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  • Writer's pictureRadiance Technologies

How To Upgrade From Data-Driven To AI-Driven Marketing Analytics

It’s a question that’s perplexing to many of today’s business leaders – who often find themselves drowning in information but struggling when it comes to applying it to practical business challenges.

Traditionally the answers were found in data analytics and related disciplines like Business Intelligence. Through these, we learned how to tap into operational, customer and external data sources to answer questions like where to find customers, how to make our products and services appeal to them, and how to keep them coming back for more.

Today, though, it’s artificial intelligence (AI) that’s unlocking the path to real value. AI has been described as the most transformative technology of all time – by none other than Google CEO Sundar Pichai. And often it’s in marketing that businesses first find ways to

create value with AI.

AI-Powered Marketing – Difficult and Expensive?

When we use the term AI in business today, we tend to be referring to machine learning (ML) – computer algorithms that get better and better at carrying out simple (or not-so-simple) tasks as they are exposed to more and more data.

It’s a technology that may seem daunting, complicated and costly if we’re new to it. But businesses of all shapes and sizes are increasingly finding that it can be surprisingly quick, simple and affordable to harness its power and start driving impressive, often transformational results.

Some examples of the way AI is used in marketing today include:

Personalisation: We’ve always carried out customer segmentation to enable us to create promotions and advertising that’s relevant to specific groups, but AI lets us take this to the next level, by approaching customers as individuals in a more granular way than ever before.

Chatbots and virtual assistants: Providing advice, help and support to customers using natural language processing (NLP) technology similar to that used by Apple’s Siri or Amazon Alexa.

Predictive analytics: Forecasting customer behaviour and market trends in order to identify patterns and clues that tell us where we should focus our marketing spend in order to get the best returns.

Sentiment analysis: AI algorithms can capture data about what your audience and customers are saying about your products, your competitors, your industry or just life in general, and convert it into insights that help you market more effectively.

Taking The First Steps

The first thing any marketer needs to consider when trying to work out how this revolutionary technology can help them is simply:

“How can I use it to achieve my marketing goals?”

Some good examples of marketing goals that businesses are currently using AI to achieve include signing up new customers (growth); better targeting of customers with personalised marketing and

messaging; reducing subscriber drop-out and churn; increasing lifetime value of existing customers and developing the ability to capitalise on fleeting opportunities to grab attention and convert audiences into customers that are only possible with real-time data.

These are covered in a guide to deploying and leveraging AI in business, recently published by Adobe. Titled Data, Insight, Action: Machine Learning & AI for Marketing Analytics, it also identifies three interconnected systems that need to be in place if an organisation wants to use AI in its marketing operations to turn data into revenue.

These are:

System of Data – How can data be unified across the multiple channels from where it pours in, in order for it to be available where, when and in the form that it’s most useful?

System of Insights – These are the tools and services we use to extract insights – information that has value, serves a purpose or can be put to use – from the data itself.

System of Engagement – How do we put these insights to use to achieve the business goals that we identified as critical when we first put together our strategy for working with AI?

Quick Wins

When starting out on this journey it’s often advisable to identify “quick wins” – initiatives that can quickly be put in place that prove the value of AI to an organisation. A simple example might be improving the open rate of an email campaign by optimising your messaging. Another might be to reduce customer churn by improving the customer service experience when faulty products need to be returned.

Picking small, specific goals like these enables you to demonstrate the value that AI can generate to those you need to have onboard.

One tool that’s often invaluable in this regard is the Customer Data Platform (CDP) – a solution designed to unify all of the information an organisation has in order to act as a “single source of truth” for customer data. “Extending existing data lake systems to provide the same services as a CDP requires substantial new development. Acquiring a CDP with these functions already available will usually be much easier, faster, and less expensive than developing custom versions or buying and integrating separate components for each,” says David Rabb, Founder, CDP Institute

For example, TSB Bank applied Adobe’s Real-Time CDP in order to gain a better understanding of its customers. By consolidating data from across all of its online and offline channels it was able to greatly improve the level of personalisation across its marketing materials, which it directly attributed to driving a 200% increase in sales over a nine-week period, and a £1 million saving in marketing costs.

Other tools can manage the automation of critical but repetitive and time-consuming tasks like data cleansing and preparation – Adobe cites research stating that data scientists spend an average of 45% of their time on data prep. Automating this work allows data experts to instead focus on the high-value tasks associated with drawing out insights and putting them to use.

Self-Service AI

Traditionally, analytics and data science in a business might have been seen as an arcane art – dictated from on-high by expensive and highly-trained data scientists.

Those days are long-gone – today, integrated AI and ML platforms offer self-service, low-code and no-code functions. This means that analytics can be “democratised” throughout an organisation. With the right system in place, any member of the marketing team can log in and generate reports personalised to them, containing the information they need to solve their unique challenges. After achieving this, teams can start to identify more ways that AI and ML can be used to grow the business, and work together on making them a reality.

In business, marketing departments have always been early adopters of new data and technology solutions, and trailblazers when it comes to putting them to work and demonstrating value. This trend looks set to continue with the new generation of AI-powered data-driven tools and platforms that are emerging today.

Developing the ability to align this technology with marketing goals, while understanding the key use cases for analytics in marketing and advertising, is the first step towards creating real growth and value.

Beyond that, we’ve only just started to scratch the surface of what AI will mean for business and wider society. But what’s clear is that today’s marketers have the toolset to ensure they are leading the way in building the AI-driven organisations of the future.

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