Worldwide, businesses need help managing the rapid changes in consumer expectations. According to a recent Harris Interactive survey on the current state of customer service in the U.S., 68% of consumers say that companies and brands must find ways to offer more customized interactions to increase customer satisfaction. And within persistent economic uncertainty, competition for consumers — essentially those driving growth — has never been fiercer.
As a result, businesses that ignore this demand for a more personalized experience will be at a significant disadvantage compared with their peers. To achieve this goal, businesses need to analyze the data available at their disposal. Unfortunately, most of them are underutilizing or ignoring a vast amount of valuable information available at contact centers. This blog will focus on how this data can be utilized to enhance customer experience.
Customer Analysis and Its Importance
Customer acquisition costs are five times more than retaining existing ones. Additionally, you must uncover the reasons behind customer loss and focus on retaining them. Irrespective of the time spent on product development and testing, the product’s effectiveness is determined when it is out in the market. Hence, businesses are realizing the benefits of applying customer analysis in understanding their customers’ behavior and catering to their requirements.
Customer analysis refers to analyzing customer data to derive valuable insights and enhance decision-making. The process uses marketing analysis techniques to gain insights about existing customers to gain new customers. Quantitative and qualitative data helps analyze customers and divide them into segments based on shared characteristics, customer pain points, and understanding how products or services solve the customers’ needs. This allows businesses to create personalized experiences based on buying behavior, gender, age, interest, and beyond and broadly involves five steps.
Step 1: Customer Segmentation
Segmenting your customers is a crucial aspect of customer analysis. They can be broadly divided into these segments.
- Geographic segmentation: Creating different groups of customers based on geographic boundaries.
- Demographic segmentation: Dividing the market through other variables, such as age, gender, income, etc.
- Behavioral segmentation: Focusing on specific reactions from the customers and how they undertake their buying journey.
- Psychographic segmentation: Grouping the target audience based on their behavior, lifestyle, attitudes, and interests.
Step 2: Data Collection
Voice of the customer (VoC) helps you to understand the customers’ view of the brand and its product and service offerings. These include customer forms, loyalty programs, surveys, and service agreements. Other data collection forms include transaction records, cookies, website visits, social media, and keyword trackers.
Step 3: Identifying Pain Points
Businesses need to be watchful of customer dissatisfaction and pain points. Dissatisfied customers can substantially damage the reputation of a brand. Broadly, four customer pain points need to be addressed.
- Financial pain points: These are the most critical of all customer pain points and occur when customers feel that the price of a product/service is exorbitant. Customers love choices and are always looking for cost-effective products or solutions that offer them value for money.
- Process pain points: They may refer to operational inefficiencies along the customer journey. These not only affect the customers, but they also impact your support team. Businesses must identify these bottlenecks to improve the customer experience and enhance agent productivity.
- Support pain points: They are often internal issues restricting businesses from resolving customer pain points quickly or effectively. Customer support plays a pivotal role in making the customer journey seamless. For any customer issues, support agents must ensure that each interaction with the team or a chatbot results in customer delight.
- Productivity pain points: These pain points occur when support agents reroute customer queries to the wrong team. Your customers expect a seamless resolution from the support team when they raise a query. However, productivity pain points may decrease agent productivity and diminish the efforts behind the customer services offered. As a result of these internal inefficiencies, customers may terminate their relationship with the brand or may have a higher tendency to churn.
Step 4: Defining Buyer Personas
Buyer personas allow you to create personas of customers based on their buying trends and motivations. These can be based on the customer demographics, such as age, gender, education, and income levels. They can also be based on product use, messaging, geographic area, pain points, interests, and email preferences.
Step 5: Defining Product Fit
The last step in the customer analysis puzzle is the market fit. This helps you to uncover challenges faced by various customer groups and how your product or service helped overcome them. If you are able to solve the problems of some personas but not the rest, the latter could be a better product-market fit. This can be accomplished in three steps.
- Market hypothesis: Define or refine your market and target audience hypothesis.
- Product hypothesis: Define your product hypotheses that fit your marketing hypotheses. Build a version of the product that reflect those hypotheses.
- Evaluate hypothesis: The final hypothesis is the culmination of the above two steps. If the product does not generate value for the customers, you must return to the first step and refine/redefine your strategies based on the findings.
The Way Forward
Customer analysis is a powerful tool for businesses to generate valuable insights into their customer base and improve overall performance. They can make informed decisions, enhance customer experiences, optimize marketing strategies, and drive growth with the help of data and analytics.
However, it is crucial to approach customer analysis with a clear strategy, a solid data foundation, and a commitment to data privacy and ethical considerations. By embracing customer analysis and leveraging it effectively, businesses can unlock a competitive edge and thrive in today’s data-driven landscape.