The technology that is available today and the next step
Summary. The CMOs should be familiar with AI and its many applications. Using this article, marketing executives can better understand AI and what it is capable of.
Marketing can benefit from artificial intelligence. This includes understanding customer needs and matching them with the right products or services. It also involves convincing customers to make a purchase. AI is a powerful tool that can improve these abilities. According to a 2018 McKinsey report, AI has the greatest impact on marketing in terms of understanding customer needs.
In an August 2019 survey, the American Marketing Association revealed that AI adoption had risen by 27% in the last year and quarter. Deloitte’s 2020 global study of early AI adopters found that 3 out 5 AI goals are marketing-related. The goals of AI include improving existing products and services, developing new ones, and improving customer relationships.
AI-Powered marketing: series reprint
AI has already made its way into the marketing world. AI is expected to grow to be a major part of marketing over the coming years. The framework will help CMOs classify their current AI projects, and to plan for future ones.
Today’s AI
AI has been used to assist in a variety of tasks, including digital advertising (also known as “programmatic buying”) and improving forecast accuracy (such as sales forecasts).
Marketing professionals who are using AI-based applications that have been well-established
Inbound call analysis, routing, and… Call analysis, routing, and …
AI also helps firms to target advertisements at the right time, such as when customers are in “the consideration phase” of their customer journey. This will help them narrow their search. This is what we see at Wayfair.com, an online furniture retailer, that uses AI to identify which customers are likely to be most persuadable and then chooses products to display to them based on their browsing history. And AI-enabled bots from companies such as Vee24 can help marketers understand customers’ needs, increase their engagement in a search, nudge them in a desired direction (say, to a specific web page), and if needed, connect them to a human sales agent by chat, phone, video, or even “co-browsing”–allowing an agent to help the customer navigate a shared screen.
AI automates the sale process by using detailed information about the individual, like geolocation data, in real-time, to offer highly personalized products or services. AI bots can also offer testimonials to customers after they have filled their shopping baskets. You can say, for example: “Great Purchase!” James, from Vermont, purchased the same mattress. These initiatives have the potential to increase conversions fivefold.
AI-enabled agents, like those of Amelia (formerly IPsoft) and Interactions, are on hand 24/7 to answer customer requests. AI-enabled agents are more suited for handling fluctuating volumes of service than humans. The AI can coach the agents to better satisfy customers or recommend a manager’s intervention.
The Framework
Marketing AI is classified based on two factors: its intelligence and whether it’s a stand-alone product or part of a bigger platform. How they are used inside an app determines their classification.
First, let’s examine two different types.
Automation of Tasks
They are programmed to carry out repetitive, structured work that requires a minimal level of intelligence. They cannot deal with complex problems, such as customer needs. For example, a system that sends out an email to welcome every new client is not able to handle this. They need help understanding the intent of a customer, giving customized answers, or even learning with time.
The field of machine learning is growing.
The machine-learning applications are limited and need a lot of data for training.
Now let’s compare standalone AI with integrated AI.
Stand-alone applications.
AI should be seen as a distinct program. AI is separate from other channels, such as those used by customers to find out about a product, buy it, or get support, and the channels employees use to sell, market, or maintain these products. To access AI, customers or employees must go outside of these channels.
Behr, a company that makes paints, has developed a color-discovery app. It uses IBM Watson to analyze tone and natural language to provide a range of personalized color suggestions based on the mood that customers want to create in their room. It allows for a Home Depot connection.
Applications that are integrated.
The AI apps are hidden within the existing system and are not visible to customers or salespeople. Netflix has offered video recommendations for more than a decade. These choices appear in the menu of the website when users visit.
Salesforce’s Sales Cloud Einstein includes several AI-based features. Cogito is an AI vendor that sells AI for call centers to train salespeople. They integrated their software with Salesforce CRM.
Our framework is divided into four quadrants, each of which is made up of two different types of intelligence and two distinct types of structures: standalone apps for Machine Learning, integrated apps, task automation apps, or integrated apps.
Understanding the quadrants in which applications are classified can assist marketers with planning and sequencing new uses.
A Stepped Approach
We believe marketers will see more value in machine-learning apps. Simple task automation systems and rule-based software can enhance well-structured business processes, and offer a good return on investment.
Stand-alone apps have their limitations but they are useful when integration is difficult or impossible. Many companies already move in that direction. In the 2020 Deloitte AI Survey, 74% of global AI executives stated that “AI will be integrated into enterprise apps in three years.” “
Getting Started
You can begin by building or buying simple rules-based apps if your company has little AI experience. The first app isn’t aimed at the customer but rather guides agents in providing service.
Once companies have acquired basic AI skills and collected a lot of customer and market data, they can then move on to machine learning. Stitch Fix’s AI allows stylists to create offers for customers using the items kept or returned and the feedback. These models became even more effective when customers were asked to choose from Style Shuffle pictures, which provided a whole new set of data.
Marketers should be constantly looking for new sources of data, such as internal transactions, external suppliers, or even potential acquisitions because most AI applications and machine learning require large amounts of high-quality data. Consider the machine-learning-based pricing model that the charter jet firm XO used to increase its EBITDA by 5%: The key was to tap external sources for data on the supply of private jets and on factors that affect demand, such as major events, the macro economy, seasonal activity, and the weather. The data XO used is public, but it is a good idea, whenever possible, to seek out proprietary sources, as models using public data are easily copied by competitors.
As marketing AI becomes more advanced, many companies automate certain decisions. AI can provide recommendations to a company when it makes a choice. AI can suggest movies or strategies to a marketing executive.
We believe that marketing AI will have the greatest impact on this area.
Challenges and Risks
It can be challenging to integrate even the most basic AI applications. Task-automation AI, for example, is not as sophisticated and can also be hard to customize to a specific workflow. AI expertise is also required by companies.
As companies implement sophisticated applications, other considerations may arise. Procter & Gamble’s Olay Skin Advisor offers a great example. Olay Skin Advisor integrates with Olay.com, an online loyalty and shopping platform. This has led to improved bounce rates, conversion rates, and average basket size in some regions. The brand is unable to offer a seamless AI-assisted experience for its customers.
Companies must always consider their customer’s best interests. The CMO can make sure that their customers’ data and money are used responsibly by creating ethics and privacy panels that include legal and marketing experts. It is particularly important to those who use algorithms and customer data that may be biased.
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Marketers should develop strategies to take advantage of AI’s current functionality as well as its future.
