No business survives without attracting customers. The customer experience (CX), or how businesses engage with customers at every point of the buying journey, is just as necessary as a good product. The best products or intuitive business models may not save the company if customers stay away due to poor service or lack of connection. Customer experience can make or break a company in today’s challenging business environment. This tech blog discusses applying various types of customer analytics to improve CX.

It is challenging to satisfy customers in today’s digital age, though. Customers are more connected than ever before. Technology empowers them to research and avail themselves of self-service options. Easily accessible information makes them aware of what and where to buy and how much to pay. They dictate terms, and marketers have no choice but to pamper them.

Customer analytics makes explicit the minds and behaviours of their customers. Insights from various touch points help businesses to know the customer inside out. They could position their products and services to improve marketing, sales, and support. 

The following are the main types of analytics and how to use them to improve customer experience (CX).

1. Customer Experience Analytics

Customer experience analytics makes explicit “what happened” during the customer journey. It reveals how customers feel when they interact with the brand. 

Applying customer experience analytics requires tracking the following metrics:

• First Response Time (FRT). Compare FRT with optimal benchmark rates to determine if customers get timely service. Compare FRT rates at different touchpoints. For instance, if customers who call support have to wait for a long time before speaking to an agent, the CX degrades. The solution may be to improve self-service, increase the number of support agents, or redesign the system, to improve FRT. Calculating the FRT requires dividing the total of the first response times by the number of cases resolved. But such an average may give a partial picture. One or two instant responses may mask the remaining eight poor scores. 
• Total Time to Resolution (TTR). The TTR metric indicates how support agents perform and how quickly they resolve customer issues. Faster resolution time improves customer satisfaction. Service managers can compare TTR rates with benchmarks and develop improvement action plans. 
• Social media comments. The nature and tone of social media comments offer insights into customer experiences. Often, customers who leave sarcastic or caustic replies to a company’s social media post have a bad experience. Customers who follow the business, leave positive comments or write reviews are potential brand advocates. Smart, proactive businesses keep track of customer opinions and expectations. They adjust marketing efforts to target them with the right offers. 

Social media comments are qualitative data. Tracking and analysing such data needs a different approach to handling conventional metrics.

2. Customer Interaction Analytics 

Delivering a superior CX requires a complete understanding of the complex customer journey. A customer may go through various stages and multiple touchpoints in today’s digital age. They may abandon shopping carts at will and select a new product at odds with their purchase history. An analysis of customer-brand interactions makes explicit customer journeys better. Marketers may engage the customer on their terms and preferences.

The key tools for customer interaction analytics include:

• Product page traffic data. The traffic data to the product page especially offers insights into the early stages of the customer journey. High traffic also indicates the success of the digital marketing strategy.
• Shopping cart abandonment rate. When many customers abandon the shopping cart, it indicates something wrong. The issue could be technical, with the checkout page failing to load correctly. Or it could be product and campaign-related, with the customer not being satisfied with the product. The enterprise could tweak its e-commerce sites, product content, and positioning, to overcome such issues.

3. Customer Engagement Analytics 

Customers engage with a business in different ways. They may opt for point-of-sales channels, customer support portals, social media, and other channels. 

Customer engagement analytics distil data from the client’s journey to glean actionable insights. Businesses may use such insights to make timely interventions. The main channels that offer data for customer engagement analysis include:

• Customer feedback data. Feedback data from touchpoints and channels offers a holistic view of customer engagement. If customers do not give feedback, it could indicate poor engagement levels. Any kind of feedback, positive or negative, is good. Negative feedback is constructive criticism and offers valuable insights into customer preferences. Businesses may also get feedback through surveys that ask customers specific questions.
• Email marketing metrics. Metrics such as click rates and click-through rates show the effectiveness of marketing campaigns. Improving these metrics boosts customer engagement.
• Visual representation of the customer journey. Map the journey to identify hindrances. Use algorithms to forecast consumer behaviour. 

Customer engagement analytics enable the business to identify active and loyal customers. Marketers may launch tailored customer-centric marketing strategies focusing on such customers.

4. Customer Lifetime Analytics 

Successful businesses identify loyal customers and devise strategies to retain them. 

The key metric for customer lifetime analytics is the Customer Lifetime Value (CLTV). This metric predicts the revenue to expect from the customer throughout their stay with the business. Segmenting CLTV identifies valuable customers. 

A trend of CLTV decreasing over time indicates the business is struggling to retain good customers. Or they may not get repeat customers. The business could then probe the underlying reasons and make amends. Causes could include higher competition, changing customer preferences, or anything else. Regaining CLTV requires innovative efforts that enable the business to stand out from the competition.

The CLTV being lower than the customer acquisition cost indicates structural issues. The business spends more money to acquire the customer than the customer will generate in revenue. Such a situation suggests faulty product lines or marketing strategies. The business would need serious strategic interventions to stay afloat.

5. Customer Retention Analytics

Customer retention analytics is the logical progression from customer lifetime analytics.

Empirical evidence suggests that it costs 5x or more to acquire a new customer compared to retaining a customer. Customer retention analytics clarifies why customers remain with the company or leave. The sources of customer retention analytics include:

The top customer retention metric is LTV to CAC ratio. Comparing customer lifecycle value (LTV) to customer acquisition costs (CAC) makes it easy to determine the effort to put in for a customer. It does not make sense to woo a customer whose LTV is less than the CAC. Savvy marketers rank customers with the highest LTV to CAC ratio. 

Customer retention analytics identify high-value customers. Marketers may devise effective strategies to retain them and reduce churn. 

Businesses today rely on customer analytics to develop customer-centric strategies. They apply analytical insights to fix pricing and promotion and make predictions. The benefits include increased sales and revenue and lower customer acquisition costs. Improving customer retention rates by 45% boosts profits by 25% to 95%. A 2% increase in customer retention equals reducing costs by 10%.  

Here are further insights on how to manage digital experiences effectively.