It’s no secret that the business analytics space has been in a state of flux for years. With the rise of AI and machine learning, analytics is about to be transformed — for the better! You know that data analytics is vital for improving your business. But have you ever thought about where analytics is headed? Have you considered what it might look like in a few years?
If you're reading this article, it's because you're trying to get your hands dirty with advanced analytics. It's unlikely that you're new to the concept of data-driven analytics or business intelligence. You've undoubtedly heard about the whole idea of data-driven decision-making and how crucial it is to keep up with the latest trends in technology so that you can stay relevant in today's competitive marketplace.
If you’re not yet using advanced analytics, you should know that it is a real game-changer in the way product-led businesses can now tap into data and reduce time to market. See this guide for a primer on getting started with analytics and why it is so powerful.
Analytics is the heartbeat of your business, it makes the data crystal clear and gives you a complete picture of your customer needs and trends, focus energy to optimize strategies. But advanced analytics, while it's growing in popularity, is still a complex subject.
If you're looking to use advanced analytics as a competitive advantage, you need new tools that simplify decision-making. With so much data produced every second, how can growth teams separate the meaningful information from all of the extraneous noise?
There are four types of advanced analytics: descriptive, predictive, prescriptive, and augmented. Each type of analytics has its strengths and weaknesses. Before delving into each of them, let's define the four types of analytics.
The four types of analytics are interrelated, and each of them offers a different scope of insights. With the proper analytical techniques, data can deliver richer insights for growth companies.
Let’s take a closer look at how these four types of analytics can bring a new dimension to using data to drive insights, create better customer experiences, and grow your company.
Descriptive analytics is a method for summarising and describing data, making it easy to understand by a non-data-savvy audience. Descriptive analytics is generally used for historical data to explain what has happened during a given period. It usually refers to looking at a series of events from different perspectives, giving you a snapshot of what happened last month, last quarter, or last year. It is all about making sense of data to paint a bigger picture of what is happening in your business.
Descriptive analytics is focused on answering questions like: where did we make the most money last month; what’s our revenue by day of the week; how many customers did we gain this quarter?
It helps gain insight into the performance of your business. However, descriptive analytics may only tell you what happened, but it doesn't explain why.
That said, descriptive analytics analyzes historical information, while predictive analytics uses that data to predict what will happen in the future.
Predictive analytics is a concept used in business to predict future patterns, uncover trends, needs, and customers’ demands.
Predictive analytics is the science of finding actionable insights in large data sets and revealing trends and patterns otherwise hidden. It’s used not only to predict future trends but also to support making faster decisions. Using predictive analytics is a great way to avoid a lot of wasted time and money on growing your business.
Predictive analytics is being applied to many use cases within the healthcare, marketing, and finance industries. In particular, predictive analytics is being applied to improve patient care in response to health trends and preventative medicine, improve sales forecasting, and battle fraud in online payments.
Predictive analytics looks at the existing data and concludes to answer your business questions.
There are many powerful applications offered by predictive analytics for product-led teams — right now, it’s a great time to get on the ball and utilize these powerful tools! For example, if you’re a growth team marketer, you can use predictive analytics to determine which customers are likely to buy your product or service, which ad can bring more visitors to your website, and more. Then you can target them with personalized campaigns. If you’re a product maker, you can use predictive analytics to inform your roadmap planning to ensure that your customers have the best chance of success with the features they anticipate most.
Every digital product is ever-evolving — new features are added, replacing the old ones, monetization models change, and new product-market fit findings. Product decisions must be made quickly; this is why predictive analytics is a must-have tool for startups who want to make rapid progress in their business.
Without predictive analytics, you'll have no idea which updates are more effective than others, whether customers are using your product in the way you expected them to or if they're purchasing your higher-ticket items.
There's a reason why businesses invest in predictive analytics and artificial intelligence — these technologies help companies make decisions faster, be more responsive to user needs, and streamline their marketing efforts.
There are many benefits from working with advanced analytics, but not all companies have taken advantage of this resource. When you think about analytics, you might only consider the marketing side of the business. However, marketing isn't the only area that can benefit from a strong understanding of analytics.
Predictive analytics is a must-have tool for product-led teams. Every time a customer interacts with your product or service, they leave behind a trail of data that can help you understand their needs and tailor the feature set or user experience to them. In this way, you can make product or marketing decisions that are more powerful and effective — testing specific marketing campaigns to see which work, for example.
Every marketing campaign or product retention effort will be more effective if the growth team targets particular customers with unique marketing programs. Upsell, cross-sell, and retention efforts will have a better ROI than the old-fashioned marketing approach. Adopting predictive analytics is about making small changes that add to significant results.
