In 2022, we saw AI and machine learning become more and more commercialized–think ChatGPT from OpenAI for generating funny conversations, and Lensa AI and DALL-E for creating art. These apps can be highly entertaining, and also subject to scrutiny regarding ethics and regulation. But needless to say, the application of machine learning is changing how we interact, create, and use data, even when we’re not aware of it. In this article, we’ll talk about a few ways in which you can use AI and data science in the field of marketing, providing businesses with new ways to connect with customers and improve their marketing efforts. Using AI and data science in marketing offers many benefits, including improved customer targeting and personalization, predictive analytics, and sentiment analysis for understanding customer feedback.
Data science canvases: faster and more collaborative
Foundational to any complex technique is the tool you’re using. At Einblick, we’ve combined the benefits of a canvas platform with the functionality to perform data science tasks, from data ingestion to data cleaning to exploratory data analysis to model building. A tool like Einblick will help you and your team to implement the tactics outlined below, with its visual paradigm and focus on collaboration.
In an industry like marketing, your edge is everything, and that depends on expertise about the market, the industry, the customers, and data science techniques. In order to bring together all of this knowledge, which different members of your team or organization may specialize in, you need to be able to communicate and work together easily. Einblick empowers you to:
- Collaborate live in the platform
- Share canvases and dashboards by toggling permission settings
- Create colored data zones and bookmarks to organize your canvas
- Use annotations to take notes and explain processes or domain knowledge
On the data science front, Einblick features:
- Profiler cell creates a quick summary of your dataset
- Chart cell, which allows you to create visualizations quickly
- SQL and Python, allowing you to switch between languages as needed
- Various data connectors, such as AWS, BigQuery, Databricks, and Snowflake (as well as file upload)
- AutoML, based on 6 years of research from MIT and Brown, will train and test several ML pipelines, which you can then utilize via our executor, while our explainer dives into feature significance
- Progressive computation engine to speed up ML tasks
You can read our documentation to learn more about all of EInblick’s features. Check out this canvas we built for targeting customers in the market for a car. You can try out our SaaS offering for free.
Personalized marketing: using AI to target customers
Using AI in marketing improves customer targeting and personalization, which is one of its key benefits. Marketers can better understand their target audience by leveraging machine learning algorithms and large amounts of data to tailor their messaging and advertising accordingly. They can achieve an effective campaign and a higher conversion rate with a targeted approach.
For example, a retail company might use AI to analyze customer purchase history and browsing behavior in order to identify specific products or categories that individual customers are interested in. The company could then use this information to send personalized product recommendations or offers to those customers, increasing the likelihood of a purchase or subscription. Think about those scrolling bars of suggested products whenever you’re shopping, or suggestions listed on sites like YouTube and Netflix when you’ve finished watching a show or movie.
AI can create personalized customer experiences on a website or app. For instance, a fashion retailer might use AI to send automated emails about items left in your shopping cart, or if you’ve been absent for a while, they may send you a promotional code. This personalized approach can lead to higher engagement and loyalty from customers. Using AI in customer targeting and personalization can have a significant impact on marketing efforts. By tailoring messaging and advertising to individual customers, businesses can create more effective campaigns that are more likely to lead to conversions.
Predictive analytics: anticipating trends and demand with AI
Predictive analytics largely asks the question, “what will happen?” This kind of data analysis can use machine learning algorithms to identify patterns and trends in data, and to make predictions about future events or outcomes. They often use AI in predictive analytics to automate analyzing large amounts of data and to identify trends and patterns that may not be apparent through manual inspection of the data.
One of the key benefits of using predictive analytics in business is its ability to anticipate customer behavior and trends. By analyzing large amounts of customer data, businesses can identify Marketing strategies then can be informed by patterns and trends in customer behavior and preferences. By predicting trends and customer demands, businesses can be more proactive and responsive to changing customer needs and preferences.
When used in conjunction with prescriptive analytics, predictive analytics can help marketers respond methodically according to their predictions. You can read more about predictive vs. prescriptive analytics on our blog.
You can also see how we predicted customer churn using Einblick:
Sentiment analysis: understanding customer feedback with AI
Sentiment analysis is a part of natural language processing (NLP), which aims to identify and quantify the emotional content of text-based data, such as customer feedback, reviews, comments, and social media posts. With popular apps like Yelp and OpenTable, for example, in the restaurant industry, customers write countless reviews everyday. That’s a lot of information that could be used to improve your product or service. Sentiment analysis is one way to start unlocking some of that information, and using it to your advantage.
In business, sentiment analysis can help identify customer pain points and areas for improvement. Businesses can identify common complaints and issues customers have with their products and services by analyzing customer interactions and feedback. Because of this information, products, services, and customer experiences can be improved.
Campaign Optimization: Measuring the Impact of AI on Marketing Efforts
An optimized marketing campaign uses data and analytics to improve the effectiveness and efficiency of marketing. The ability to measure the impact of marketing efforts is one of the key advantages of AI in campaign optimization. Analyzing customer data and engagement metrics can help businesses improve and optimize their marketing campaigns. For example, what’s the conversion rate historically for similar campaigns? Which blog posts, landing pages, or social media posts converted the most people? Businesses can then create more effective marketing campaigns by responding proactively to customer behavior.
Businesses can optimize their marketing campaigns using AI in a variety of ways. To identify patterns and trends in customer behavior and preferences, a retail company might use AI to analyze customer data and engagement metrics. Using these insights, the company can adjust its marketing strategies and tactics in order to create more personalized and effective campaigns.
- AI and machine learning are becoming increasingly a part of everyday life.
- In the marketing industry, you can use personalized marketing, predictive analytics, sentiment analysis, and campaign optimization to level-up your strategy.
- In order to implement any of these strategies, you and your team need the right tool to succeed.
Einblick is an agile data science platform that provides data scientists with a collaborative workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick customers include Cisco, DARPA, Fuji, NetApp and USDA. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.