AI for Marketers - Data & Marketing Association | DMA · dataset. These different sets of data allow the ML to predict, inform and potentially act upon new experiences. The greater

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AI for Marketers: Information and AdviceAI for Marketers: Information and Advice

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AI for Marketers: Information and Advice

Contents

Introduction � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �03

A Very Brief History of Machine Learning and Artificial Intelligence � � � � � � �04

Understanding AI � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �05Automation and Workplace Culture � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 05Risks � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 06Privacy, Compliance, and Ethics � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 06

Understanding Machine Learning � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �08Background to ML � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 08Implementing ML/AI � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 09Applications in Marketing � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 09

Conclusion � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 10

About the Campaign � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 11

About the DMA � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �12

Copyright and Disclaimer � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �13



Introduction

Artificial Intelligence (AI) and Machine Learning (ML) are terms which have generated a lot of conversation and an excess of hype. While both have been around for some time, we now have the computer processing power to truly and effectively use them to increase efficiency and effectiveness and to deliver value and benefits to users. From a socio-economic perspective, the impact is comparable to the way electricity enabled a step-change in industry.

Like human intelligence, AI is not easy to define. It encompasses a wide range of algorithms that allow machines to perform ‘intelligent’ tasks. From the outside, it is more useful to define AI as a method for computers to learn about the world around them, to recognise patterns from previous experiences, and to make intelligent decisions.

The increase in the availability of affordable computing power and access to increasing volumes of data have enabled engineers to use the wide range of AI techniques available to drive pioneering advances, particularly in the fields of natural language understanding, machine vision, and reduced error-level predictive and responsive analytics.

For example, the performance of AI surpasses that of humans by automatically transcribing voice calls in real-time with a lower word error rate than humans; in business, legal contracts and financial anomalies can be identified in near real-time; and AI-driven automation can drive optimisations beyond natural human ability.



Machine learning and artificial intelligence are not new. Work on AI can be traced back around 100 years when Italian pathologist and Nobel Prize winners, Camillo Golgi and Santiago Cajal discovered that only certain neurons in the human brain absorbed silver-salts. This inferred a degree of individualism in brain cells, and the further discovery of discrete networks of individualised neurons led to repeated attempts to formularise and simulate the learning, categorisation of information, and adaptivity of human brain function using iterative calculus mathematics and primitive machines.

The real research into AI began in the 1950s with attempts to encode human intelligence into symbolic forms, such as logic rules, that could be processed by newer, more powerful computers.

There have been various rises and falls in interest since then.

With the advent of faster machines, interactive networks, and huge datasets, machine learning research took a different approach by seeking to automatically identify patterns within datasets and use those patterns for inference.

While some of these methods still explore applications of the original research into neural network simulation, a variety of alternative probabilistic ideas took hold. We now include all of these within the general term ‘Machine Learning’.

A Very Brief History of Machine Learning and Artificial Intelligence



AI can be implemented to solve a specific problem or as a systematic upheaval. While the former is the more risk-averse, changing well established manual processes can create cultural and moral concerns – especially when results are not guaranteed.

Automation and Workplace Culture

With AI- and ML-enabled automation, we can provide users with a smoother experience, and free up valuable time by automating mundane tasks and business processes. In turn, this makes it necessary to revise operational metrics; for example, a contact centre’s average call time may increase if easy tasks are resolved automatically, and operators deal with more complex issues.

To fully exploit these new efficiencies, business leaders need to devise new, more proactive and adaptive operating models to reshape processes with AI/ML. There will be a step-change in the people’s focus and required skill levels.

In an AI-enabled business, there should be a clear and inclusive plan to upskill where necessary so that employees can focus on more uniquely human work such as empathising, initiative, creativity, and helping customers.

To enable AI/ML, businesses will need to enhance their data capture capabilities and data management.

We refer to this detailed data as rich data. For example, customer service agents at a call centre might only capture the outcome of an interaction. If rich data for that call were available, such as recordings of the entire call, AI/ML could be used to analyse the content of the conversation, and how the interaction fits into a pattern of communication with that agent. This can help paint a complete picture of customer interactions and customer satisfaction, and can help to analyse and suggest improvements in call centre processes, benefits of which will be passed to the customer using these services.

All of these tremendously powerful opportunities require board-level buy-in and an acknowledgement that their business context is changing.

Successful leaders will provide the culture that facilitates and drives that change with new competencies around the development, deployment, and management of AI/ML-enabled services.

Understanding AI

06Copyright / DMA (2019)


Risks

At its core, AI/ML comes with considerable risks that, if unaccounted and unplanned for, can lead to damaging consequences for marketers and consumers.

