Tag: 2018

The Data Analytics Trends that Will Shape 2018

Posted by on January 10, 2018

2018-data-analytics-trends

As a field, data analytics is only growing. Not only has the industry of data science broadened substantially, but many companies are finding themselves devoting large amounts of resources towards understanding data analytics and trying to identify new trends. This reliance upon data is only going to grow through 2018, as companies are finding that the data that they collected may contain even more useful information than they previously believed.

2018 is going to find many companies making better use of the data that they already have, and fine-tuning their existing data collection and analysis methods.

Better Personalization Metrics

Industry leaders are hard at work creating incredibly detailed profiles of their customers. Companies don’t need to develop this information themselves. Google, for instance, has fine-tuned its customer profiles and made these customer profiles accessible to those using its analytics and advertising services. Social media platforms have been able to capture customer information similarly, from Facebook to LinkedIn.

The result of this is that advertising is likely to become hyper-personalized to each customer. Not only will companies know the demographics of each customer (age, gender, location), but also their buying habits, how much money they make, and which locations they frequent. Businesses will be able to increasingly target customers and anticipate their needs, ideally creating a situation in which advertising becomes more valuable to the customer.

Augmented Reality Systems

Augmented reality has been kicked around for the last decade, held back by issues regarding processing speed and (perhaps more importantly) battery life. Augmented reality feeds digital information about an individual’s location directly to them, often through a visible “heads up” display.

Not only is this going to change the way individuals interact with the world, but it’s also going to change the data collected. How often do users spend looking at a specific product? Which products or locations do they display further interest in? These will all create incredibly valuable data points that will again be used to create a realistic model of what customers want and need.

Streamlined Data Solutions

Companies have built up their data caches. Now they’re looking for streamlined, agile solutions that can help them make use of the data. In the past, companies were satisfied with collecting as much data as possible and then mining them for as many insights as they could find. Now, companies are more focused on fine-tuning their systems, generating and using the minimal amount of data they need for effective results.

This will create a rise in agile data science, whereby companies will be able to quickly create data sets, respond to and modify these data sets, and produce tangible results from their data sets. In this, the emphasis will be less on the data itself and more what the data can do for the company.

The Science of the Customer Journey

Buyer personas have led to further exploration of the customer journey, a science that attempts to identify the stages that customers go through when investigating and making a purchase. Customer journeys are an incredibly effective way to understand customers and their unique needs.

Data science is likely to be integrated into further understanding of the customer journey. What drives a customer to seek a product? How often does a customer generally research a product? What types of research are most effective and most compelling? What makes a customer more or less likely to find a company and engage in a purchase?

Customer journeys are designed to model customer behavior, so that companies are able to more accurately give customers the information and the prompts they need to continue their journey. In the coming year, this will evolve into a science of its own, and marketers will likely be collecting more customer behavior-related data than ever.

Machine Intelligence Continues to Advance

Alongside all of this, machine intelligence and machine learning will continue to advance. Many businesses have large volumes of data, but it is actually identifying patterns within that data that has become difficult. More advanced machine algorithms will be developed to clean usable, actionable insights from the data that is stored. Machine intelligence will increasingly be used for tasks such as scoring leads, identifying keywords, and targeting specific demographics.

More advanced, learning algorithms are being developed that can, within their parameters, work to improve their own functions. With the right data sets and the right code, marketing algorithms will be able to fine-tune themselves and optimize their own performance. This will be especially useful in A/B testing or split testing, as algorithms will be able to test out different marketing functions and determine the optimal configuration on their own.

Small amounts of this are already cropping up in apps and social media platforms, such as the ability of an algorithm to determine what is most likely to get a profile clicked on, or which photos and posts are most engaging. This can be used in a marketing sense to determine not only which products are most attractive to customers, but which photos they prefer to see, and what descriptions they’re most interested in.

For the most part, 2018 is going to see a maturation of data analytics and data science, as companies invest more money into both collecting and understanding their data. But technology itself is going to play a significant role as well, as the technology behind machine learning and AI is becoming more sophisticated and complex. Either way, companies are going to have to invest more in their data if they want to understand their customers and continue to market directly to them.