Service agencies and product companies are constantly looking to improve their value propositions. They carry it out by specifying customer profiles outlining their needs and expectations. That, for example, explains why Agile is so popular among startups - it brings a data-driven approach to the business and enables them to work more efficiently.
Product teams could track their progress and decide where to go next. That way, there is no need to spend many resources on product development unless they confirm its value.
Due to the principles of Agile, companies receive a significant advantage over competitors.
Though the next point becomes a challenge at the stage of scale: where the demand is right now? How to reach out to those who require a service at a particular moment?
Knowing the location and time where a product is needed is key for marketing. In this circumstance, a vendor could propose an advanced cost to justify their expenses. It's just a classical notion, which has not changed in times. Besides that, it is the main factor affecting its sales and growth.
Big tech companies focus on predicting the demand. They utilize the incoming data like historical statistics and external public API sources to forecast the ordering in the nearest future. Once found, the systems begin to complete the request as if customers have already made it. This advantage becomes possible due to the implementation of AI in their marketing strategy.
Practical Examples of AI in Marketing
For instance, Uber dispatches the car to a rider before a trip has been booked. Amazon ships the package to a buyer even earlier than it was purchased. The mentioned companies analyze the data in real-time and identify the time and place for future demand. Every business component, including making an offer, delivering the service, and setting a price, operates due to AI-powered solutions.
The current article contains many examples of AI in marketing from Amazon, Uber and some other startup companies. We hope it will interest you and help come up with useful ideas regarding your business.
Artificial intelligence functions similarly to the human mind. It analyzes the incoming information, defines the relationships between the elements, constructs the patterns and uses them for further reflections.
Same as the human mind, artificial intelligence makes predictions based on previous experience. It studies the successful events, determines their criteria and utilizes them for the next processing operations.
For example, the picture below demonstrates how the AI-based solution helps marketers leverage the lead generation process.
First, it profoundly analyzes the leads to indicate their common factors. The system makes an analysis and finds many factors that must be considered.
This kind of work would be challenging for humans to perform, as it requires keeping multiple points in mind while comparing. Though computer systems are trained by data scientists so they can carry out this job perfectly.
The above-shown picture will help us figure out the process of using AI in the lead qualification process of marketing. Its algorithm is based on the clustering principle.
Clustering is the main principle of data analysis with AI. Let's study it by overviewing a simple case coming from the practice of lead generation work.
About the clustering in simple words
Having done preliminary research, a data analyst receives a few models describing their customers. The row with the green rounds emphasizes the pattern of sales-qualified leads. But the first task will be to check if this conclusion is correct.
After a while, the specialist gets new data from CRM. The information contains the entire scope of leads, including the new paid customers. Now the AI machine clusters the whole information and checks if the parameters of closed deals coincide with the proposed model. In the course of analyses, it found a few contacts who match a green-colored flow. It means that the AI system proved the pattern of the sales-qualified person.
Next, the company could obtain the contact database from outside and sort it out, finding a cohort of sales-qualified leads and suggesting it to the sales division. This way, the company significantly saves its time in lead qualification work.
Data Fuels The AI-powered Business
Data is fuel for AI-powered ventures, so it should be collected immensely. Meaning thousands, hundreds of thousands of specifics regarding clients, their preferences and consumer behavior. These statistics should be fetched daily or hourly in CRM or data storage because real-time analytics is a core for that business.
How Does AI Affect Marketing
AI-based systems process the streams of information received from various resources. They function in real-time and constantly analyze the new information. The amount of data they can work with is quite impressive.
Uber, for example, manages 18 million rides per day. Amazon ships over 66 thousand orders per hour, i.e., 18.5 orders per second. Besides that, both platforms set an individual price for each order.
Benefits of AI Marketing:
- Marketing Research. AI helps marketers analyze dozens of information regarding persona buyers, their customer journey, purchase preferences and expectations.
- Demand Prediction. Based on historical data and market analytics, AI determines goods and services that will be required in a certain location soon.
- Personalization. Knowing the preferences and expectations of customers, AI could send an offer earlier than the prospects would search for. This way, the company overtakes its competitors and sets an individual price.
- Automated Delivery. Artificial intelligence oversees the entire process of purchase delivery, from the moment when a customer ordered it on a website to the event when they confirm it was delivered.
- Gamification. As far as AI could predict opportunities coming soon in a certain area, it is possible to set a competition among the vendors. Also, the AI could calculate the bonus to engage service providers undertaking their job.
- Improved Customer Experience. Machine learning can calculate what customers really expect to get when searching for information. Due to AI algorithms, they get better recommendations and suggestions.