Poor, untested, and unrestricted implementations can make bad decisions which can create distressing situations for service users or employees.

Besides the financial impacts, and because AI is currently in the public focus, companies should consider the reputational risks of a worst-case scenario before implementing new AI/ML solutions.

Some key risks that must be recognised and mitigated are:

• Bias

• False positives

• Misused language

• The fundamental issue of the programme reaching incorrect decisions (which often hints at a more significant problem)

Privacy, Compliance, and Ethics

To quote Simon McDougall at the ICO “I don’t think the person on the street knows what’s going on and that in itself is a concern for us.”

The use of personal data requires careful planning to make sure the solution is compliant with data protection and security requirements to protect the customer. But the planning shouldn’t stop there.

Even when the data is compliant, any business using AI/ML must check all of their processes against their ethical position, making sure that it demonstrates transparency and care in how it reaches decisions.

Leaders should establish working practices and mechanism around AI/ML to carefully monitor and manage the potential risks.



The new ICO AI framework is a good place to start:

Proposed Framework

1. Governance and Accountability

Cross-Cutting

Focus Areas

Risk appetiteLeadership

engagement and oversight

Management and reporting

structures

Compliance and assurance

capabilities

Data protection by design and

by default

Policies and procedures

Documentation and audit trails

Training and awareness

2. AI-specific Risk Areas

Fairness and Transparency in

ProfilingAccuracy

Fully automated decision making

models

Security and cyber

Trade-OffsData minimisation

and purpose limitation

Exercise of rightsImpact on broader

public rights*

*Includes only considerations within scope of an ICO investigation/audit

Whenever technologies become fashionable, there’s a risk that people will get carried away with the idea of what’s possible, hyperbolic claims, clairvoyant doomsday-ing, or ‘black-box’ generalities.

Leaders must make sure that they focus on the ‘art of the practical’. In the context of machine learning, this means taking an outcome-driven, deliverable business value, customer-interest approach.

Even when looking at all of these things, results must still be measured against specific success metrics.

In the light of such uncertainty, organisations must test their basic ability to action projects with in-house resources, engage outside assistance or procure ‘black-box’ AI solutions that can address the issues. This may well involve pausing ML projects until there exists a clear path to implementation and a reasonable chance of success.



Businesses should relate to ML from the perspectives of business outcomes and technical capability.

As a business outcome tool, it requires specific skills to identify which problems can be addressed by ML. This will most probably require a change in management interaction.

As a technical capability, besides core, niche ML skills, it requires a creative data management perspective, all implemented in a drawing together disparate datasets and orchestrating data for insight purposes as well as deploying ML into production systems and building effective backpropagation or other learning feedback loops.

High-quality data, and large amounts of it in the initial learning phase, allow ML programmes to identify deeper trends and insights. This helps the system make better predictions for the specific target area. This, in turn, yields even higher quality data, and so on, in a convergent iterative loop.

Success in one area will enable your team to deliver successes in other areas.

Background to ML

In a nutshell, Machine Learning, of which Artificial Neural Networks are a subset, is a specific discipline that seeks to ‘automatically’ identify trends – such as customer behaviour patterns.

These trends can be linear or complex – and can even be hidden within a varied dataset. These different sets of data allow the ML to predict, inform and potentially act upon new experiences. The greater the accuracy, the number of ‘varied’ experiences, and the range of initial guidance, the more accurate the system will be.

In other words, each ML method seeks to identify commonalities and trends to varying degrees of accuracy. It learns from a range of known experiences – the inputs, and matches them to known results – the outputs. The learning achieved in this training phase can be used to predict outcomes for new experiences. As such, the data used in the training and learning stage must be of the highest quality possible. If not, there is an increased risk that the programme will not work as intended.

Several applications of ML, especially simulated Neural Networks, include a continuous self-learning and adjustment mechanism to produce ever-converging and (hopefully) more accurate results These are sometimes called feedback loops or referred to as backpropagation.

Understanding Machine Learning



Implementing ML/AI

Beyond the, geeky, mathematics, the main considerations when implementing ML/AI should be:

• What is the problem?

• What does success look like?

• What tools and expertise do we have in-house, and do we need outside experts?

• What is the risk? Could there be any financial, resource, or reputational risk involved?

In turn, the immediate technical/operational questions should be:

• How good does the training data need to be?

• What data is available to the business?

• What degree of accuracy should the outcome have?

• How will all of this be measured?

Applications in Marketing

There’s no question: AI is a gamechanger for marketers and customers alike. As such, it’s essential to categorise the ways it could help and how to make sure that it does help.

