This article was written by our partner Cognetik, as part of our series highlighting direct insights from our large ecosystem of partners.
Data Science is an umbrella term used for multiple industries, such as data analytics, big data, business intelligence, data mining, machine learning & AI, and predictive analytics, and is clearly on an upward trend. Specifically, the big data and business analytics market was valued at $168.8 billion in 2018 and is forecasted to grow up to $274.3 billion by 2022 at a CAGR of 13.2％, according to Market Reports World.
The surge in spending for data science solutions, talent within the industry, and successful implementations demonstrate that companies understand the tremendous impact data can have on business performance.
As business interactions around the world become increasingly digitized, massive amounts of data are created and can be evaluated through predictive analytics tools to help companies gain a better understanding of market dynamics and underlying trends. With this knowledge, companies can then uncover the needs and expectations of their customers, and ultimately improve the end-user experience.
Therefore, it is no surprise that predictive models rank as one of the top big data technology trends around the world. The value data science can provide for businesses today is unprecedented.
However, even though leveraging data is at the heart of many businesses today, data alone can not provide all of the answers organizations need. Companies require insights and actionable paths they can take to optimize and adjust their business for maximum results.
Impact of Data Science & Global Utilization
The rise of data science has helped analysts and digital teams at large become real-life wizards who gather data at an unparalleled pace, validate its accuracy, assess its meaning, generate insights, formulate actionable plans, and deliver incredible results. Companies all over the world have realized that this isn’t necessarily magic, but rather a transformation and process they need to adopt in order to stay competitive and maintain relevance in the digital age.
Data Science has provided solutions for many industries that have been struggling for a long time. For example, in retail, companies have completely revamped the way they interact with their customers by focusing on creating easier paths for purchasing and tailoring the experience to the needs of specific audiences. In the healthcare industry, data science has drastically reduced the time needed to develop new drugs and has streamlined the ability for patients to get professional help in remote areas.
Cities have also been forever changed by data science, with thousands of sensors embedded throughout our neighborhoods to optimize traffic, reduce crime rates, and improve the overall quality of life.
The Connection Between Data Science & User Behavior
A business may experience thousands of digital interactions with a single user across display, search, social, and on the site or app. These interactions take place on multiple devices, such as mobile, desktop, tablet, or wearable devices.
Initially, analyzing immense data volumes associated with each individual user to make relevant connections was no easy task. However, with the rise of AI and machine learning algorithms, analyzing data points from multiple data sources to create a holistic view of users is now realistic and attainable.
User behavior, including actions, what they search for, and how they interact with digital properties as a whole, can now be collected and transformed into specific customer segments. These insights ultimately lead to personalized user journeys to gain a comprehensive understanding of user behavior, develop targeted advertising, and improve digital experiences.
For retailers today, recommendation engines are among the most used tools because they can give businesses an in-depth look into the interests and goals of their customers and help predict trends. The recommendation engines are complex machine learning components and deep learning algorithms designed to keep a track record of customer segments, analyze behavioral patterns based on this data, and improve the digital experience for customers.
Why You Need to Understand User Behavior
Banks and retailers were among the first industries that realized understanding behavioral patterns of their clients can lead to incredible breakthroughs.
For example, with data science, banks can manage their resources efficiently and make smart decisions through customer segmentation, fraud detection, customer data, and risk modeling via real-time predictive analytics.
By leveraging data science, banks can also have a holistic view of their customer lifetime value as well as part of specific profiling patterns. This, alongside behavioral pattern analysis, allows banks to make accurate predictions about their clients.
In time, customer profiling became one of the top data science applications in finance. By leveraging data they collect from all sources linked to their customers, financial institutions have managed to assess risk and liabilities associated with specific clients before even working with them.
Cognetik: Taking Behavior to the Next Level with Data Science
Our valued partnership with Contentsquare helps numerous industries capture intricate behavior patterns of consumers, provide sophisticated segmentation, and improve digital properties through the power of data.
Data science takes data analysis to the next level, allowing businesses to predict what users might do, augment the user journey, and provide incredible insights that are unmatched.
As an analytics and data science company, Cognetik helps the Fortune 1000 go above and beyond the standard recipe for making data science a reality. Our team of experts can guide you through the process, analyze what would work best for your business, and help you implement it in order to gain a holistic view of your users and improve your digital properties.