Amazon is a gigantic retail and cloud software business. Its marketplace division serves 200 million 'prime now' members, who pay $139 annually. The company used to be the most profitable business in the world and possesses an immense database of its consumers.
How did Amazon manage to become a top company? What's the key to their secret? At the first point, Jeff Bezos, its CEO and founder, implemented a flight wheels model in the company's marketing. Its core idea is to cater to each customer so that they want to retain.
In terms of mechanics, a flywheel is designed to store rotational energy. Instead of wasting it by turning it on and off, it keeps the energy constant by distributing it to the other parts of the machine.
In the case of Amazon, it looks so: the company adds more and more incentives to keep buyers engaged. It provides free monthly e-books, unlimited video streaming, cloud storage and the fastest delivery in the world within the Amazon Prime package. This loyalty system motivates its customers to stay longer with the marketplace service and recommend it to their family and friends.
Even though Walmart and Target deliver the purchases absolutely for free, Amazon's shipment is preferable among consumers. Its advantage is that the marketplace brings a package to a door on the same day or even at the same hour. Its analytics guessed it right, that the fastest delivery is a value that customers will not mind being charged for. This feature is the second core concept, placing Amazon apart from its competitors.
Lastly, Amazon sells over 400 million products. The marketplace pretends to be as comfortable and fast as an offline store. Even more than the offline, because a buyer could purchase any good, whether it would be a home air conditioner or a chocolate bar. It became possible due to implementing AI in every step of its workflow. Warehouse management, supply chain and delivery, website recommendations and voice search are operating due to AI algorithms.
Amazon definitely has the most powerful tools for all the little computational processes involved in moving the packages through many suppliers, routes of transit and all the steps that a package goes through.
Mike Liebhold, a senior tech researcher at the Institute for the Future
The mentioned tools and calculations in Amazon were developed by the Supply Chain Optimization Technologies group. As comes from the name, it aims to adjust the retail process logistics. But what they really accomplish goes out of one's expectations. Their target is finding new marketing opportunities.
For instance, they managed to detect that consumers frequently abandon their grocery carts when bananas are sold out. They figured out that this mainly happened on Mondays. Hence, the defined relation indicated the day of the highest demand for bananas. This insight helped Amazon increase its profit and prevent customer churn.
The shipping service of Amazon is shaped into a network. It consists of warehouses and distribution centers located over the entire US country and overseas. It is performed by the following steps:
- The AI algorithm selects a certain good that will be in demand soon. It assigns the anticipated order to a fulfillment center.
- A worker in the fulfillment center carries out the order, collecting it into a package.
- After assigning to a pipeline, this package gets a label without specifying an address. It will be added there soon, once a purchase is confirmed.
- The package moves to a nearby distribution center, or hub, located within 20-50 miles from the final place. It will wait there until an online shopper clicks "buy".
- Once a client makes a purchase, the desired package will be delivered within a few hours.
The described process is called anticipatory shipping. It was invented and patented by Amazon in 2013.
The software engineering team of Amazon completed an algorithm to predict what goods their customers will buy soon. It considers their shopping habits, including a list of factors:
- history of previous purchases;
- items are added to the wishlist;
- time spent on the website;
- search queries and mouse clicks.
Except for the mentioned, the algorithm is sensitive to micro-economy changes. For example, a price surge for sunglasses in the area of winter resorts may indicate an increase in demand. In this case, the AI solution defines the model as well as the number of packages to be supplied. The sunglasses will wait in the truck or shipping hub until their future owners click the buy button.
Predictive analytics is a core of Amazon's retail business. However, it could hardly be possible without artificial intelligence. At least, we don't know any other powerful tool that could substitute it. But before deep learning and AI becomes a reality, a team needs to undertake lots of preparation work. Data collection and its management pre stand for developing the software.
When I talk to people about our journey from our old modeling to the new, I guess that about 40% of the time was actually spent in preparing the data. <..> “Features are really all about getting the data in the right place. Without really spending that time and effort, we would risk either getting poor or biased results because the data didn't properly represent our decision set. (Quote Source)
vice president of Traffic & Marketing Technology at Amazon.com
In our latest blog post, we shared more information regarding data collection. Reading it, you will learn more methods and efficient practices that might be used in your upcoming work. We hope it will help you organize your environment before moving to AI-powered infrastructure.
Amazon's approach to artificial intelligence spans more other fields than just delivery. It targets the recommendation system too. Due to statistical reports, this marketing initiative brings them over 30% of the revenue from the whole retail business.
Uber was called to live after its future founders faced the same problem: the customer experience of the taxi service had been unsatisfactory. Garrett Camp and Travis Kalanick both felt unhappy while waiting for a cab, losing much time and opportunities to meet with important partners. Their personal experience motivated them to enhance the supply service of the taxi industry.
