users of services based on
public hosted models should not include
personal or company confidential
information in their prompts (in much the
same way that people should not include
personal information in their search queries).
This issue has caused many organisations to
ban the use of ChatGPT altogether,9
especially in the financial sector.
No specialised expertise
The default version of public models, while
well versed in a huge range of content from
the public Internet, cannot answer questions
or create content based on private
information (such as a company’s product
details) to which they do not have access.
While it is possible to tune the performance
of these public models using prompt
engineering and embedding techniques, as
explored below, those techniques fall foul of
the limitations around data privacy and
security already mentioned above.
Cost
The pricing models for public GAI tools are
based on usage and data volumes. The
OpenAI APIs are priced by ‘token’ (both on
the prompts and responses). A token is
roughly four characters or about 0.75 of a
word. The GPT-4 API currently costs
between US$0.03 and US$0.06 per thousand
tokens. Feeding complex prompts to these
services at high volume could thus generate
quite significant costs — for example, a
thousand 1,000-token prompts generating a
thousand 500-token responses on GPT-4
would cost US$60 (though GPT-3 is
currently around a tenth of this price).
Private instance of cloud-hosted
foundational model
A better approach is to use one of the
available hosted GAI models provided by
the major cloud providers. Amazon’s
Bedrock service provides an environment
for hosting generative models from
Anthropic, Stability.​­ai (Stable Diffusion) and
Amazon itself. Microsoft provides the Azure
OpenAI service which makes it easy to get
up and running with GPT and DALL-E
models from OpenAI, while Google’s
Vertex AI platform supports its own set of
GAI models, such as PaLM, Imagen and
Codey.
The first virtue of a dedicated cloudhosted GAI model is that it provides a much
more secure environment for data that may
be passed through prompts. Although
organisations should still pay attention to
data security and privacy issues (as discussed
in more detail below), these will more
closely resemble the kinds of issues the
organisation is already addressing in its use of
cloud-hosted services.
The second advantage of this approach is
that it enables the organisation to customise
the model to address the target use case
more effectively.
For example, imagine an Internet service
provider (ISP) that provides home Internet
service and wants to deploy an LLMpowered chatbot to help customers diagnose
connectivity issues at home. The following
prompt entered into ChatGPT generates the
answer below (summarised by ChatGPT
from a longer, more comprehensive answer):
PROMPT: My internet at home isn’t
working. How can I fix it?
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ANSWER (summarized):
1. Check modem and router connections.
2. Power cycle modem and router.
3. Restart your devices.
4. Verify signal lights on modem and router.
5. Test a wired connection to isolate Wi-Fi
issues.
6. Reset your router to default settings.
7. Check for service outages with your ISP.
8. Update firmware and drivers for modem,
router, and devices.
9. Contact your ISP’s technical support for
further assistance.
This is a good and helpful set of generic
suggestions, but if the ISP wants to include
any information that is specific to their
businesses or products (such as how to use
the web to check for service outages, or the
connection from the router) they need to
customise the language model with this
information. Broadly, there are two ways of
doing this:
1. prompt engineering: structuring and
augmenting prompts to create more
helpful responses;
2. model tuning: augmenting the foundational
model with additional information.
Prompt engineering
Prompt engineering is the process of
including structured information in the
prompts provided to text-input-based GAI
services to guide their responses. Including
the right additional information within
prompts can significantly improve the
quality of responses.
Some key prompt engineering techniques
include:
Creating the right system message: The
system message is a special message to the
LLM that sets its overall behaviour. For
example: ‘You are a helpful assistant
working for XYZ corp, an ISP. You can
answer questions about internet connectivity
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and account management. If you are asked
other questions, answer, “I’m afraid that’s
outside my expertise area.”’
Use few-shot learning: You can increase the
chance that an LLM will generate a useful
response by providing several examples of
what a successful response looks like before
asking your question.
Provide clear instructions and prime the output:
By providing clear instructions for the LLM
(for example, ‘Provide a step-by-step guide
to diagnosing Internet connectivity
problems’ and repeating them at the end of
the prompt (eg ‘Step 1’:) you can ensure the
response will be effective and in the format
that you need.
Use chain-of-thought prompting: LLMs will
often generate better results if asked to include
their train of thought within the answer.
Integrate external data: An app built on top of
an LLM can call out to an external data source
and include data from this source in the
prompt. This could be search engine results, or
relevant information about the user entering
the query. In our ISP example, this could be
information about the user’s hardware setup.
