One Thing Marketers Should Know About Using Artificial Intelligence Or Machine Learning
The introduction of advanced technologies, such as Artificial Intelligence (AI) and Machine Learning (ML), has revolutionized how we perform tasks. These technologies are beneficial for many fields including professional and personal life. Many fields can use AI and ML for their benefit.
Marketing is a field that can use AI and ML to open new ways for businesses to generate more revenue and ensure optimum customer experience in multiple ways.
Marketing data management is also one of them. Marketing data management is key if digital marketers want to get great results from their marketing strategies. Initially, marketing data management was performed manually, but AI and ML have automated this process and made it far easier.
We have put together details about marketing data management, the role of AI and ML in marketing data management, and the challenges that will cross your path while using AI and ML in marketing data management. Read on to learn more.
What is Marketing Data Management?
Before we discuss marketing data management, it is necessary to understand the core purpose of marketing, which is identifying valuable customers. Marketing data management is a process that involves the collection and sorting of data. Additionally, it includes advanced market research and competitive intelligence.
Many businesses may understand that the IT department should carry out this process per the definition. However, it is the core of marketing to determine the best customers, their shopping patterns, and the products or services they want to buy. Hence, the data at the heart of the marketing field is managed through the process named 'Marketing Data Management'.
Questions Answered by Marketing Data Management
Marketing data management answers a wide variety of questions that are essential to the marketing field. For example, what is the ideal persona of your best customers? What is the exact number of customers you classify as the best ones? What kind of additional information do you have about your customers? Is there any defined shopping pattern of your ideal customers according to various situations and events? What is the scale of the market in which your business works? How many prospects are there? Many other questions like the aforementioned ones are answered using marketing data management.
Handling of Data
It is worth mentioning that marketing data management collects, handles, and sorts tons of data, which is quite difficult to perform manually. The only way it can be performed easily, and the defined goals are achieved based on the data resultantly, is by using advanced analytics. These advanced analytics require AI and ML to work in tandem and deliver the desired results. In the coming sections, we will discuss the working of Artificial Intelligence and Machine Learning in this process.
Why is AI Needed for Marketing Data Management?
The modern marketing ecosystem relies on data-driven advertising strategies that assure a considerable ROI. However, the main issue with a data-driven marketing system is the inclusion of large arrays of data from various sources. This huge noisy data is nearly impossible to classify and categorize manually, so you won't get valuable insights from this much information. Hence, it requires an efficient technology that can derive valuable insights from all the tools used for analytical purposes. AI (Artificial Intelligence) is the only option to obtain the required data from analytical packages, marketing software services, and datasets.
Apparent Use of AI
AI provides the solution to gather the required information from various platforms and tools with significantly less time and effort. This process allows businesses to better use consumer-related data, implement better marketing strategies, and know the platforms that should be equipped with effective techniques. There are many online tools that are using AI to help users like AI paraphraser, AI plagiarism checker, and many more.
AI-backed Algorithm to get Data Insights
The collection of raw data is performed using various platforms, such as email databases, web servers, and CRM (Customer Relationship Management) solutions. This data is processed using multiple analytical services. A clean datasheet is derived from the processed data, which is then analyzed using multiple AI-backed algorithms to get insights necessary to make decisions.
Implementation of AI-Backed Solution for Optimum CX
Marketing is swiftly moving towards a personalized experience for customers. AI-backed solutions are playing a key role in setting this trend. These solutions aim to obtain relevant data and present products and services for marketing in a way liked by individual consumers. This process not only ensures personalized marketing but also ensures a customized shopping experience for individual consumers.
How Does ML Play its Role in Marketing Data Management?
Machine Learning (ML) is the ability of machines to perceive data through various sources and act accordingly. Simply put, machine learning enables machines to mimic human reactions in various situations. This technology also uses information obtained from marketing data management to strengthen the relationship of businesses with their consumers.
The objective of using machine learning is to ensure customer-centric content marketing and other types of advertising strategies to achieve various goals. Here is how it plays its role in marketing data management.
The Use of Chatbots and Advanced Solutions
It goes without saying that the modern marketing and e-commerce ecosystem is taking the help of various smart solutions to ensure a great experience for customers who shop online. Chatbots have become a popular way to communicate with consumers and answer their queries in real-time. These chatbots take the help of machine learning and deep learning libraries to understand the queries and answer them accordingly in real-time. Many chatbots also provide audio-based responses to ensure customer satisfaction.
Voice Commands
Another way ML (Machine Learning) is helping businesses enhance their relationships with consumers is the easy perception of voice commands. For example, many e-commerce vendors offer shopping and navigation through voice-based commands to ensure a simpler and easier online shopping experience. Therefore, voice-based e-commerce, or simply voice commerce, needs ML to act according to users' voice commands.
