Advanced analytics—a subset of business analytics—goes beyond traditional business intelligence (BI) techniques to make predictions about what’s likely to happen in the future and what drives current trends, giving organizations that leverage these techniques a leg up over the competition.
Today, data has the power to shape the world. Projections estimate that IoT devices alone will generate 79.4 ZBs of data by 2025. But, with all this data in reach, the next question is, what do we do with it?
In this post, we’ll break down of some of the most common advanced analytics techniques so that you can utilize them for optimal business outcomes.
What Is Advanced Analytics?
Advanced analytics produce insights that would be difficult to discover using traditional business intelligence techniques, such as reporting and querying. This is done using a combination of several different quantitative methods, such as descriptive and predictive data mining, statistics, optimization, and simulation.
In other words, advanced analytics uses next-generation technologies and mathematical modeling to answer the question, “What will happen?” rather than “What happened?” Examining the techniques used can help your organization decide how to get the maximum impact from advanced analytics.
Use These 5 Advanced Analytics Techniques to Enhance Your Business
Ready to dive into advanced analytics, but unsure of where to start? Here are five common techniques that can help you make a splash at your organization right now.
1. Descriptive Modeling
Descriptive modeling refers to a mathematical system that describes the relationship between factors responsible for real-world events. It’s often used to maximize the effectiveness of advertising and marketing campaigns by quantifying the target audience’s interests and behaviors. This data is then used to estimate how the target market might react to different elements of an organization’s marketing strategy.
The main aspects of descriptive modeling are:
- Customer segmentation: Helps to differentiate customers so you can discover deeper, more impactful insights and tailor your marketing accordingly
- Behavior-based segmentation: Analyzes how customers use and purchase products or services, enabling you to tailor your offerings to meet their needs
- Value-based segmentation: Pinpoints and quantifies how much value each customer brings to the organization and enables you to tailor your marketing to high-value customers specifically
- Needs-based segmentation: Focuses on ways of benefiting from customers’ motivations so you can cater your products and services based on what will best meet their wants and needs
Using descriptive modeling, a company can ascertain how different product placement strategies impact the rate at which customers purchase their products. They can then use this to ensure their products get the most prominent spots on the shelves, showroom floors, and digital marketplaces.
2. Predictive Analytics
Predictive analytics uses data engineering to predict what’s likely to happen in the future. Predictive analytics involves far more than collecting and studying data—it’s centered around using the data you already have to generate future insights and making them work to your organization’s advantage.
Foundational predictive analytics techniques include data mining, machine learning, and predictive modeling. Using these tools, predictive analytics can erase much of the guesswork surrounding what customers may do in response to everything from advertisements to election campaign strategies to new product releases and give a clearer picture into what they will do.
For instance, by using predictive analytics to analyze how successful past ad campaigns are within certain target markets, an organization can make smart decisions about how to design and deploy their next campaign.
3. Optimization and Simulation
Optimization and simulation involve studying data, simulating potential outcomes, and then using those simulations to make a data-based recommendation about what to do next. In this way, your machine learning model can run different scenarios and present the most profitable one for you.
This can be a powerful tool when a company wants to shift away from making decisions based on gut feelings and move toward data-driven strategies.
For example, a car manufacturer could study historical datasets associated with the timing of the releases of different classes of vehicles. Their simulation-based optimization model can then recommend the best time of year to release each class of vehicle. In this way, the company can give customers exactly the kind of car they’re looking for, when they need (and want!) it most.
4. Text Analytics
How many pages can you read in a minute? One? Two? Three? Now, what if you had to not only read the words but also describe linguistic patterns indicating what they say about the author’s intentions? Text analytics can do this for you. It involves a machine learning system that studies text, generated through writing or speech, and discerns patterns that provide contextual insights.
For example, text analytics can be used to examine interactions between customer service reps and customers via chatroom discussions. It can automatically pick out phrases that indicate the most common issues customers have. Instead of a customer success manager having to comb through hundreds of pages of conversations, the text analysis machine can figure out which problems should rise to the top of their troubleshooting list.
5. Multimedia Analytics
Multimedia analytics incorporates machine learning models that study various forms of media and extract meaningful patterns from them. Using multimedia analytics, organizations can study large collections of videos, text, speech, music, and raw data to unearth valuable insights that would have otherwise gone unrecognized.
For example, a company could use multimedia analytics in conjunction with facial recognition technology studying the expressions of their customers during the purchasing process. While keeping the subject’s identity anonymous, the organization could figure out which emotions people were feeling using the movements of facial markers. For instance, if someone’s eyebrows are raised and the corners of their mouth have crept higher, they might be smiling.
By finding connections between emotions and purchasing decisions, the company can then help inspire those feelings to create an atmosphere that’s more conducive to a sale.
Use Advanced Analytics to Propel Your Organization Forward
You’re already collecting mountains of data—now it’s time to make the most of it.
Advanced analytics can help you leverage your data to take your organization’s operations to the next level. Whether you use it to fine-tune marketing strategies, optimize internal processes, or get a data-based glimpse into what the future may hold, advanced analytics gives you the flexibility and power you need to meet your goals.
All on board and ready to get started? We’ve put together A Human’s Guide to Machine Learning Projects to walk you through step-by-step how to get your advanced analytics work off the ground.