For working with large quantities of external
data, embeddings can be used.
Embeddings
OpenAI’s GPT-4 model API will accept
prompt input of up to 32,768 tokens (about
24,500 words) — equivalent to a 100-page
novel, while Anthropic’s Claude model will
handle up to 100,000 tokens. This means
that entire documents can be passed through
prompts and the LLM asked to perform tasks
such as summarisation, translation or style
modification or to use the content as context
to answer specific questions. In our example
above, the ISP could pass their customer
service manual through the prompt to
provide the context to answer the question.
However, passing a large block of content
through every prompt session will cause
costs to be high — for example, at current
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prices,10 a single 24,000-word prompt to
GPT-4 that generates a 1,000-word response
(say, a summary of the content in the
prompt) would cost around US$2. It would
not take very long for the cost of such
queries to mount up.
A way to address this issue and enable
prompts to incorporate context from even
larger corpuses (including image data) is to
use embeddings. Embedding is the process
of creating a simplified multidimensional
representation (a vector) of input data (such
as words, sections or text, or regions of an
image). Input elements with vectors that
are close together in multidimensional
space are more similar than those that are
far apart.
Embeddings have been used for some
time for natural language processing, having
been proposed by Google researchers in a
paper in 2013.11 They can be used to
augment prompt context via a three-step
process:
• Step 1: Pass the supporting content
through an embeddings API to generate a
vectorised index into the content and store
this.
• Step 2: When a user enters a prompt,
vectorise the prompt and use this vectored
representation to retrieve relevant sections
of the supporting material.
• Step 3: Append the retrieved supporting
material to the user’s prompt and pass to
the generative AI model.
Cloud vendors are already creating
services to make this process easier.
Microsoft Azure’s ‘OpenAI on your data’
offering12 uses the Azure Semantic Search
service (which uses embeddings to vectorise
words and text) in conjunction with the
OpenAI GPT APIs to enable just this kind
of use case. An easy-to-use consumer-facing
example of this kind of setup is the tool
ChatPDF, which enables the user to upload
a PDF file and then use a chat interface to
ask questions about it.
Embeddings are also used in some
image generation models, such as Stable
Diffusion, to enable a starting set of images
to be used as the baseline for the model so
that it generates output with a more
consistent style.13
Model tuning
Effective use of prompt engineering and
embeddings can improve the performance of
a GAI model, but for some scenarios it will
be necessary to fine-tune the model itself.
Examples of these scenarios include:
• ensuring the best performance across a
defined range of inputs on a specialised
topic;
• tuning the model to a specific kind of task,
such as sentiment analysis.
Model tuning is a more time-intensive
process than prompt engineering and
involves creating a structured set of data in
a prompt/response format, where each
prompt/response pair provides a training
example for the model. For example, for
our ISP, an example prompt/response pair
might be:
Prompt
Response
How do I check if my
Netgear Nighthawk
R7000 router is connected to the Internet?
First check that the
LED on/off switch on
the back of the router
is switched to the ON
position. The Internet
LED on the top panel of
the router (second from
the left) will be illuminated with a solid or
blinking white light if the
router is connected to
the Internet. If the light is
solid amber, the router is
connected to the modem
but no connection to the
Internet is detected.
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For most training scenarios, several
hundred such prompt/response pairs will be
needed.
The benefits of model tuning over
prompt engineering are as follows:
• Prompts to a tuned model can be shorter
as they do not need to include contextual
data (since this is already now in the
model), saving token costs.
• Because of the shorter prompts, the model
will return answers more quickly.
On the other hand, tuning models comes
with some downsides:
• Creating the training material is timeconsuming.
• A tuned model is likely to be more
expensive to use per token (for example, a
tuned version of GPT-3 is about a hundred
times more expensive per token than its
untuned counterpart).
• There is certain data that you cannot use
to tune a model (for example, quickly
changing data or customer data).
Running a private model on-premise
on private cloud
Many use cases for GAI will rely on data
that must be very tightly controlled and
perhaps restricted to on-premise or private
cloud environments. In this case an
organisation will need to provide the
hardware to train (as necessary) and run the
model itself. Many open-source models
(including versions of GPT and Stable
Diffusion) are available on Hugging Face.14
NVIDIA’s Triton Inference Server provides
a platform for training and deploying models
on a local server, taking advantage of
multiple CPUs and GPUs for model scaling.
