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The evolution of customer analytics

Anja De Vries
10 Mar 2022

This blog post explores the evolution of customer analytics, how customers have taken power, its growth due to social media and what we can expect next.

Customer Analytics timeline
Reference: Capgemini

What do we mean by Customer Analytics and why is it important?

Customer analytics has come a long way due to the explosion of the internet into every aspect of consumer life. It allows for the capture of meaningful data patterns through customer profiles and internet behaviour data. These patterns can help deliver better products/services, more personalised online experiences and new beneficial insights by leveraging them alongside prescriptive, descriptive, and predictive analytics.

In today’s competitive market, customers now more than ever have the power and are no longer the captive audience they once were in traditional marketing. Whilst customers used to be loyal to a specific brand, they are now more loyal to brands that provide the best customer experience and have the choice to do business where they want to.

In one recent study, 80% of customers indicated they prefer brands that offer a more personalized experience, rather than the generic one-size-fits-all marketing approach and 1 in 5 customers say they would pay up to a 20% premium for personalised products or services. With this in mind, it is crucial for businesses to use data-driven decision making and adapt a more customer-centric approach.

How Customer Analytics came to be

Starting in the late 1980s, businesses were able to measure and analyse marketing strategies through market attribution. Market attribution allows for businesses to monitor marketing techniques that contribute to successful conversions across multiple types of media.

Moving into the 1990s, businesses wanted to start understanding when and how much traffic was coming to their websites. Through counting the number of hits made to a web server, this was soon achieved and more complex questions were starting to be asked.

By the late 1990s, customer analytics started using more sophisticated methods by tagging sites and using cookies to gain more accurate insights.

Then in the early 2000s social media arrived. Social media has changed many aspects of the internet since the first true social media launched and it has added another complex dimension to understanding customers through their social interactions and customer profiles by unlocking new insights. These insights allow businesses to not only see the number of people who engage and follow them, but also gauge thoughts and feelings towards their brand and products through sentiment analysis and analysing user reviews.

By the late 2000s, digital businesses had become more than the traditional eCommerce website and needed to adapt to having a presence on all the major marketing and social media channels to develop a seamless experience and stay ahead. Businesses could then continuously interact with consumers, who are now dependent on using their mobile devices everywhere they go to stay connected, to promote their products and services, and highlight positive customer feedback. By promoting feedback and essentially electronic word of mouth (eWOM), businesses can retain existing customers but, more importantly, attract new ones. eWOM has grown majorly with the internet and social media, allowing consumers to gain feedback and opinions on businesses not only from people they know, but from all over the world. According to AdWeek, 85% of website visitors find content generated from other users more influential than brand photos or videos, showing how important consumers value other consumers’ opinions. Due to this, customer analytics has had to evolve by also incorporating these reviews and social media data and to keep up with the new ways customers interact with products and brands and their journeys across the different channels.

In today’s digital world, businesses can collate all these different sources of data and perform more interesting analytics such as customer behaviour analytics and predictive analytics. It allows businesses and brands to analyse consumers’ digital footprints through data collected by tracking customers across apps and websites using fingerprinting, pixel tracking and cookies (amongst many others), capture full session details at a much more granular level and add a more human side of analytics.

How can an organization use customer analytics and what is the future?

The Global Customer Analytics Market is expected to reach $29.8 billion by 2026 and it will be interesting to see what the future for customer analytics will bring. However, in order to stay at the forefront, businesses and brands will need to continue to be able to collect and process increasing quantities of dynamically changing personal data and analyse it in real-time to make smart business decisions and deliver a targeted, personalised customer experience. Artificial Intelligence and Machine Learning both have had a profound impact on customer analytics and by using these technologies, businesses will be able to speed up both customer experience and customer service by allowing more sophisticated ways in which businesses can learn trends, identify patterns, and predict future behaviour.

One example of how these technologies will transform the retail sector is through the creation of digital stores. Capgemini’s CornerShop is a prime example where retail shopping and customer engagement has been transformed to bring to life the store of tomorrow. By using the latest technologies such as Machine Learning, Augmented Reality and computer vision, Capgemini, The Drum and SharpEnd have reimagined shopping experiences and enabled the evolution of customer experience and improvements in in-store operations.

Through a combination of data driven AI and machine learning, alongside human insight, businesses will be able to provide a seamless customer experience, gain a major competitive advantage and fortify their place in the market by not only using descriptive analytics to show what has already happened, predictive analytics to show what could happen, but also prescriptive analytics to show what should happen in the future.