The first step Uber took on its way was to enlarge the number of suppliers on the market. The more drivers they would have, the faster pick-ups they would manage to provide. The founders considered the business model of Airbnb, which became a new trend in the startup economy. This company offered to share the space with clients. If an owner has some more rooms, they can monetize them. With no need to apply for any agency or middle person, all the processes could be handled via the marketplace.
Garrett and Travis endeavored to create a network consisting of individual drivers who had an opportunity to pick up a passenger while moving to their work and returning back. The advantage was obvious: a person you gave a lift could split the fuel cost with you. Besides that, you will make some cash. Statistically, most Uber drivers are private individuals who provide the service after finishing their daily full-time job.
Uber needed to motivate drivers to spend more time on the app. They were expected to stay online longer to pick up more passengers. Without that, Uber wouldn't affect the supply in general. The solution they came up with was based on gamification. It was aimed to encourage the drivers to arrive at the passenger as soon as required.
Gamification in Uber is presented by two features:
- The heat map, that shows the area of the demand.
- The promotion, which is being paid for completing a quest.
The mentioned approach helped Uber implement its marketing idea to life: deliver the transportation service to a passenger faster than a traditional taxi.
The heat map is fully integrated with the AI. Its algorithm makes it possible to track the dynamics of passenger demand. The heatmap changes consistently as it correlates with the data received from external sources, like weather forecasts, concert events, airplane arrivals, etc.
Same as Amazon, Uber utilizes predictive analytics, empowered by AI. It works in the following way:
- The system calculates the highest demand in the area and displays it on the map in a real-time mode.
- Viewing it, a driver moves towards the shown place and waits for a new order.
- A driver gets an expected order and fulfills a job in a short while.
- After completing a trip, the system pays an earning to the driver, including an announced bonus.
The area on the map, covered with bonus offerings for drivers, is called a surge area. Its territory is colored with orange and red colors. This approach helps Uber encourage their drivers to come to the various locations as well as assists them in making more money.
Let's talk about the main problem of Uber. Despite the fact that they created the biggest transportation network in the world and changed the passengers' experience, getting a profit remains a critical point for their business. The passengers associate Uber with a taxi service providing fast pickups for an affordable fee. Because of small earnings, drivers leave the platform and Uber needs to spend more to acquire new suppliers.
Since 2011 Uber has been losing tremendous amounts of money. It spends more than it earns. But at the latest time, it adjusted its profit. You can view it on the below graph:
This adjustment could be explained from one side by serious cutting of the spending for general and administrative needs. On the other, they earned much more than in the previous years, since its revenue grew to 18.3 billion in 2021.
Uber's solution to adjust its revenue is also based on artificial intelligence. They implemented dynamic pricing, which implies setting individual pricing for every passenger. The algorithm predicts the best moment when the rider needs a service most of all and can pay a higher price.
Using AI, Uber calculates the surge and adds it to the base fare:
... pricing can vary from minute to minute. It can also pull in personal customer data and historical activity to determine how to price the ride. If your online behavior shows a pattern of going certain places and times, it may charge more to your account at those times (Forbes).
There are a few more circumstances when an AI algorithm could be set at a higher price:
- if a phone battery is going to die (in this case, riders rarely skip the app and wait for 15 minutes more);
- if the client's financial capacity allows them to pay more.
Predictive analytics empowered with AI is a core of Uber's transportation business. It helps calculate the demand and price for transportation services, processing the data in real-time. Consider using AI in marketing, especially if you want to build an uber-like application for your business.
Unlocking the Potential of OpenAI: How MarTech is Using AI-Powered Language Models for Marketing
OpenAI, an artificial intelligence research lab, was founded in 2015 by Elon Musk, Sam Altman and others. Its goal is to create and develop advanced AI models and technologies. They developed a range of AI products and tools that can be used across industries, including marketing and MarTech.
In 2020, OpenAI released GPT-3, an advanced language model trained on large internet datasets with 175 billion parameters. It performs various natural language processing tasks: language translation, question answering, and text summarization. It is available through an API and can be used to build a wide variety of applications, from chatbots and virtual assistants to content creation and customer service automation.
In December 2023, OpenAI took the internet by launching a free preview of ChatGPT, its new AI chatbot based on GPT-3.5 and trained with 1.5 billion parameters. It gathered over a million sign-ups in the first five days of the free preview. ChatGPT is developed for conversational language understanding and generation, making it an excellent tool for developing chatbots or virtual assistants.
Compared to ChatGPT, GPT-3 is a larger and more powerful language that can handle a wider range of tasks, but it may not be as optimized for conversational language as ChatGPT.