From chatbots that respond to customer inquiries to robots that can predict consumer spending habits, predictive analytics is changing how businesses operate every day.
Predictive analytics widely employs anomaly detection technology. If something unusual happens, you want to be able to notice it and act accordingly. This is exactly what anomaly detection does — it finds hidden patterns in your data and tells you that something unusual is happening. For example, you might have an anomaly detection system that spots weird activity on your sales platform or one that highlights strange activity on your customer service site.
Predictive analytics allows verifying initial product and marketing assumptions, using unique mathematically intensive predictive user-level behavior modeling for the key metrics including:
Predictive analytics minimizes communication errors, helping further enhance customer satisfaction, loyalty, and word-of-mouth referral since customers get more relevant and personalized communication. Predictive analytics can be a helping hand to any growth team, saving time to market, and streamlining real-time, data-driven decisions.
Prescriptive analytics is a type of analytics that uses machine learning and artificial intelligence algorithms to automate business insights. Prescriptive analytics investigates the potential effects of various alternatives and which options will provide the best results.
The difference between predictive analytics and prescriptive analytics is that predictive analytics uses past data to predict the future. In contrast, prescriptive analytics uses past data to support decisions about the future, closely aligning those decisions with customer behavior. Prescriptive analytics is excellent for making live or near-live decisions since it goes a step farther than predictive analytics by providing advice on how to proceed.
The data is ubiquitous. With data from search activities, online transactions, customer loyalty programs, user-generated ratings/reviews, or product feature adoption, we can build previous purchase patterns, predict the future ones, and prescribe new approaches to influence future customer behaviors and purchasing choices.
It takes a lot of time, energy, and money to ensure you’re getting the most out of your marketing spend. To do so, you’ll need to continually research trends, identify opportunities in your market, and develop strategies that are uniquely suited to your brand.
Prescriptive analytics can help growth teams make sense of the data, a powerful tool for planning, measuring, and delivering marketing activities. Prescriptive analytics can help you understand the uncertainty in your data, determine how much money should be put toward each marketing activity, which channels are most effective, and how much revenue we should attribute to each.
Prescriptive analytics is a great way to create new products or services. It enables growth teams or even digital agencies working with large amounts of customer data to develop advanced marketing strategies, better position and price products. Prescriptive analytics allows acting more precisely with marketing campaigns and customer outreach and not relying simply on intuition or experience.
According to Gartner, augmented analytics enables technologies such as machine learning and AI to assist with data preparation, insight generation, and explanation to augment how people explore and analyze data in analytics.
Augmented analytics aims to harness the power of machine learning and natural language processing (NLP) techniques with human interpretation to provide automated insights with greater accuracy.
Augmented analytics is an unstoppable force, bringing a new dimension to how product-led teams can use data to drive insights, create better customer experiences, and grow their businesses.
Insights are the new oil, not data since data is worthless unless it can be transformed into actionable insights. Today, companies can access more data than they could get just a few years ago. As businesses continue to struggle with the burden of growing datasets, the challenge of integrating data from disparate sources, and more data generated every day, new technologies are being developed to help businesses handle these challenges.
Augmented Analytics makes data actionable. It is a powerful tool that can help you cut through the noise and truly understand how your business is performing in areas such as customer acquisition, engagement, or retention. Through visualization and data storytelling, augmented analytics helps you better understand your customers so that they will keep coming back.
Growth teams today are building more complex products with an increasing number of features. As a result, measuring the impact of all of these features on the business becomes increasingly tricky.
And this is where augmented analytics comes in, combining human knowledge with machine learning and natural language processing to help you better understand your customers' behavior.
How can we make complex data analysis easier to consume for all marketing team members? Augmented analytics is the answer. The best way to understand it is to think of augmented analytics as a layer on top of your data that provides additional context and clarity. Understanding the data is more accessible by providing relevant information outside of numbers and charts.
The world of business has changed significantly over the past decade. It’s no longer just about selling a product or service to a consumer. Analytics can help you make more informed decisions about your business by giving you access to insights that you didn’t know existed before and making decisions that are:
Growth teams leveraging augmented analytics capabilities are poised to make better business decisions, resulting in healthier revenue streams, more engaged customers, and ultimately better business performance.
Advanced analytics will only serve to improve your business — and it's never too early to start planning for the future!
Narrative BI for Google Analytics removes the pain involved in sifting through Google Analytics data to find actionable insight, making it possible to extract natural language narratives with unparalleled speed and accuracy.
We hope this guide to four types of advanced data analytics has been helpful in your product and growth marketing pursuits! If so, please share it with your friends and colleagues and subscribe to our blog!