As the marketing industry keeps getting bigger and better, of course with the help of innovative technological software and applications to improve how we conduct our day-to-day business, marketers are faced with the reality of learning new ways to become successful within their workspace and businesses.

According to FortuneBusinessInsights.com statistics, companies are increasingly demanding real-time operational insights, and the business intelligence market is predicted to grow with a compound annual growth rate (CAGR) of 8.7% over the next seven years. By 2028, the market is expected to be worth a whopping $43.03 billion.

We focus on the Business Intelligence (BI) and Artificial Intelligence (AI) tools that will shape how we conduct our daily business processes, and actively support your marketing strategy.

What is BI?

Business intelligence (BI) is the most powerful way to understand critical metrics around your business.
Commonly known as BI, it’s a marketing process through which marketing professionals use data and analytics to make better decisions about their marketing initiatives.

The goal of BI marketing is to improve marketing returns of interest (ROI) by using data to inform marketing strategies, plans, and tactics. However, reports claim that 74% of employees feel overwhelmed or unhappy when working with data. The reason why employees feel overwhelmed is that 97% remain unused by many organizations.

Common Ways To Use BI In Marketing:

There are a number of different ways that BI can be used in marketing.

• One common use is for customer segmentation, which allows businesses to be 64% likely to understand their customer’s complaints and challenges. By analyzing customer data, marketers can identify groups of customers with similar characteristics. This information can then be used to develop targeted marketing campaigns that are more likely to be successful with each specific group.
• Another common use for BI in marketing is for media mix modeling, commonly known as marketing mix modeling (MMM). This is a process of using data to analyze the effectiveness of different marketing channels in order to optimize the media mix for future campaigns. This kind of analysis can help marketers understand which channels are most effective at reaching customers, driving brand awareness, or generating leads and sales.
• BI can also be used for campaign tracking and attribution. This helps marketers understand which touch points along the customer journey are most important in influencing a sale. This information can then be used to optimize future campaigns. Now that you understand what is BI, we jump into how AI plays a role in BI.

How Does AI Play A Role In BI?

Artificial intelligence (AI) is being used right now to power business intelligence analytics, which in turn allows marketers to achieve better results in campaigns. Furthermore, data collected through business intelligence can be used to drive a company’s overall metrics.

The AI software industry is forecast to total $62.5 billion in 2022, an increase of 21.3% from 2021 currently.

So now that we know what business intelligence is, AI-powered business intelligence analytics can help businesses collect valuable data along the way.

The data collected can be used to improve a company’s overall metrics, such as customer satisfaction or profitability.

What Is Data Analytics In Marketing?

Data analytics is the practice of collecting, manipulating, and analyzing data to extract valuable insights. Marketing is one of the many fields that have been transformed by data analytics.

Chief marketing officers (CMOs) spend around 6.5% of their marketing budgets on analytics because 28% of marketers say their marketing analytics data helps them to measure the performance of their campaigns more effectively.

 By harnessing the power of data, marketers are able to better understand customer behavior and preferences, identify new opportunities, and optimize their campaigns for maximum ROI.

 While data analytics can be used to support a wide variety of marketing activities, it is perhaps most commonly used for marketing research, customer segmentation, and target marketing. As the field of data analytics continues to evolve, there is no doubt that its impact on digital marketing will grow faster.

 Now that we have explained the fundamentals of BI and AI, let us dig deeper into the tools that may be important to use for Business Intelligence.

Three Tools Used For Business Intelligence

Tableau

In recent years, data analytics has become an increasingly important tool for businesses and organizations of all kinds. 

Tableau is one of the most popular Business Intelligence tools on the market. It is loved by users for its ease of use, rich set of features, and a large ecosystem of support. 

Furthermore, Tableau can connect to almost any data source you can imagine, handles larger datasets with ease, and has a great community behind it.

