How to use data analytics to enhance customer experience

What does a better customer experience look like in marketing? It could mean the delivery of more relevant information, the delivery of information right when they need it, or even to help save customers’ time or make choices simpler.

Data analytics drive these actions. If you have enough data to extract trends and patterns, you will be able to make the right information reach the right audiences at the right time. Businesses that use data-driven personalization see a 5-8x return on marketing investment⁽¹⁾. 

For marketers, this blog offers some interesting insights on data analytics and how it can better the customer experience.

4 Ways Data Analytics Can Help In Enhancing Customer Experience

1. Delivering relevant customer experiences through optimized content - 

With data coming in from your website, social media handles, and paid advertisements, marketers are learning more and more about their customers—their needs, environmental factors affecting their actions, their preferences for various content formats, and what counts as a "satisfied experience." This rich data can be analyzed and applied to deliver more relevant experiences. like—showing display ads on sites they visit, offering discounts when they are in the decision stage of the buyer journey, adapting keywords to current trends, anticipating demand during certain times and modifying content according to it, etc. For example, if you are using Google Analytics, you can find out visitors’ session duration on your website. It will give you an idea about how well the page is serving its purpose and if the visitors are finding it interesting or not. Then, optimize the content so that it meets the audience’s needs.

What it means for marketers – ROI. When content is optimized, audiences will respond better to the campaigns, leading to a better return on investment.

2. Learning about specific parameters and what works

With data analytics, marketers can gain insight into various factors, such as the best time to launch campaigns, high-performing keywords, channels that drive the most traffic to your website, and so on. These analytics come with visualizations, helping marketers make better sense. In turn, this helps customers access information when they are most receptive.

What it means for marketers – Better utilization of resources. Once you know what is clicking with audiences (such as social media channels, and the time of the day when they are most receptive) you will not be wasting your budget on things that don’t yield results.  

3. Setting realistic goals concerning the audience’s needs 

With data analytics, marketers can gain insight into various factors, such as the best time to launch campaigns, high-performing keywords, channels that drive the most traffic to your website, and so on. These analytics come with visualizations, helping marketers make better sense. In turn, this helps customers access information when they are most receptive.

What it means for marketers – Better utilization of resources. Once you know what is clicking with audiences (such as social media channels, and the time of the day when they are most receptive) you will not be wasting your budget on things that don’t yield results.  

4. Delivering the next level of experience using AI and ML 

Going beyond the manual analysis of the data and trends, data analytics tools such as Google Analytics are now employing artificial intelligence (AI) and machine learning (ML) for predictive analysis and to suggest the future course of action. In digital marketing, ML helps analyze large volumes of data, apply calculations to it, and predict behavior to derive better results. So, when website visitors start seeing ads that they have already interacted with before or ads for products that are lying idle in their cart, that is machine learning. This benefits the customers, as things stay on top of their minds and distractions don’t steer them away from their customer journey.

What it means for marketers – Machine learning helps to further reduce the manual intervention of marketers in analysis. ML helps to forecast demand and prepare marketers to strategize accordingly. Areas in which ML helps marketers are – understanding user behavior on a website, responding to customer queries through chatbots, sharing more personalized and optimized content with audiences, and even finding the right audiences for your campaigns as done by AI-based Facebook or Google Ads.

Today data is everything, and analysis of this data is the bigger and more critical requirement. Further, how this data is used to improve your customer’s experience is what will help you achieve your marketing goals and boost ROI.

Reference and Citation-

1. https://www.invespcro.com/blog/data-driven-marketing/