Various Other Use Cases
The use of ML is not limited to the use of Chatbots and Voice Commerce in marketing data management. The most important use of ML is perceiving the information from the sorted data and instructing machines to mimic the problem-solving methods of humans. It perceives valuable insights and analyzes human actions in various use cases to work accordingly. For instance, it performs audience segmentation to provide consumer-centric content through ads, email campaigns, blogs, and social media.
Success with the Use of ML and AI in Marketing
We have discussed the role of AI and ML in marketing data management and their use cases in detail to help you understand how they have become a need in the marketing field. It is time to discuss how these advanced technologies can work wonders for businesses and enable marketers to open new ways for the progress of businesses. Here we will discuss how AI and ML in marketing data management assure success in marketing and related processes.
Marketing Data Management
Using AI and ML in marketing will help you get great results from marketing data management. Collecting, handling, and processing marketing data will allow you to get valuable insights and make decisions accordingly. For instance, you will be able to identify your best customers, define your best customer, and understand which product will likely be purchased by customers who resonate with a particular persona. Additionally, you will be able to understand the techniques to reach out to prospects belonging to various personas because of the integration of AI and ML in the marketing data management process.
Customer Intent
Customer intent is also a particular type of data required by various businesses to understand customers' buying intent and their requirements. This type of data included valuable insights from sales and marketing information from the industry. It takes account of actions performed by various customers interested in a specific kind of product or service. For example, customer intent data observe access to online content, registration for events, social media engagement, and view of competitors by ideal consumers. Building an algorithm that uses AI and ML to obtain such data and make decisions accordingly will enable you to work with the marketing and sales teams to work in tandem with each other. Each team can get an advantage from the data produced by the other team.
Opportunity and Purchase Prediction
The use of AI and ML can help businesses understand the opportunity of cross-selling or up-selling products by understanding the purchase pattern of consumers. The Algorithm developed for this purpose will take help from marketing data management to understand the requirements of individual consumers, analyze their shopping activity, and predict their possible purchase in combination with a particular product. It will also keep track of various situations and seasons to predict up-selling opportunities. Hence, businesses can use AI and ML to generate more revenue, sell more products or services, and provide users with a customized shopping experience, leading to a better CX.
Challenges Likely to Come Your Way with the Use of AI and ML
Like the opportunities to pave the way for the success of businesses, the use of AI and ML may become the reason for a few challenges crossing your way. Therefore, it is necessary to be aware of these challenges to make yourself capable of dealing with these issues and keep your business progressing. Further details are given below:
Multiple Options
You need to use proper tools that are particularly developed for a specific purpose to achieve desired results. However, the overwhelming number of AI-backed services and tools may confuse you about selecting the tools for a particular purpose. Therefore, it is essential to be fully aware of your requirements and the capabilities of a specific tool. For instance, if you want a tool to help you perform market segmentation, make sure you choose the right tool or service.
Privacy
The data you obtain regarding your prospects' purchase history and actions can be highly valuable if you use it the right way. However, it will become pretty creepy if you try to reach out to your prospects without thinking about the consequences. Furthermore, your targeted audience may think that you are offending their privacy. Therefore, ensure you are vigilant while responding to the inquiries made on your competitors' sites.
Miscalculations
It should be understood that the machine learning algorithm used to generate predictions based on the data obtained through AI is not collected in just a quarter. It will be a false approach, and you may end up annoying your customers because of false predictions and enduring losses because of stocked items based on such predictions. If you want to ensure proper predictions, it is essential to collect data in 18 or 24 months. If this much data is unavailable, stay away from early predictions and don't articulate any patterns.
Changes Impact Forecasting Ability
Another thing that should be considered while relying on predictions for stock products and forecasts of purchase patterns is the change in your business. It could be anything, such as adding new items to your portfolio, increasing prices, and changing marketing strategies. Make sure you also take account of the allocated budget for various activities from quarter to quarter to ensure accurate predictions.
Change of Patterns from Region to Region
Business expansion is always exciting, and working on similar predictions and forecasts while entering a new region and targeting a new audience may sound tempting, but it can lead to serious losses. There is no need to forecast, even if you are entering a new region with similar products and prices. Instead, move the task of evaluating good customers to the sales team.
End Words!
Long-term marketing success relies on a proper understanding of data and analytics. However, manually getting valuable insights from the overwhelming amount of marketing-related data is nearly impossible. You need advanced technologies like AI and ML to help you obtain, process, and analyze data to make important decisions. These decisions will help you come up with effective marketing techniques and ensure optimum customer experience (CX).
We have discussed the role of AI and ML in detail to help marketers understand how these technologies can be helpful for them in sorting data, understanding customer intent, and ensuring accurate forecasts regarding the purchase patterns of the targeted audience. Hopefully, you will get a lot of valuable takeaways from this blog. We wish you luck with data-driven marketing!