Microsoft Power BI

Microsoft Power BI is a Business Intelligence tool that enables users to collect, visualize and analyze data. It provides a self-service environment where users can create their own reports and dashboards without the need for IT support. 

PowerBI is easy to use and quick to deploy, making it ideal for small and medium businesses. 

It also integrates with other Microsoft products, such as Excel and SQL Server, making it a powerful tool for enterprise users.

Sisense

Sisense is a Business Intelligence tool that helps users to visualize and interact with data. It’s designed to be easy to use, even for complex data sets, and it offers a range of features that make it popular with businesses of all sizes.

• First, Sisense offers an intuitive interface that makes it easy to create beautiful visualizations. 
• Second, it’s designed to handle large data sets with ease, making it ideal for businesses that need to work with lots of data. 
• Finally, Sisense offers a range of connectivity options, so it can easily be integrated into existing systems.

How Can We Use These Tools To Achieve Our Marketing Goals?

1.By Clustering Algorithms To Customer Segments For BI.

You are probably asking yourself these questions as a marketer: How do businesses know which products or services to offer their customers? How do they decide what kind of messaging will resonate most? How can they determine which channels will be most effective for reaching their target audiences? The answer to all of these questions lies in data. 

According to Study.com, clustering algorithms are a type of business intelligence tool that can be used to segment customers based on certain characteristics. Businesses need to gather as much information as possible about their customers in order to make informed decisions about how to best serve them. 

Now with the vast amount of data that is available, it can be difficult to know where to start. This is where clustering algorithms come in. By grouping together customers with similar characteristics, businesses can better understand who their target audience is and what they are looking for. 

In turn, this allows businesses to tailor their products, services, and marketing messages to better meet the needs of their customers. As a result, clustering algorithms can be an invaluable tool for businesses that are looking to gain a competitive edge.

2.Online vs Offline Company Sales Data Analytics

Sales analytics is the process of collecting and analyzing data to improve sales performance. Sales analytics can be used to track and measure a variety of metrics, including customer acquisition, conversion rates, customer lifetime value, and more. 

Offline sales data has traditionally been collected and analyzed manually, while online sales data is often collected automatically through tracking software. There are advantages and disadvantages to both methods.

Difference Between Online and Offline Sales Data Analytics

• Offline sales data can be more accurate than online data since it is not subject to the same potential for errors. However, it can be more time-consuming and expensive to collect and analyze offline data. 
• Online data, on the other hand, can be collected automatically and in real time, making it easier and faster to analyze. However, online data can be less accurate than offline data, as it can be subject to errors in tracking & reporting. 
• Both offline and online sales data have their advantages and disadvantages, but both can be used to improve sales performance.
• According to ConversionSciences.com, online data is defined as data that is collected through interactions that happen on digital channels such as website clicks, social media engagement, and online transactions. 
• Offline data is often thought of as surveys or information that is gathered through retail store locations or other places where consumers are not engaging with a company digitally.

In conclusion, online data has the benefit of being very granular and easy to track. However, it can be difficult to draw conclusions about the cause and effect because there are often so many variables at play. 

For example, if a user visits a website and then makes a purchase, it’s hard to know whether it was the result of an effective ad campaign, word-of-mouth marketing, or simply because they were in the market for that product. Offline data, on the other hand, can provide more context around consumer behavior. For example, if a customer fills out a survey about their in-store experience, companies can use this feedback to improve the quality of their service.

Closing thoughts

It is now more important than ever for marketers to be competent in the latest technology, in order to remain ahead of the curve and keep up with the competition. The use of innovative software and applications has greatly improved how we conduct our day-to-day business, and has also provided us with new ways to become successful in our work. 

By understanding and utilizing these tools, we are able to gain a better insight into our customers, as well as create more targeted and effective marketing campaigns. In addition, we are also able to save time and money by automating various tasks that would otherwise be carried out manually. As the marketing industry continues to grow and evolve, so too must marketers themselves in order to remain successful.