Business intelligence is the most powerful way to understand the key business critical metrics surrounding your business, both past and present. Understanding the focal points of your business intelligence and what data analytics you collect make up your BI is critical, because these metrics allow you to take big leaps forward in your business.
Thus, marketers in 2021 are fully focused on business data and analytics to understand what their marketing campaigns are all about, and how to optimize them to produce results. Artificial intelligence is being used today to empower the same business intelligence analytics that allow marketers to achieve better results in campaigns, as well as collect valuable data along the way that can drive overall business metrics.
Top 3 Ways to Use Ai in Business Intelligence for Marketing Success in Your Organization
What Is Business Intelligence For Marketing?
Business intelligence tools for marketing are data-driven bullet points that help marketing professionals execute higher ROI and more efficient campaigns. Tools like data visualization and AI models can help marketers predict future results, optimize current social media campaigns, visualize marketing campaign results, and more.
These bi tools allow marketing campaigns to understand deeper metrics about their target audience, and how to carefully craft campaigns that return the highest ROI for the organization. Like other forms of business intelligence, analytics and data visuals are driven from raw, captured data and can be measured through marketing data-driven processes. We will look at using AI and machine learning to enhance our data analytics capabilities and marketing efforts to generate higher ROI results than previous business intelligence efforts in marketing.
Recommendation System As Game Changer Email Marketing Campaign
How Do We Build BI Solutions With Data Insights and Recommendation Systems?
Deep learning based recommendation systems for marketing (like the one we built *here*) require multiple inputs to realize high ROI insights. We will use e-commerce and SaaS as our example niches for this. Here’s an overview of the fields we need.
Company data about customer interaction with the company. This could be previous purchases, pages viewed, time spent on the site, social media following, etc. The closer the data interaction is to our ultimate goal (getting sales!), the better results it will serve us towards our ultimate goal.
Data information that makes up a particular product. This allows our models to learn deep trends about product likeness, and how users interact with it. Data from the various sources that make up a product (customer experience reviews, product titles, product descriptions) works best to achieve our end goal.
Once we’ve defined the feature points we want to use to achieve our marketing analysis goals, we build and train our recommendation system to focus on learning the relationship between the actions of specific customers and what item they ultimately buy.
This model plugs into business intelligence dashboards and input pipelines and provides marketers predictions for what products past and potential customers will want to buy. Using this business intelligence, email marketing campaigns can send personalized marketing emails like Amazon!
3 Real Examples of Recommendation Systems Used for Business Intelligence
These three recommendation systems are perfect for incorporating into your business intelligence strategy when focusing on email marketing initiatives.
Neural collaborative screening for marketing
This machine learning architecture allows you to make product and service recommendations based on just the two data points we know you already have! Using only customers and their past purchases, we build a BI model that predicts future purchases for each user on your email list.
This model allows companies to quickly and easily engage business intelligence tools that make it easier to make email marketing decisions and integrate AI into their business.
For companies that have a higher level of business data, we may use a deeper level model built only for click-through prediction. DIEN is an e-commerce and advertising focused model that matches our business intelligence for marketing focus. While this model requires more data points and a wider variety of sources, its accuracy in understanding the click-through rate of an ad work is top notch.