Using our outcome-based method, here are some applications to consider:

• Deep customer insights

• Customer acquisition modelling, like learning from previous customer journeys to identify behavioural trends and key conversion factors.

• Chatbots

• Ethical profiling for intent and programmatic applications

• Customer retention optimisation – deep insights into customer satisfaction, call centre analysis, sentiment

• Voice search and natural language generation

• Content management and curation

• Personalisation

• Computer vision

• Channel attribution

This list could cover just about every area of marketing. So, where do we start?

Initially, marketers should avoid the temptation to blindly automate processes and view ML as another option to augment their data analytics.

It’s also advisable to avoid the urge to tick the AI box with ‘black box AI’ solutions that do not make their processes and outcomes clear.



This initial positioning paper focused on laying out the fundamentals around ML/AI for marketers, to show the benefit it can create for customers, and to serve as an agenda for future outputs.

Artificial Intelligence and Machine Learning provide vast opportunities for innovation, creativity, and an optimised customer experience.

However, it should never be looked at as the latest must-have bolt-on to your existing marketing and business infrastructure.

AI and ML rely entirely on historical data and require thought and planning in order to create potential business benefits as there can be a lot of risks and ethical implications associated with this technology.

When considering the use of this technology, you need to start with the problem you’re trying to solve. Consider your current business capabilities in terms of resource and infrastructure, and work backwards from there.

Hopefully, this document has clarified what AI and Machine Learning means to marketers. Future pieces will cover:

• Case studies covering applications of AI in different sectors, including B2B and social media

• Implementing AI and Machine Learning into your current infrastructure

• Commercialising AI and the return on investment of AI

This work is being carried out by the Connected Technology Hub, part of the Data Council, with special thanks to:

• Alex Granat, Chair, Connected Technology Hub & Strategy Director, Connections Group

• Dr Neil Mackin, Technical Business Development Manager, Amazon • The Lord Clement Jones, CBE• Dr Tim Drye, Chief Data Officer, Datatalk

Conclusion



Changes to the governance of data have far-reaching consequences for your business.

The new General Data Protection Regulation (GDPR) has already had an effect on how your business does business, and how it manages, protects and administers data in the future.

The new regulation came into place in 2018 and is still making waves.

At the DMA, we want to demystify these regulations and offer support to help you work to the best of your ability.

We also run events to encourage the practice of Responsible Marketing. Our popular Legal Updates discuss the current political and legal affairs affecting the industry and allow you to speak directly with the DMA’s finest legal minds. Keep an eye on your emails, or visit our events page to book your spot.

For those dealing with vulnerable consumers, we have a masterclass in recognising the needs of vulnerable consumers and how to make reasonable adjustments to benefit a broad range of employees working with customers in vulnerable circumstances.

This campaign is brought to you in partnership with OneTrust, the largest and most widely used technology platform for operationalising privacy, security and third-party risk management.

Responsible Marketing

About the Campaign

https://dma.org.uk/responsible-marketing



The Data & Marketing Association (DMA) comprises the DMA, Institute of Data & Marketing (IDM) and DMA Talent.

We seek to guide and inspire industry leaders; to advance careers; and to nurture the next generation of aspiring marketers.

We champion the way things should be done, through a rich fusion of technology, diverse talent, creativity, insight – underpinned by our customer-focused principles.

We set the standards marketers must meet in order to thrive, representing over 1,000 members drawn from the UK’s data and marketing landscape.

By working responsibly, sustainably and creatively, together we will drive the data and marketing industry forward to meet the needs of people today and tomorrow.

Published by The Direct Marketing Association (UK) Ltd Copyright © Direct Marketing Association.

All rights reserved.

www.dma.org.uk

About the DMA

http://www.dma.org.uk



‘AI for Marketers: Information and Advice’ is published by the Data & Marketing Association (UK) Ltd Copyright © Data & Marketing Association (DMA). All rights reserved. No part of this publication may be reproduced, copied or transmitted in any form or by any means, or stored in a retrieval system of any nature, without the prior permission of the DMA (UK) Ltd, except as permitted by the provisions of the Copyright, Designs and Patents Act 1988 and related legislation.

Application for permission to reproduce all or part of the Copyright material shall be made to the DMA (UK) Ltd, DMA House, 70 Margaret Street, London, W1W 8SS.

Although the greatest care has been taken in the preparation and compilation of this report, no liability or responsibility of any kind (to the extent permitted by law), including responsibility for negligence is accepted by the DMA, its servants or agents. All information gathered is believed correct at October 2019. All corrections should be sent to the DMA for future editions.

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AI for Marketers - Data & Marketing Association | DMA · dataset. These different sets of data allow the ML to predict, inform and potentially act upon new experiences. The greater