Traditionally, the answer has been in related areas such as data analytics and business intelligence. Through these, we leverage operational, customer, and external data sources to help us understand where to find customers, how to appeal to them with our products and services, and keep them coming back for more. I learned how to answer questions like how.
But today, artificial intelligence (AI) is paving the way for real value. AI has been described by Google CEO Sundar Pichai as the most transformative technology of all time. Marketing is often the first place companies find ways to create value with AI.
AI-Powered Marketing – Difficult and Expensive?
When the term AI is used in business today, it tends to refer to machine learning (ML). Machine learning (ML) is a set of computer algorithms that are getting better and better at doing simple (or not-so-simple) tasks. data.
This is a technology that may seem difficult, complex and costly if we are not familiar with it. But businesses of all shapes and sizes are increasingly realizing that harnessing that power to deliver impressive, often transformative results is surprisingly quick, easy and affordable.
Use cases for AI in marketing today include:
Personalization: We’ve always performed customer segmentation to be able to create promotions and ads that are relevant to specific groups, but with AI, we’re approaching customers as individuals in a more granular way than ever before. can take this to the next level.
Chatbots and virtual assistants: Offer advice, help, and support to your customers using natural language processing (NLP) technology similar to that used by Apple’s Siri and Amazon Alexa.
Predictive analytics: Predict customer behavior and market trends to identify patterns and clues, telling you where to focus your marketing spend for the best returns.
Sentiment analysis: AI algorithms can capture data about what your audience and customers are saying about your product, competitors, industry, or life in general and transform it into insights that help you market more effectively. increase.
take the first step
The first thing marketers should consider when trying to understand how this revolutionary technology can help is simply:
“How can I use it to meet my marketing goals?”
A good example of a marketing goal companies are currently using AI to achieve includes new customer onboarding (growth).Personalized marketing and better targeting of customers
messaging; reduce subscriber dropouts and churn; Develop the ability to increase the lifetime value of existing customers and capitalize on fleeting opportunities to grab attention and convert audiences into customers. This is only possible with real-time data.
These are covered in Adobe’s recently published guide to deploying and leveraging AI in business.with title Data, Insights, Actions: Machine Learning and AI for Marketing Analyticsand identify three interconnected systems that organizations must put in place if they want to use AI in their marketing efforts to turn data into revenue.
Systems of data – How can data be integrated across multiple channels to make it available from where, when, where and in the most useful way?
System of Insights – These are the tools and services you use to extract insights (information that is valuable, useful, or usable) from the data itself.
System of Engagement – How will you use these insights to achieve the business goals you identified as important when you first put together your strategy for working with AI?
As you begin this journey, it’s often a good idea to identify “quick wins,” initiatives that can be implemented quickly to prove the value of AI to your organization. A simple example is improving the open rate of your email campaigns by optimizing your messages. Another way is to reduce customer churn by improving the customer service experience when a defective product needs to be returned.
By choosing small, specific goals like these, you can demonstrate the value that AI can create to those who need it.
One tool that is often invaluable in this regard is Customer Data Platform (CDP) – A solution designed to integrate all the information an organization has to act as a “single source of truth” for customer data. “Extending an existing data lake system to provide the same services as a CDP requires significant new development. or buying and integrating each individual component, it will be much easier, faster and cheaper.” David LoveFounder, CDP Institute
for example, TSB Bank applies Adobe’s real-time CDP To understand our customers better. By integrating data from all online and offline channels, we were able to significantly increase the level of personalization across marketing materials. 200% increase in sales In 9 weeks, we saved £1m in marketing costs.
Other tools can manage the automation of important but repetitive and time-consuming tasks such as data cleansing and preparation. Adobe says data scientists average We spend 45% of our time preparing dataBy automating this work, data professionals can instead focus on high-value tasks related to extracting and leveraging insights.
Traditionally, analytics and data science in business might have been viewed as an esoteric art directed by highly trained and expensive data scientists.
Those days are gone. Today, integrated AI and ML platforms offer self-service, low-code and no-code capabilities. This means that analytics can be “democratized” across the organization. With the right systems in place, any marketing team member can log in and generate personalized reports containing the information they need to solve their own challenges. After achieving this, the team can begin to identify more ways to grow the business using AI and ML, and work together to make them a reality.
In business, marketing has always been an early adopter of new data and technology solutions and a pioneer when it comes to making them work and demonstrating value. This trend is likely to continue with the new generation of AI-powered, data-driven tools and platforms 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 to creating real growth and value.
Moreover, we are only scratching the surface of what AI means for business and wider society. But what is clear is that today’s marketer has the toolset to ensure he leads the way in building his AI-driven organization of the future.
Adobe sponsors this post, but the opinions are my own and do not necessarily represent Adobe’s position or strategy.