Predictive and Augmented Analytics
Introduction:
They used to say that “content is king,” well maybe and more accurately, “Data Is King.” So we have compiled these articles to show you how data can be used for marketing.
Predictive and Augmented Analytics
[1]In this data era, the biggest challenge is simply knowing what to do with all of it. As our data footprints grow, it is challenging for brands to collect and analyze it all. Every time we visit a website, click on an ad or make a digital financial transaction, we’re leaving a little more data behind that can generate incredible business intelligence for anyone who’s able to harness it.
Getting accurate and actionable insights takes a lot of work and a lot of expertise. Yesterday’s business intelligence tools are already struggling under the weight of constantly growing data. The challenges that come with cleaning, organizing, and analyzing data can be enough to make digital managers decide that advanced data analytics is just too complicated and is better left to large enterprises with deep pockets.
This article shows you a light at the end of the data tunnel — predictive and augmented analytics that use artificial intelligence to automate insights that you can take action on.
The Evolution of Data Analytics
Since the Industrial Revolution, inventors and factory owners realized the importance of optimizing for efficiency, and analytics have been part of the business world. Data analysis all started with statistical models, but the process was manual and tedious. For example, in 1880, the U.S. Census Bureau took more than seven years to analyze and report on the collected information. While ongoing developments thankfully increased the speed of analysis, the introduction of relational and non-relational databases and data warehouses made analytics the domain of computer scientists and IT specialists. In addition, evolutions like log file analysis, hit counters, Javascript tags, etc., continued to leave business teams reliant on technical support for critical business insights.
But around 2006, tools like Google Analytics started giving web page owners effortless access to information about their visitor activities, including quantitative and qualitative behavioural data. These capabilities expanded further by 2012 with advanced tools that tracked user behaviour across multiple devices. And today, a digital manager can even access real-time data that can be incredibly valuable — if you know what to do with it.
The volume of data collected in our omnichannel environment is so vast that even savvy business analysts cannot keep up with all the intelligence buried in data that we don’t have the time or tools to access.
According to research gathered by Nodegraph, every minute online, there are 4.2 million search queries, 4.7 million videos viewed, and 400 new Facebook users. In addition, research shows that the advanced analytics industry will likely grow at a compound annual growth rate (CAGR) of 15% and reach a value of over $22 billion by 2023.
That’s a massive amount of untapped intelligence about customer behaviour. And this is precisely where many digital managers find themselves stuck.
While the burgeoning field of data science has grown into a high-demand specialty that takes years of training, that doesn’t mean sophisticated analytics are beyond your reach. Research by Gartner predicts that artificial intelligence (AI) will become ubiquitous within new software products over the next three years. Moreover, advances in technology have already integrated AI-based tools to give us augmented and predictive analytics — without needing an army of data scientists to do all the heavy lifting.
[2]What is predictive analytics?
Like augmented analytics, predictive analytics is a type of advanced analytics. Predictive analytics focuses on identifying patterns in data and determining if those events are likely to happen again.
Predictive analytics combines historical datasets with statistical modelling, data mining, and machine learning techniques to estimate or forecast future outcomes for a business or organization.
Many industries and use cases depend on predictive analytics to make critical decisions. It’s an effective tool for modelling customer behaviour, assessing risk, building accurate sales forecasts, and more.
Understanding the benefits of predictive analytics
Since predictive analytics makes it possible for companies to not only look at what happened in the past but also make reliable predictions about changes or risks to a business, it has two primary benefits for organizations:
1. Answers “what if” before it occurs:
While predictive analytics can’t predict the future, it can help businesses forecast the impact of specific campaigns, initiatives, or changes before they’re executed. In addition, because predictive analytics builds statistical models based on historical data, it allows business users to make decisions for a test drive by calculating the outcome of any choice. With this information, companies can better prepare themselves for the reality of their options and make the most informed decisions possible.
2. Increase competitive advantage:
Like augmented analytics, predictive analytics helps businesses gain a competitive advantage by seeing every opportunity in front of them. These insights into the opportunities to come to enable a company to be proactive, get ahead of any problems before they arise, and anticipate critical trends in its market
Predictive analytics is not without disadvantages, however. Because predictive analytics applies statistical analysis, queries, and machine learning algorithms to historical datasets, it is not the right tool for uncertainty. When the unexpected happens, and businesses don’t have historical data to turn to, as was the case with the COVID-19 pandemic, relying on predictive analytics only will make it difficult to respond.
[3] What’s Augmented Analytics?