Adobe Stock, via titima157The Digital Happiness Index: Quantifying Your Customer Experience
Although conversions are the desired outcome of a good customer experience, they are not the end-all be-all for brands. A happy customer may make a purchase, but more importantly, a happy customer will return.
But how exactly do you define customer happiness? How do you understand the nuances of customer frustration and pinpoint what exactly fosters engagement? And how do you turn all this intelligence into an effective retention strategy and greater customer lifetime value?
There are plenty of systems designed to measure user experience; these primarily and, for the most part, deal with the locations users visit on your site, conversions and the oft-cited biggest UX failure: bounces.
But a basic set of analyses on user experience won’t cut it, and certainly won’t glean any discernment on the nuances of users’ digital happiness. The good news is that, for brands interested in quantifying the user experience as a whole, there’s a metric that does exactly that.
Calculated from several other behavioral metrics and consolidated into one mega metric, the Digital Happiness Index (DHI) is a unique measure of visitor satisfaction, providing an objective view of whether or not your overall experience is hitting the right notes.
What Is Digital Happiness And How Can You Achieve It?
Before we delve into the DHI, let’s focus on digital happiness. A rather simple concept, it denotes the convenience, satisfaction and even the pleasure of interacting with a website or online interface such as a search engine results page (SERP).
As a feeling, it is incidentally difficult to pin down, even in the digital realm. But with the new, futuristic metric that is the DHI, you can determine how happy your site visitors are, based on their experience with your site or app.
The first of its kind, the DHI combines KPIs from the 5 key strands that contribute to overall customer satisfaction:
Is navigation seamless and friction-free? Is your content proving effective to helping visitors reach their goals? Are visitors coming back to your site? Are they exiting early or completing their journeys? And finally, are they finding what they’re looking for — be that information or products?
By quantifying these various strands of experience, and combining metrics into one score, the DHI provides brands with an objective grasp of whether or not visitors are enjoying a positive experience.
Calculating the DHI: the 5 Dimensions of Digital Experience
Here is a look at what comprises the Digital Happiness Index and what makes it tick.
Using behavioral data from our tool, the DHI separates the data into 5 dimensions to filter the numbers into intelligible concepts behind visitors’ digital happiness. Our clients get a comparison to industry standards, and every score represents an aggregate of every session on the website.
As we mentioned earlier, the DHI has 5 components, aka the 5 dimensions that make up its final score, a number out of 100, which is the average of the 5 scores of each dimension. To come up with this rating, we consider the following five dimensions:
- Flawless: Are customers enjoying a smooth experience free of technical performance issues?
- Engaged: Are customers engaging with and satisfied with the content?
- Sticky: Are visitors loyal, returning to the site frequently?
- Intuitive: Does the navigation make it easy for visitors to enjoy a complete experience?
- Empowered: How easy is it for customers to find the products and services right for them?
Each of these 5 individual scores is determined by its own calculations, based on metrics like time spent on site, time spent engaging with pages/elements, bounce rates, and more.
It also takes into account if users have reached their destinations and the way they’ve done so. It captures whether users ran into UX issues like non-intuitive navigation — clicks on non-clickable content, misleading clicks, et al.
Making Sense of the Digital Happiness Index
Innovations in SaaS and marketing have led to more avant-garde methods of measuring digital customer experience and benchmarking customer satisfaction.
Although the complex, 5-tier system of our mega metric is supplemental, it is very much in line with our granular approach to behavioral analytics.
The fact that the 5 dimensions deal with different occurrences in the UX means the DHI is casting as wide a net as possible to capture your customer’s mindset. Based on this score, you can shine light on areas of friction and other obstacles in the customer decision journey.
Customers today will not hesitate to review a poor UX or give one star for a session that doesn’t meet their expectations. But they are also giving you continuous feedback on your site or app through their interactions — with every tap, click, scroll or hover, they are voicing their feelings about your CX.
Here at Contentsquare, we’ve got a horde of people dedicated to helping you hear and understand what your customers feel and want — in fact, we’ve got 170 people in R&D and innovation alone.
Happiness of any kind is difficult to pin down to a numerical format. With a consolidation of 5 distinct aspects of the UX, you will come as close as possible to determining how digitally happy your visitors are with your content.