A benefit of using models in this way
(whether on-premise or in the cloud) is that
the cost profile is determined by the
compute and storage required to support
them, rather than a token-based cost. This
278
can have benefits in a more scaled
environment, especially if the model needs
to be tuned.
IMPLEMENTATION CONSIDERATIONS
AI regulation
Article 22 of the General Data Protection
Regulation (GDPR) grants data subjects the
right not to be subject to automated
decisions (eg by AI systems) that have a
significant or legal impact on their lives
(such as being offered a loan or a job). Most
automated decision making for marketing
(eg advert targeting) would be unlikely to
meet this criterion, though dynamic pricing,
or making someone aware of, say, an
insurance product that another person was
not made aware of, could potentially reach
this bar.
Relatively few GDPR enforcement cases
have listed a breach of Article 22 as a factor,
but the Italian DPA fined two food delivery
companies — Deliveroo15 and Foodinho16
— a combined total of €5.1m for their use
of AI models to assess their drivers’
performance in a way that was neither
transparent nor deemed necessary for the
functioning of their businesses.
In addition to the GDPR, the EU is (at
the time of writing) proposing a draft law to
regulate the use of AI, known as the EU AI
Act.17 The final form of the law is still being
worked out with the member states, but the
core framework will be based on a
classification of AI systems according to the
risk they pose, in three categories:
• unacceptable risk (banned);
• high-risk (allowed but must be registered
and conformity with regulation
established);
• low-risk (no action required).
Generative AI is mentioned specifically in
the draft legislation, primarily in the context
of notification to users of its use.
Organisations will be required to:
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• disclose that content was generated by AI;
• design the model to prevent it from
generating illegal content;
• publish summaries of copyrighted data used
for training.
These requirements provide an incentive
for organisations to use models which can
provide information about the copyrighted
data they have used for training and may
even result in EU-specific versions of public
models.
Privacy and GDPR
Organisations that have used third party
services (such as public cloud platforms or
SaaS products like Salesforce) have long had
to ensure that these services (and their
providers) are GDPR-compliant; as data
processors under the law, they have a
responsibility to store and process the data
safely, retain it for an appropriate period and
facilitate the retrieval or deletion of the data
in response to a data subject request.
The foundational GAI models provided
by the likes of Microsoft and Google are
available as hosted services on those vendor’s
public cloud platforms, and so the same rules
will apply to the ingestion of any personal
data into them, most notably that personal
data can be removed from them at a data
subject’s request. For this reason, personal
data must not be used in fine-tuning models,
as it will be impossible to remove
subsequently without entirely retuning the
model, which will be resource-intensive and
expensive.
Organisations using cloud-hosted models
will need to pay attention to cross-border
data transfer, especially transfer of data out of
the EU. The major cloud vendors are
making their GAI models available in
different regions around the world, but
organisations should pay attention to where
apps built on top of such services are hosting
their data. In the rush to get into the GAI
market, some app vendors may take
shortcuts on areas like privacy policies and
practices, so these should be carefully
assessed.
Non-AI regulation
Because GAI models are generating content
and supporting automated decision making,
their outputs will be subject to other
regulation, such as advertising standards and
copyright law. Creating many variants on
advertising creative using a GAI model,
especially using contextual or customer data,
increases the risk that one of the variants
may be misleading (for example, offering a
service in an area where it is not available or
conflating two conflicting product
attributes). Advertising regulatory bodies like
the UK’s Advertising Standards Authority
are already strengthening their own use of
machine learning to be able to detect
misleading or offensive adverts at scale.
Complying with appropriate copyright
laws is another challenge that GAI-created
content will need to contend with. OpenAI
and Meta are the subject of multiple
lawsuits18–20 claiming that they have used
copyrighted work to build their models;
organisations using GAI for content creation
could find themselves caught up in these
lawsuits or new ones if the generated
content too closely resembles copyrighted
works. The risk of accidental (or lazy)
plagiarism using LLMs is high.
SUMMARY
Generative AI will have a significant impact
on the digital marketing industry,
particularly in the area of automated
creative generation, though its applications
will deliver wider benefits around
integration and analytics. Organisations
using GAI tools should pay close attention
to data security, privacy and ethical
considerations to ensure that they do not
create reputational or legal risk through the
use of this new technology.
© Henry Stewart Publications 2054-7544 (2023) Vol. 9, 3 270–280 Applied Marketing Analytics
279
Thomas
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Applied Marketing Analytics Vol. 9, 3 270–280 © Henry Stewart Publications 2054-7544 (2023)