Editorial: Generative AI and its impact on marketing analytics

As seen in the journal, Applied Marketing Analytics, Volume 9 Number 3

From the advent of the Internet, carrying on

a digital relationship with prospects and

customers has yielded a hefty amount of

behavioural and intention-revealing data.

The purpose of marketing analytics has

always been to improve the process of

getting a message out into the world,

improving the impact of that message on

the target audience and boosting the results.

Machine learning gave us the ability to

interrogate that data as never before.

Predictive, prescriptive and iterative became

the watchwords of the day.

Today, the watchword is interactive.

‘Interact’ as in converse. Being able to have

a chat with our data has long been promised

and has finally arrived. We can converse

with our data, asking strategic questions

rather than merely querying for facts and

figures. We also have the opportunity to put

the machines to work on our behalf and

that’s where ‘active’ of interactive comes in.

Generative AI can perform more of the

repetitive, tedious tasks like predictive

modelling and data visualisation that humans

have been saddled with. That automation,

and the resulting nuanced and granular

customer insights, affords us more time for

the strategic, decision-making jobs that

require common sense and intuition.

Newly released abilities for multiple

large language models (LLMs) to

communicate with each other, to take

action on conclusions, and our ability to

create our own generative pre-trained

transformers are yet another wave of

innovation. We are required to rethink

how we automate our work. At the same

time, we are required to consider how our

prospects and customers might converse

with us in new ways.

This issue of Applied Marketing Analytics

takes a close look at generative AI and its

uses for leveraging data for almost everything

in marketing. Seth Earley provides advice

on what key points executives need to

understand about knowledge management

and the use of LLMs. Vaikunth Thukral,

Lawrence Latvala, Mark Swenson and Jeff

Horn review best practices in optimising the

customer journey with LLMs, including

some of the pitfalls to avoid.

LLM use cases in marketing analytics are

covered by Katherine Robbert, Christopher

Penn and John Wall, while Jeff Coyle and

Stephen Jeske assess the impact of how AI

copilots can help transform mundane data

into golden insights and a more nuanced

understanding of customer behaviour.

We also have included papers with a

broader scope. Brandie Green presents a

framework for creating a data-driven

culture; even more important now, with the

arrival of generative AI. She focuses on the

challenge of data literacy and the continued

value of web analytics. Naeun Kim, Terry

Haekyung Kim and Jinsu Park turn their

attention to the adoption of analytics among

small-sized retailers to better understand

their customers and optimise their marketing

tools, lessons that are applicable to

companies of all sizes.

But the highlight here is generative

AI — the latest in analytics tools and

technologies. This is a fast-paced topic that

requires significant diligence to keep up.

At the same time, Ian Thomas reminds us

of the ethics of analytics. Our attention

is drawn to the technical, privacy and

copyright issues we need to acknowledge,

address and manage. With great data comes

great responsibility.

This edition of Applied Marketing

Analytics is a clarion call to think about

computing in a different way — as a

cognitive companion rather than a

calculator. This is the turning point where

we stop using bots and start employing

proactive agents — and so will our

customers.

As these agents get better at performing

multi-step tasks without explicit instructions,

our approach to devising, developing and

deploying systems will change dramatically.

The time to understand the

underpinnings of generative AI is now.