Customer Analytics

Customer Analytics Definition

Customer analytics refers to the technologies and techniques by which customer behavior data is used to provide organizations customer insight and inform business decisions. Such techniques include predictive analytics, data visualization, information management, and segmentation, which are often used in direct marketing, site selection, and customer relationship management endeavors.

Diagram depicts the components that are involved in collecting customer data, and the types of results to come out of it.
FAQs

What Are Customer Analytics?

The primary goal of customer data analytics is to improve the overall customer experience. With the use of various customer analytics solutions and customer analytics software, businesses can collect and gain insight from customer data, segment customers into groups based on common characteristics, create personalized interactions between the brand and customers, predict future customer behavior, and ultimately make business decisions that create a successful campaign and a satisfying customer journey analytics solution that will retain and attract customers.

The process of measuring and analyzing the nuances of the customer experience, an increasingly important factor in business decisions, starts with the customer’s awareness of their need for product or service, then the means by which the customer researches businesses and products, and finally through the sales funnel to purchasing. Touchpoints throughout this journey include customer service, marketing, sales, and social media and review website interactions.

Key Performance Indicators that businesses should track for customer acquisition data analytics include: 

  • Social listening -- tracking comments, likes, shares via Artificial Intelligence 
  • Opens, Views, Clicks, Traffic, Click-Through-Rates, time to launch, single customer visits, and sources of traffic via A/B testing or tools such as Google Analytics 
  • Bounce rate and pages per session once a customer is interacting with your company’s website 
  • Session recording software 
  • Purchase/activity conversions
  • Rate of checkout abandonment 
  • Percentage of customers who require customer support
  • Net Promoter Score -- the likelihood that a customer will recommend a company to someone else
  • Upsell/Cross-Sell Rate -- the rate at which customers continue to purchase and/or upgrade 

The wide range of tools used in customer journey analytics include customer management relationship platforms, customer service management software, sales platforms, Google Analytics, social media tools, email marketing platforms, campaign analytics software, data collection and management platforms, content management systems, and proprietary map metrics.

Customer Analytics Trends

Customer experience analytics is on track to become a more pivotal factor in securing purchases than both price and product. In order to cope with the vast quantities of customer data being created each day, maximize their understanding of customer behavior patterns, interests, and locations, maintain relevance, and ultimately gain a competitive advantage, it is crucial for business to keep up with the latest predictive customer analytics trends:

  • Customer Data Sources: a combination of emerging customer data sources, such as voice enabled smart devices, in-home automation, wearables, and social media journeys, reveals significant and valuable details about a customer’s lifestyle
  • Artificial Intelligence: AI and Machine Learning are evolving to become more human-centric, with AI enabled customer behavior analytics systems projected to possess a grasp of ethics and empathy in the near future
  • Cloud Analytics: the shift from traditional, on-premise analytics models to cloud models is growing
  • End-to-End Integration: end-to-end analytics processes combines customer data with relevant, real-time data from marketing, sales, customer service, and external social collaborations, which results in greater customer insights and analytics

Use of Data Analytics in Telecommunications

As data science and AI become more mainstream, telecom service providers are applying more advanced, telecom customer analytics to areas such as customer value management in order to address customer retention in the telecom industry. 

The use of data analytics in telecommunications can help with identifying factors that impact customer experiences and creating personalized, holistic actionable strategies. Agents, bots, and other self-service tools have access to rich, real-time, contextual data, such as a customer’s location, time of day, type of use, and recent service issues, which aids in developing personalized customer relationships.

Telecom service providers require sophisticated big data analytics and Business Intelligence tools to perform activities such as data mining, predictive modeling, real-time customer analytics, and data optimization, which are used most commonly in the following big data analytics use cases in telecom: 

  • Customer Sentiment Analysis: Software collects feedback from various social media sources in order to create an assessment of a customer’s positive or negative reaction to a product or service. Text analysis techniques aggregate data, revealing recent trends in customer issues.
  • Customer Churn Prevention: Smart data platforms can combine customer transaction data and data from real-time communication streams reveal actionable insights on customer issues and feelings, facilitating immediate responses, and customer loyalty and satisfaction. 
  • Customer Lifetime Value Analytics: Customer lifetime value is the prediction of the net profit and revenue generated by the entire future relationship with a customer. Based on customer purchasing behavior, activity, services utilized, and average customer value, the CLV model can measure the potential profitability of customer segments and avoid profit loss. 
  • Customer Segmentation: Customer value segmentation, behaviour segmentation, lifecycle segmentation, and migration segmentation help telecom companies precisely segment the market and target specific content for specific groups, based on the retention and value drivers for each customer.

Does OmniSci Offer Big Data Customer Analytics Tools?

With OmniSci's advanced analytics for telecom, analysts can rapidly visualize billions of records to spot anomalies and immediately drill down to specific network issues that can cause customer churn. 

The scale of telecom data is too vast for existing telecom analytics tools to handle, but with OmniSci’s millisecond querying, filtering, and visualization of massive telco data sets, big data customer analytics teams can visualize and analyze data relating to demographics, usage, connectivity, network performance, and reliability in real-time to better understand customer behavior and ultimately enhance customer experience and reduce customer churn rates.