How analytics have evolved.
Who would’ve thought just a few short years ago that we'd be speaking about predictive analytics and machine learning in the same sentence?
Predictive analytics has existed for decades.
Open source programming also thrives on sharing technique and solutions.
All of these developments, combined with cloud solutions, have ushered in accessibility to machine learning techniques in almost every industry and research.
Companies have been just getting by with basic metrics that explain what has happened with marketing activity on a website.
But machine learning changes the measurement world.
Some competitors are early adopters, gaining improved operations and drawn to the allure of discovering new relationships from collected data that can spark new opportunities.
Shifts in Analytics
With that in mind, analytics is experiencing some major shifts in the face of machine learning. Multiple trends have emerged.
Data Mining Shifts The Role of Web Analytics
The need to verify data has always been important. But it's become even more important as smart devices from phones to cars have become networked to the internet.
The influence of web analytics has been amped up for data mining, offering an ability to delve into internet-related data. Moreover, the open source movement, through the support of APIs, has created an environment where sharing data is possible. Such conditions feed into machine learning models, which thrives on a lot of data to build model accuracy.
By the Way, You Can Drop the 'Web' in Web Analytics
Just like Will Smith dropping the Fresh Prince moniker (or MC Hammer dropping the “MC” from his name, if you prefer), the term “web analytics” is passé. It has been long dropped among analytic practitioners.
The Web Analytics Association changed its name to Digital Analytics Association years ago. The change is a fitting byproduct of technology advancements.
Thus we are seeing “web analytics” being referred as “digital analytics” or just plain old “analytics”.
Accounting for Metadata Into Current Marketing Tools
Geospatial data is more widely available, such as AdWords snippets that can produce data associated with a location. That popularity is increasingly appearing in predictive analytics.
This means solutions will have to find ways to blend such metadata into its displays. That means...
The Growth of Tools That Can Easily Blend Data
There is a tool to fit every skill set and need, ranging from a dashboard-enhancing solution like Tableau to cloud-based solutions like Data Studio that can help analysts group data from Google-sponsored tools and databases.
This is useful for establishing initial datasets for advanced techniques.
What Tool Best Visualizes My Data Model?
Whereas in the past, statisticians used some sort of scripting language to build a predictive model, vendors have been making inroads in making their software easier to use.
Many IDEs help display data in more user-friendly ways. This leads to an array of model building processes to choose from, like Neo4j which data relationships in a node diagram, or data visualization packages for R programming that complements RStudio, a popular IDE for that language.
Despite these advances, one asterisk remains:
Marketers and analysts still require the skills and judgment to make sure the software reports accurate data.
Technology Leaders Are About Machine Learning Deployment
Remember when the world seemed divided into PC and Mac? That Berlin Wall between operating systems has been long torn down.
There have been some tech camps, such as the browsers wars, but once the cloud took off, many of these difference became academic.
With the world moving toward machine learning, the big kahunas are MAG — Microsoft, Amazon, Google — leading the mainstream path in predictive analytics.
In fact, Amazon is the most intriguing and in many ways the most lethal threat given its large retail footprint, forays into delivery drones, experiments with brick-and-mortar bookstore, and most intriguingly, a budding personalization advertising platform.
Privacy Management Can’t Be Private
Businesses are becoming judicious on how consumers access their content that appears in-store and online, particular as recommendation engines have access to consumers in more ways than a website screen.
The result: managers must be more alert and accountable to customer reaction to those media and if privacy is violated when data from those multiple sources are combined.
Highlighting how privacy and data are managed has to be part of every business model to just access. Permission is no longer an option. It is a must.
Recommendation Engines Are Refining A/B Testing
A/B testing usually highlights when one web element is preferred over another. But accuracy, the quality or state of being correct or precise, is difficult to achieve when the test is conducted manually. Instead, analytic solution providers are creating test platforms with machine learning test capability.
This means media elements for a website, app, or paid media campaigns can be tested against various layouts, images, and texts that define not only presented offers but also impact the larger customer experience.
Since machine learning algorithms generate data and responses, the payoff for optimization testing is better management of test accuracy.
Current solutions are starting to come to market with this capability. Adobe announced an Auto-Target, an A/B testing feature that uses a machine learning protocol to automate personalization testing of elements and learn the preferred individual experience with media.
With companies expected to spend on analytics in general over the next three years, you should expect these analytics trends to last a long time.