How Customer Analytics Helps to Optimize Buyer Experience

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Customer Analytics

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Customers always want to be treated well. So, no matter which industry you belong to, their satisfaction is vital if you want them to remain loyal to your business.

Research from PwC suggests that 32% of customers leave a brand after a single bad experience.

Similarly, 86% of buyers are ready to pay more for an excellent customer experience.

While this is a challenge, this is a priority too. Hence, every business is using customer analytics to optimize buyer experience.

Here are some of the very effective customer analytics ideas companies are using to optimize their buyer experience:

Data Unification

Data unification is highly recommended for any behavioral analytics. It helps companies create a single customer view. They can understand and better engage with customers by knowing them and their point of interest.

Data unification involves unifying customer data across all your internal systems. If your business platform is large, the process is not easy to carry out. Additionally, it could be daunting, time-consuming, and costly.

The unification tools from a new generation of data make this task super straightforward. They are capable of pulling together dozens of data sources that encompass millions of data points. Also, the results can be expected within weeks, instead of months.

Several companies are using data unification to omit conflicting customer data, resulting in improved data accuracy and customer service.

Churn Out Info from Data

Customers start producing data from the moment they enter the website, like clicking, navigation, and browsing activities.

Everything is absorbed by websites and software and stored in their database. Analytics software uses this data to make accurate predictions.

Tools like Google Analytics 360 give you deep insights by analyzing the data it gets. For example, from which browsers and operating systems the customers are coming from, what customers like the most, what they are disliking, and the parts of the sales funnel that are turning the customers off are some of the great insights for customer optimization.

Similarly, tools like Finteza, offer an in-depth analysis of data across 15 parameters. Moreover, you can filter the collected information via several different variables.

For example, you can see how many visitors registered on your websites using Google search. Finteza also offers traffic quality measurement and detects 12 types of low-quality traffic.

With real-time traffic quality measurement, you can filter bot visits and fake conversions so that you can stop paying for traffic that won’t convert.

Real-time Insight Delivery

Artificial intelligence (AI) is arguably an incredible driver of the digital customer experience (DCX).

Gartner found in 2019 that enterprise use of AI technology grew 270% over the last four years. Also, AI is expected to create nearly $4 trillion in business value by 2022.

But, to make AI impactful for CX, actionable insights have to be shared in real-time. These insights aren’t accessible to CX or marketing teams in a consolidated form they can use directly.

That is why AI performs analysis on larger data sets to determine meaningful relationships within every data. It can even predict the likelihood of future behaviors with high accuracy.

AI analyses and reveals insights into microseconds. As a result, analytics teams can get answers to internal requests faster than ever before.

Big Data Analytics

Big data is the new trend in the industry that is peaking fast. At present, the big data industry is worth $189 Billion which is  $20 Billion more than the last year. Also, it is expected to reach $247 Billion by 2022.

Indeed, companies are finding big data helpful in making healthy business decisions.

Here are some of the ways big data analytics is helping companies:

  • Review Analysis: Reviews from different platforms like Google, App Store, and Yelp are collected to get insights about their issues and experience. Automated software is used to analyze all the submitted reviews about any particular product or service. Then it classifies reviews as positive, negative, and neutral.
  • Personalized Customer Service: Big data analytics tools are programmed to get an insight into the requirements and preferences of each customer. It helps create separate and different strategies for each client to improve customer satisfaction.
  • Identifying Weak Metrics: There is a lot of information about the customer experience that big data management metrics contain. AHT (average handling time) and FCR (first contact resolution) are to name a few. It is easy to calculate customer response time across multiple channels, using big data. Knowing the types of customer inquiries, or weak and strong spots in customer service procedures will help agents improve their problem-solving efficiency.
  • Agent analysis: Some of the support agents might lack in their performance. For example, response time average, hold time, and abandonment rate are significant factors to influence customer service productivity. Here, big data analytics help in finding those agents.
  • Net Promoter Score (NPS): NPS, helps analyze and measure customer experience and satisfaction. Usually, it is a numerical score (between 1-10) calculated by customer recommendation. It could be about any particular brand service or product. 

Final Thoughts

The increasing number of new participants in the industry have led to fierce business rivalry. This has compelled business owners to focus largely on improving customer experience and finding ways for it.

Customer analytics along with AI and big data has emerged as a solution to the companies to build loyal customers. With the above-mentioned analytics ideas, collecting data, and analyzing them has become much easier.

There are various advanced customer analytics tools to reach the optimum level of CX. They automatically collect customer experiences across a range of touchpoints, like email, online reviews, and comparison site feedback. The analysis performed on the collected data helps in spotting the areas for improvement for better CX.

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