As a marketer, you‘ve likely noticed data’s importance in decision-making and strategizing. You’re also probably tired of being bogged down with manually collecting and reporting data.
Data science is integral to the marketing world, but time is money, and the time you spend working with large datasets could go toward performing other tasks, such as content marketing.
Fortunately, artificial intelligence can blend with data science in many ways to make your job as a marketer easier without neglecting essential practices like data reporting.
We will explore the connection between data science and AI and tools you can use as a marketer regardless of your skill level in data analysis.
Difference Between Data Science and Artificial Intelligence
Data science is an umbrella term that describes analyzing, managing, and interpreting large amounts of data to inform decision-making.
Artificial intelligence is a field in which a computer or a robot completes tasks that typically require human intelligence and discernment.
Though data science and artificial intelligence are different fields, they often combine to streamline tasks, improve efficiency, solve problems, or increase productivity.
For instance, AI often depends on data scientists to study, clean, and feed data so the machine can learn and improve. However, data science is less dependent on AI since the science can analyze and explain datasets on its own.
However, AI can extract information from data in a way that isn’t possible with typical data science techniques. This is especially the case with rich data types like video or large volumes of data.
Examples of AI in Data Science
Below are some examples of AI in the world of data science.
AI in Survey Drafting
It’s common to use surveys to gather data and information about your audience and consumers, and AI is often leveraged to automate many aspects of the survey process.
AI surveys eliminate the need for human intervention in tasks such as data entry and survey report analysis.
Leveraging AI with survey drafting and execution allows organizations to automatically collect responses from various channels like online forms and chatbots.
From there, AI can categorize the information based on predetermined criteria, analyze patterns and trends, and generate a report based on its findings quicker than if done manually.
AI in Data Collection
One way AI typically works in data collection is web scraping, which involves using automated bots or scripts to extract data from websites. AI allows these bots to quickly and efficiently pinpoint patterns and insights in the data.
In turn, organizations can make data-driven decisions about their products, services, or marketing strategies.
AI data collection also helps deliver a more personalized user experience on a website. AI can collect user behavior and preferences data to recommend products or content tailored to each visitor.
Personalized experiences improve the likelihood of conversion and engagement. This process is prevalent and helpful to e-commerce websites.
Many e-commerce sites use AI to collect and analyze data on customer behavior and use that information to provide personalized product recommendations or targeted campaigns.
Data Tools that Use AI
Here are some helpful AI tools for collecting and analyzing data you can leverage in future projects.
1. Tableau
This analytics and data visualization tool allows users to interact with their data and is an excellent platform if you have little to no coding or data analyzing experience.
With Tableau, you can create and share reports across desktop and mobile platforms.
Tableau also supports complex computations, dashboarding, and data blending, which combines data from various sources into one helpful dataset.
2. GitHub Copilot
GitHub Copilot is a programming assistant that provides autocomplete suggestions to coders. Developers can use GitHub Copilot while writing code or using basic natural language prompts that tell Copilot what they want it to do.
The data tool can complete many coding tasks and is proficient in many coding languages, such as Python, Go, and JavaScript. Even better, you don’t have to know how to code to use it.
3. ChatSpot
ChatSpot is HubSpot’s conversational CRM bot that sales, marketing, and service professionals can connect to HubSpot to boost productivity and streamline different processes — data reporting.
Professionals can use chat-based commands to direct ChatSpot to pull reports, create new segments, and manage leads.
4. Microsoft Power BI
Microsoft’s business intelligence platform lets users sort through and visualize data for insights. Users can import data from almost any source and build dashboards and reports immediately.
Furthermore, Microsoft Power BI allows users to build their own machine-learning models and use other AI-powered features to analyze data.
5. Akkio
Akkio’s business analytics and forecasting tools help users analyze their data and predict potential outcomes. The tool is meant for beginners and is especially useful for sales, marketing, and predictive analysis.
With Akkio, you can upload your dataset and select the variable you want to predict, which helps Akkio build a neural network around that variable.
How to Pick the Right Tools
Finding the right AI tool to help you collect, manage, and analyze data can be challenging, but it isn’t impossible. You must assess your skills and preferences to find the best tool for your next data science endeavor.
Ask yourself what part of the data process you are trying to streamline or improve. How proficient are you in reporting or coding? What is your skill level?
For example, if you‘re familiar with programming languages like Python, you’ll want to find tools that are compatible with this language. If you’re currently using tools like Microsoft or HubSpot, find compatible or built-in tools.
You don’t have to be a scientist or a masterful coder to use AI in your marketing or sales data projects. All you need is the right tools that fit your knowledge and needs.
