Zero to AI: A Guide to Becoming an AI-Driven Marketer
“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within three years.” ?Mark Cuban
TL;DR Summary
In early 2000, a young Jeff Bezos, founder of Amazon, sat down for an interview with a reporter from a seemingly amateurish Dutch television program at the Amazon offices in Seattle. As Bezos was starting to introduce himself on camera, he was abruptly interrupted by the interviewer to adjust the staticky sound that was coming from his microphone. As the producers fiddled with the mic, Bezos waited patiently on-camera with an understanding smile, appearing happy just to be interviewed. At this point, Amazon was just five years old and still very much an internet start-up. When the audio finally got fixed, the interviewer launched into her first question with a thick Germanic accent: “What is Amazon.com, and what’s the mission of Amazon.com?” Bezos smiled and eagerly launched into his prophetic response on his vision (remember, this was back in 2000):?
“We want to be earth’s most customer-centric company. So we’re trying to build a completely new standard and raise the bar worldwide on what it means to be focused on customers and obsessed over customers. We’re not a book company, we’re not a music company, we’re not a video company, we’re not a toy company or an electronics company 一 even though we sell all those things. Instead, we’re a customer company. It’s a completely new concept.
“If you talk about being customer-centric, there are two parts to that. The first is sort of the traditional meaning of customer-centric, and this is listening to customers, figuring out what they want, and giving it to them. The second meaning is completely new for the internet, and that is personalization: putting the customer 一 each individual customer 一 at the center of your universe and building the perfect store for that customer. It’s about getting the right product to the right customer.”????
At its core, from the very beginning, Amazon was a customer-obsessed company. They believed in delivering the best customer experience, and the key to that was getting the right product to the right customer. As an internet retail start-up, they were the pioneers in building a product recommendation engine: a machine learning model that mines customer data to predict what products customers have a propensity to purchase next. Over the next decade, the machine learning algorithms that powered Amazon’s recommendations had been continuously fine-tuned and improved based on the troves and troves of customer and transactional purchase data feeding it. In other words, the recommendation engine was getting smarter at predicting what customers wanted to buy. Much better. By 2013, a McKinsey study claimed that up to 35 percent of Amazon’s total sales were driven by product recommendations alone.??
In recent years, the cost of processing power and data storage has been getting cheaper, and with it, the cost of prediction. This, along with the rapid advancements of data, digital and technology, led to major advancements in the field of machine learning as predictive methods had become faster and more effective at prediction, especially in a supercharged form known as deep learning. In an interview with Wired Magazine, the head of Amazon’s recommendations team, Srikanth Thirumalai, explained how he proposed to Jeff Bezos back in early 2014 to completely revamp and modernize the twenty-year-old recommendation system at the time (which was already generating 35 percent of all sales, remember) to apply deep learning capabilities to get even more predictive: “No one had really applied deep learning to the recommendations problem and blown us away with amazingly better results, so it required a leap of faith on our part.” he said.?
A leap of faith they took.?
To get a sense of how good Amazon’s recommendations have gotten since then, you need not look any further than the patent Amazon obtained for what it calls “anticipatory shipping”. Essentially, the patent describes a system of delivering products to a customer before they place an order. Presumably, the recommendation engine is so good at predicting what a customer needs based on their browsing behavior, product searches, preferences and habits that Amazon, as the patent suggests, would simply ship recommended products to customers without them having to order. In other words, customers would receive products before they even knew they wanted it.?
As Ajay Agrawal, professor at the Rotman School of Management at the University of Toronto, my alma mater, explains in a McKinsey article about his bestselling book Prediction Machines: “By doing this, Amazon could increase its share of wallet for two reasons. The first is that it preempts you from buying those goods from its competitors, either online or offline. The second is that, if you were wavering on buying something, now that it’s on your porch you might think, ‘Well, I might as well just keep it.’ And if you don’t want an item that was shipped to you, you can simply return it. Or perhaps Amazon will just let you keep it as a gift, as the patent anticipates: “Delivering the package to the given customer as a promotional gift may be used to build goodwill”. The patent states.
This, and other parts of Amazon’s business today such as pricing strategies, warehouse optimization, fraud detection, and voice-enabled technologies like Alexa, are a part of Amazon’s AI Flywheel. The AI Flywheel is Amazon’s strategic framework that represents their holistic approach to incorporating AI across its entire business. The concept of the flywheel is based on a self-reinforcing system in which AI enhances different aspects of the business, such as data collection, analysis, customer experience, and decision-making. As each element of the business improves, it further reinforces the other components, creating a continuous cycle of growth and innovation.
The Power of AI
Customer experience has been a key differentiator for businesses across various industries for decades. It has the power to transform brands, enhance customer loyalty, and ultimately drive revenue growth. With the rapid advancements in AI applications, marketers are better positioned to deliver personalized, efficient, and enjoyable customer experiences than ever before. AI has evolved rapidly over the last few years, from simple chatbots to sophisticated machine learning algorithms that can analyze vast amounts of data and make predictions. The potential for AI to reshape the customer experience is enormous, with countless applications across different industries. These applications include personalized recommendations, automated customer support, sentiment analysis, and fraud detection, among others.
One of the most significant advantages of AI technology is its ability to analyze vast amounts of customer data, uncovering hidden patterns and insights that can be used to improve customer interactions. By leveraging these insights, businesses can create more targeted marketing campaigns, improve product offerings, and optimize their customer service. As a result, customers enjoy a more seamless, personalized, and engaging experience with the brand.
How to Get Started
Getting started with AI may seem daunting, but with a clear understanding of its potential applications, a willingness to experiment, and a strategic approach, marketers can begin to harness the power of AI to revolutionize their daily activities and elevate their marketing efforts to new heights. If you're considering incorporating AI into your marketing efforts but don't know where to start, this guide will walk you through the process step by step, from understanding AI basics to implementing specific AI-powered marketing tools.
Step 1: Understand the Basics of AI
Before diving into AI-powered marketing, it's essential to understand the basics of AI and how it can be applied in marketing. Familiarize yourself with the key terms and concepts, such as machine learning, natural language processing, and deep learning. Research the various AI applications in marketing, such as customer segmentation, content creation, personalization, and predictive analytics. Here's a great McKinsey overview of Generative AI tools like Chat GPT and DALL E.
Step 2: Define Your Marketing Objectives and Challenges
Identify the specific marketing objectives you want to achieve using AI and pinpoint the challenges you currently face. This might include improving customer engagement, increasing conversion rates, or enhancing personalization. Understanding your objectives will help you determine which AI-powered tools and techniques are most relevant to your needs.
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Step 3: Assess Your Data Capabilities
AI relies heavily on data to function effectively. Evaluate your current data collection and management capabilities to ensure you have access to sufficient, high-quality data to support your AI initiatives. This may involve updating your data collection methods, implementing a data management platform, or cleaning and organizing your existing data.
Step 4: Build Your AI Team
Assemble a team of professionals with expertise in AI, data science, and marketing to help guide your AI implementation. This might include hiring new talent or training existing team members in AI and machine learning concepts. Having a dedicated AI team will ensure you have the necessary expertise to implement AI effectively in your marketing strategy.
Step 5: Choose the Right AI Tools and Platforms
Research various AI-powered marketing tools and platforms that align with your marketing objectives and challenges. These might include AI-driven content creation tools, customer segmentation software, or chatbot platforms. When evaluating tools, consider factors such as ease of use, scalability, and integration with your existing marketing technology stack.
Step 6: Start with a Pilot Project
Before fully integrating AI into your marketing strategy, it's a good idea to start with a pilot project. This will allow you to test the effectiveness of AI tools and techniques on a smaller scale, learn from any challenges, and make any necessary adjustments before scaling up. Choose a project that aligns with your marketing objectives and challenges, and monitor its progress closely.
Step 7: Measure and Optimize
Once your pilot project is up and running, continuously measure its performance using key performance indicators (KPIs) relevant to your marketing objectives. This might include metrics such as click-through rates, conversion rates, or customer engagement. Analyze the data collected during the pilot to identify areas for improvement and optimization, and make adjustments as necessary.
Step 8: Scale Up and Integrate AI Across Your Marketing Strategy
After a successful pilot project, gradually scale up your AI initiatives and integrate them across your marketing strategy. This might involve expanding AI-powered personalization across multiple marketing channels, implementing AI-driven content creation tools for all your content marketing efforts, or using AI for customer segmentation and targeting in your email marketing campaigns.
Step 9: Continuously Learn and Adapt
The world of AI is constantly evolving, and it's essential to stay informed about new developments, tools, and best practices. Encourage your team to participate in training, workshops, and conferences to stay up to date on the latest AI trends and techniques. Continuously refine and adjust your AI marketing strategy based on new insights and learnings.
Step 10: Share Your Success and Learn from Others
As you successfully implement AI in your marketing strategy, share your success stories, challenges, and learnings with others in your industry. Join online forums, attend conferences, and participate in networking events to exchange ideas and experiences with other professionals. This will not only help you stay updated on the latest AI trends and best practices but also provide valuable insights to improve your own AI marketing initiatives.
Step 11: Monitor Ethical Considerations and Regulatory Compliance
As you incorporate AI into your marketing strategy, it's crucial to consider ethical implications and comply with relevant regulations. Ensure that your AI algorithms and data practices are transparent, fair, and unbiased. Be aware of privacy concerns and abide by data protection laws, such as the General Data Protection Regulation (GDPR) or the Personal Information Protection and Electronic Documents Act (PIPEDA). By prioritizing ethical AI use, you'll build trust with your customers and avoid potential legal issues.
Step 12: Stay Agile and Embrace Innovation
In the fast-paced world of AI and digital marketing, it's essential to maintain an agile mindset and embrace innovation. Be prepared to pivot your AI marketing strategy as new technologies and techniques emerge. Encourage your team to experiment with new AI tools and approaches, and continuously iterate and refine your marketing initiatives. By staying agile and open to innovation, you'll ensure your marketing strategy remains cutting-edge and effective.
Putting It All together
Implementing AI in marketing is a multi-step process that requires a solid understanding of AI concepts, clear marketing objectives, a dedicated AI team, and the right tools and platforms. By starting with a pilot project and gradually scaling up, you can effectively integrate AI into your marketing strategy and drive significant improvements in performance. Don't forget to continuously learn, adapt, and stay agile as you navigate the ever-evolving landscape of AI and digital marketing. With dedication and persistence, you'll unlock the full potential of AI to transform your marketing efforts and drive business growth. The key is to take that all important first step!
Louis Cho is a globally experienced Marketing, Data & Analytics, and Customer Experience Executive with 20+ years of experience in leveraging data, digital and technology to drive customer loyalty, engagement and growth.
Director, GTM Strategy | Viper Partners | Sell-Side Healthcare Investment Bank
1 年Well said! #customerexperience Groupon uses Jasper for its product descriptions.