Customer analytics is the systematic examination of consumer information and behaviour to reveal, attract and retain the most prominent customers. It is a process where the data from customer behaviour is used make key changes in business through market segmentation and predictive analytics. This data is then used to determine marketing strategies and customer relationship management ideas.
Metrics used in customer analytics:
Customer Experience (CX) analytics is typically measured using 10 different types of metrics.
1. Net Promoter
2. Customer Satisfaction
3. Customer Effort
4. Resolution Time
5. Consumer Lifetime Value
7. Churn Rate
8. Social Sentiment Analysis
9. Reviews and Customer Ratings
10. Referral Percentage
Net Promoter Score:
Net Promoter Score (NPS) rates the probability of a product to be recommended by one person to another. These ratings are given on a scale of 1 to 10. A consumer who’s probable rating is a 9 or 10 is considered a promoter. Passives are people who score 7 or 8. Detractors are those with a score of from 0 to 6. Net promoter score is calculated by subtracting the percentage of detractors from the percentage of promoters (POP–POD). This will help us to determine the health of our product.
Customer Satisfaction Score (CSAT):
CSAT is measured with numerical values to evaluate the satisfaction of a customer with a an interaction. It is also widely used to determine the satisfaction of a specific service or product. Customer satisfaction score can be derived by using the following formula:
CSAT = Total number satisfied customers / Total number of responses X 100.
Customer Effort Score:
This score defines the values to mark and track specific areas for improving a business. CES is the amount of effort that is put by a customer in order to resolve a problem with a product or service. Customer effort score can be measured by dividing the sum of all individual customer effort scores by the number of customers who provided a response.
Average Resolution time is the amount of time taken by a customer support executive from opening the first query mail from a customer regarding an issue till the final mail sent to her/him signifying the resolution of the issue.
Consumer Lifetime Value:
CLTV is calculated by the amount of total mone
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Average order value X Average repeat purchase X Average time customers stay.
Consumer retention defines the ability of an organization to maintain its current consumer rate after their acquisition in the beginning. Higher retention rates indicate customers repeating the purchase of a certain service or product. This would clearly indicate the growth of a business. Retention = (End customers) – (Added Customer) / (Start customers) X 100.
Customer churn rate defines the end of the relationship with a customer. Either they no longer use a service or a product or they formally end a service agreement. Higher level of churn rate denotes consumers’ dissatisfaction over a product or service. This would clearly indicate a decreased business growth.
Churn= Start customers – End customers / Start customers X 100.
Social Sentiment Analysis:
Social sentiment acknowledges the extensive emotional response from consumers to a business or an individual on social media. It is impossible to measure it by numerical values. Instead, social sentiment analysis attempts to reveal the perception of a brand by analysing the context and tone of the content.
Reviews and Customer Ratings:
Reviews and ratings provide a collection of customer opinions over a product or service. Ratings are given through numerical values for easy comparability and reviews are asked from customers in writing to understand sentiment.
Referral rates denotes the number of customers who are entering into business from a specified referral program. These rates might vary across different industries. Often, referral ratings are associated with CSAT and Retention.
Referral ratings = No-of-referred purchases / No-of-total purchases.
Use Cases of Customer Analytics:
The value of these analytical tools can be best identified by explaining the main use cases of today’s business world. And further define these use cases with context to the most applicable industries. Below are a few simple examples of those use cases.
1. Churn Prevention
Key industries: Telecom, Insurance, Retail, Banking, and Automotive.
2. Customer Lifetime Value
Key industries: Utilities, Telecom, Banking, Insurance, and Retail.
3. Customer Segmentation
Key industries: Pharmaceutical, Life sciences, Automotive, Banking, Insurance, Retail, Utilities, and Telecom.
4. Sentiment Analysis
Key industries: Education, Insurance, Retail, Pharmaceutical, and Telecom.
Major points to keep in mind:
Below are the main points that will help you to build a strong customer analytics system for your business.
1. Know your objective – Define the end goal and work backwards to understand what’s needed to achieve it. This involves drafting and answering a set of questions to mark milestones and requirements
2. Track your metrics – To understand if the strategy is on the right track, monitor the metrics shortlisted. This would highlight the key performance indicators and where they are at any given point in time.
3. Data Analysis – Visualize your data the right way for easy consumption and for actionable insights. This will help figure out if the business and marketing strategies are on the right track.
4. Model Evaluation – Once the model has been built evaluate how well the model can perform on unseen data. Metrics like overall accuracy, recall, speciality and the under curve area are computed from confusion matrix. If the model checks are not performed as expected, it is more likely the model coefficients are not acceptable.
5. Taking Action – After a time period customer analytics system must include new customer profiles, new segments, predictions and response values. It is important that an analyst must be able to identify when the predictive tool is no longer accurate or the when marketing strategies are no longer effective so that proper can be taken to modify the system.
6. Automation – Once the models are performing well it is suggested that the system be automated. This can be done by integrating the customer data, process of business, analytic techniques and marketing strategies. One of the main benefits of automation is that it allows the analyst to set up triggers that alerts when unacceptable changes in the model accuracy occurs.
Also read: How to set Google AdWords-step by step
Use Cases of Customer Analytics in Banking Sector:
There are several analytical tools that can help banks to extend the customers as groups or individuals. Here are few of them:
1. Customer Profitability – Helps a bank in assessing customers’ current economic value.
2. Severity and Likelihood – Helps a bank to determine the likelihood of a customer defaulting on a loan in a certain time period.
3. Customer Segmentation – helps a bank to pinpoint groups of customers who are internally similar and externally dissimilar.
4. Customer relationship development – Helps a bank to identify customers for suitable offers.
Use Cases of Customer Analytics in Insurance:
Here are the few examples of major use cases of customer analytics in insurance sector.
1. Detection of fraudulent claims – Insurance companies face major losses every year because of fraudulent claims. Now a days improvements in data science have made it easier to detect fraudulent claims and suspicious activities.
2. Real-time risk mitigation – The very nature of business in the insurance industry involves risk. Analytical models can be used to perform real-time risk analysis which allows companies to take quick action in a volatile risk circumstance.
3. Life time value prediction – CLV is predicted with the help of customer behaviour data to determine the company’s share of their wallet. These models helps to predict customers’ behaviour in maintenance or surrendering of a policy.
Use cases of Customer Analytics in FMCG:
A formulated use of analytical models would help FMCG organizations cope with trends that impacting them.
1. Commodity price volatility – Analytics helps to acknowledge the furious changes in the resource market volatility and to make a better use of critical resources that are in production.
2. Unfulfilled economic recovery – Analytics backs the changes in the value by finding the key price points in the market, describing customer segments, creating new techniques in pricing based on competitive intelligence in production and logistics to reduce prices.
3. Health and responsibility as the new basis of brand loyalty – Nowadays companies face a challenge in aligning offers and activities based on customers’ interests and values. Big data and analytics helps an organization to have a better understanding of customer sentiment and preferences.