With the launch of ChatGPT, the demand for AI-powered solutions has skyrocketed. People immediately started testing the new technology, asking ChatGPT to answer various questions. Tech giants like Google, Microsoft, and Amazon have been expanding their AI capabilities and offering their customers a range of AI-powered products and services. And many MarTech companies are already considering using this technology in their products and services.
MarTech companies that use the OpenAI GPT and GPT-3 language models
So, let's see some examples of MarTech companies that are using OpenAI's GPT and GPT-3 language models:
- Persado: This AI-based messaging platform uses machine learning and natural language processing to create personalized marketing messages. They claim that using GPT-3 has resulted in more effective language and higher client engagement rates.
- Copy.ai: This MarTech startup uses GPT-3 to generate marketing copy for businesses. Their AI-powered copywriting tool can generate product descriptions, social media posts, and other marketing content.
- HubSpot: This popular marketing and sales platform, has integrated GPT-3 into its Conversations tool to help businesses engage with customers more effectively. The Conversations tool uses GPT-3 to analyze customer inquiries and generate real-time responses.
- Rasa:This is a conversational AI startup that uses GPT-3 to improve the natural language understanding of their chatbots. By integrating GPT-3 into their platform, Rasa's chatbots can understand and respond to complex customer queries with greater accuracy.
- Chatdesk: A customer service platform that uses GPT-3 to automate responding to customer inquiries. Using GPT-3, Chatdesk's chatbots can provide customers with more personalized and human-like responses, leading to higher customer satisfaction rates.
- PathFactory: This content engagement and marketing analytics platform uses GPT-3 to generate personalized content recommendations for website visitors.
- Acrolinx: This content optimization platform uses GPT-3 to analyze and optimize marketing copy for SEO and readability.
How GPT-3 Helps MarTech to Maximize Marketing Success
GPT-3 can be used to generate high-quality and engaging content for their clients. For example, a content marketing agency might use GPT-3 to generate blog posts, social media updates, and other content that is optimized for SEO and designed to engage its target audience.
GPT-3 can be used to automate various marketing tasks, such as email marketing campaigns, social media postings, and lead generation. By using GPT-3 to automate these tasks, marketers can save time and effort and focus on more strategic tasks.
Chatbots and conversational marketing
GPT and GPT-3 can be used to create chatbots and conversational agents that can interact with customers and provide personalized support and assistance. For example, a customer service chatbot might use GPT-3 to understand and respond to customer inquiries, providing quick and accurate solutions to their problems.
MarTech companies use GPT and GPT-3 to create highly personalized email campaigns for each subscriber. For example, an email marketing platform might use GPT-3 to generate personalized subject lines, body copy, and calls to action that are designed to increase open rates, click-through rates, and conversions.
GPT-3 can be used to analyze large amounts of marketing data, such as social media analytics and customer feedback. By using GPT-3 to analyze this data, marketers can gain valuable insights into customer behavior and preferences, allowing them to make more informed marketing decisions. For example, an e-commerce platform might use GPT-3 to analyze customer data and generate product recommendations that are tailored to each individual's preferences and purchasing history.
By leveraging the power of GPT-3, MarTech companies can streamline their operations, save time and money, and ultimately drive more effective marketing campaigns. It's no surprise that so many large companies are eager to jump on the AI bandwagon.
- Properly collect the data regarding your users.
- Constantly track behavior of your clients.
- Find out what features you would like to automate.
- Implement machine learning in your company.
- Use external APIs to get more meaningful data.
To start using AI in your company, you could apply out-of-box solutions. They will essentially save your time building a machine learning platform from scratch. Except for that, they will bring the best practices to your team and increase your capacity in artificial intelligence.
Amazon Machine Learning
You could use Amazon machine learning to analyze customer feedback in emails, product reviews, forums or phone transcripts and make useful recommendations to your product or service teams.
Getting started with Amazon machine learning is easy. Amazon Machine Learning's visual tools help you preview the data to ensure quality, and the wizards and APIs will guide you through creating machine learning models. Once the models are built, you could use Amazon's machine learning tools to evaluate and refine them. After that, they will be ready to generate predictions. You could decide whether you want to get them on-demand or in real-time. With Amazon Machine Learning, you can create models from large datasets and generate many predictions. There's no upfront cost, and you pay only for used resources.
- Optimized Prime: How AI And Anticipation Power Amazon's 1-Hour Deliveries, by Alina Selyukh.
- Predicting The Future Of Demand: How Amazon Is Reinventing Forecasting With Machine Learning // Forbes, December 3, 2021.
- Method and System For Anticipatory Package Shipping (Patent of Amazon Technologies, Inc.).
- Uber Revenue and Usage Statistics (2022), by Mansoor Iqbal.