It’s often said that success comes to those that can accurately make sense of the abundance of data that enters a business. However, with data joining the company from all angles, it isn’t easy to process it to contribute to the service. Augmented analytics is a process of analyzing both historical and current data through advanced data processing techniques. This includes:
- Statistical modelling
- Machine learning
- Data mining
The days of sitting around a table with endless sheets of paper trying to make sense of graphs, tables, and numbers are over. Instead, we can now deploy complex algorithms and technologies to pick up on trends, customer behaviour and make predictions.
For many years, businesses worldwide have been forced to guess what their audience needs at any given time. What sort of content do they need? What additions do they want to a product? What eBook do they want to read? It was a world of guesses. If we got lucky, we guessed correctly, and the audience grew closer to the brand. But, unfortunately, it was easier to go wrong than it was to go right.
Now, we’re basing all business decisions on insight rather than pure guesses. In short, you can optimize the decision-making process and automate the data analysis process. Suddenly, you’re receiving insights and predictions in seconds rather than pouring over 100-page reports.
With this market steadily growing, businesses in the United States are starting to realize that their money is better spent with augmented and predictive analytics than elsewhere. By 2025, experts predict a market worth $22 billion thanks to 25% compound annual growth from 2021.
People are often confused by ‘augmented,’ but it’s another way of saying something gets larger. If a product goes through an augmentation process, it gets bigger. With augmented analysis, we’re handling more significant quantities of data and generating more substantial outcomes.
Benefits of Augmented Analytics
Now that you have a grip on the basics, why should you invest in augmented analytics and the technology that comes with it?
Faster Processing
Firstly, you increase the speed at which the business can analyze data. Humans are essential to a business, and it’s important to note that this technology isn’t designed to replace humans. Instead, it’s designed to make our job easier. Despite all the positives of human workers, it’s fair to say that data processing and analysis are slow tasks. With so much data, it’s impossible to analyze and develop actionable insights within hours or minutes (let alone seconds!).
As technology handles the analysis stage, your team works on high-value tasks (technology cannot perform!). With augmented analytics, you get valuable insights but without the expensive and unattainable data science team.
Improved Accuracy
Even after humans go through lots of data, there’s no guarantee that the insights are accurate. People get tired, they misread data, and they make mistakes. The technology produces more reliable and precise results.
According to one source, cleaning and organizing data accounts for over HALF of all time spent analyzing data. Augmented analysis performs the same task in seconds which allows you to utilize your time more effectively. In addition, the technology generates accurate insights leaving humans more time to ensure that the results are used correctly.
Better Customer Experience
Why are we going through all this effort anyway? Ultimately, you’re reading this guide and seeking better data analysis systems because you want to provide a more robust experience for the customer. As a result, you’re analyzing real-time information. And you will gain an understanding more about your audience than at any point in your history.
Predictive and augmented analytics are so valuable because the insights we receive on the other side are actionable. In other words, they tell us what we could do next. So whether you learn about the efficacy of an ad campaign, how people feel about products, or something else, AI guides your decision-making and constantly improves the experience for all customers.
With predictive analytics, the apparent benefit is that you see likely outcomes before they happen. If used correctly, this adds a competitive edge and could be the difference between customers choosing your brand and a competitor — as long as you act on the insights accordingly.
Optimize Your Marketing Strategy
Finally, all of this means that you’re optimizing the marketing strategy and ensuring a positive ROI (as well as receiving a positive ROI on the technology itself!). Augmented analytics gathers data from various sources, whether this is Shopify, Google Ads, or Facebook. Therefore, you’re improving the marketing strategy from all angles.
Additionally, businesses have been able to identify anomalies that previously would have gone undetected. Often, companies focus on external threats and cyber criminals when internal mistakes can be just as costly.
Augmented Analytics vs Predictive Analytics
When researching this market corner, you’re likely to see both of these phrases used, but how do they differ?
They both fall under the same umbrella but augmented Analytics uses natural language processing and machine learning to spot trends and patterns. Over time, it continually learns based on previous performance and historical data. Therefore, it gains in power, and the marketing team is left to validate.
Although there are many similarities, predictive analytics is more focused on using data to predict future outcomes. It still uses machine learning and other complex techniques, but it aims to predict outcomes (hence the name!).
Conclusion:
Data is everywhere. Using it to your benefit and the benefit is a powerful tool for your website marketing. We hope that these compiled articles can assist you in your marketing efforts.
Articles compiled by hughesagency.ca
Article reference links:
