Analysis of customer

Analysis of customer
See also
Concept engineering Marketing research Market metrics Product research Marketing information system Economic feasibility Areas of marketing Promotional plan Social network marketing strategy

Analysis of customer is common method used in marketing for acquiring basic information about customer or groups of customers. It involves basic steps:

• identification of customers
• gathering information necessary to understand customer needs
• dividing customers in sub-groups with similar characteristics
• gathering data about customer needs and behaviours
• asking customers about their satisfaction from past interactions and purchases
• analysing profitability of customers or groups of customers

Gathering basic information about customer leads to creating successful and profitable CRM strategy (customer relationship management) and deploying more personalized marketing activities. Developed CRM helps with understanding customer behavior, acquiring potential clients and maximize customer value. To gain the needed information for analysis there are various methods:

• periodic survey – directly way to determine customer's approach to the company and satisfaction from the purchases
• number of lost customers – enable to see the size of rate
• analysis of consumer complaints – identify the issues with offered products.

Methods of analysis

ABC method

The concept is related to the income and it is based on Pareto's rule: 20% of customers generate 80% of total turnover achieved by company. Contribution to sales split customer into three categories:

• A – the most valuable for the company (the highest return)
• B – average contribution in amount of the turnover
• C – the lowest money spent

ABC method is based on the past data what due to the seasonality or other fluctuation can lead to mistakes in proper customer segmentation.

Customer Lifetime Value (CLV)

To analyze large database related to personal information and customer transaction, there are few tools which enable to assess the customer equity. One of the most popular is the concept presents value of all profits engendered by customers in the future – Customer Lifetime Value (CLV). The method has a lot of applications:

• targeting customers
• customer segmentation
• performance measurement
• product offering
• pricing

In literature there is six approaches distinguished in CLV model:

• RFM – based on Recency (the number of periods since the last purchase), Frequency (number of orders in the certain periods), and Monetary (the average money spent in the periods considered)
• Probability model – based on Pareto/NBD and Markov chains
• Econometric model – based on Pareto/NBD with including customer acquisition and retention
• Persistence model – based on behavior
• Computer Science model – based on theory (like utility theory)
• Diffusion/Growth model – based on customer equity

Data mining

Data mining is the automatic (through mathematical and statistical techniques) process of searching useful information in large database and creating novel and beneficial pattern. The knowledge gained through this advanced analysis is predominately used for forecasting and decision making due to possibility of identifying valuable customers and predicting their steps. The model is divided into two categories: descriptive (classification) and predictive (clustering). The descriptive approach means looking for a model that enables to describe data classes, while the clustering analysis involves data object within unknown class label.

Barriers

The implementation of customer analysis in practice is sometimes limited by important barriers. The value of taken activities depends on the availability and quality of data which are becoming critical aspect of model correctness. Deploying some technique requires data comes from different business area as well. Data ownership and responsibility are define by the organization what usually create a need to expand the accesses and shift the data privacy. Integration analytical model into company processes is successful when it meets with deep engagement of all employees. Complexibility and development of models are one of the most common reason for failure in analysis customer data. Understanding and deploying them are too difficult and time-consuming for company's managers. In addition, the result of analytics do not match often with the decision made by the management team what leads to the next investigation needed.

Examples of Analysis of customer

1. Demographic Analysis: Drawing conclusions about customers based on their age, gender, income, marital status, education, occupation, religion, and other demographic characteristics.
2. Psychographic Analysis: Drawing conclusions about customers based on their lifestyle, values, interests, and other psychological characteristics.
3. Geographic Analysis: Drawing conclusions about customers based on their location, such as a city, region, or country.
4. Behavioral Analysis: Drawing conclusions about customers based on their purchase patterns, brand loyalty, and other behavioral features.
5. Needs Analysis: Drawing conclusions about customers based on their specific needs, such as products that meet specific criteria.
6. Attitudinal Analysis: Drawing conclusions about customers based on their attitudes and opinions, such as attitudes towards certain products or services.
7. Segmentation Analysis: Drawing conclusions about customers based on segmenting them into different groups, such as age groups or income levels.

Advantages of Analysis of customer

Analysis of customer is a common method used in marketing for acquiring basic information about customer or groups of customers. It offers several advantages, including:

• Increasing customer understanding: Analysis of customer provides businesses with great insight into customer behavior, preferences, and spending habits. This helps businesses tailor their products and services to meet customer needs, leading to greater customer satisfaction.
• Improved targeting: Analysis of customer can help businesses target their campaigns to specific customer segments. This helps businesses increase the effectiveness of their campaigns and maximize the return on investment.
• Enhanced customer loyalty: Analysis of customer can help businesses identify customer loyalty and retention opportunities. This helps businesses increase customer loyalty and build relationships with their customers.
• Increased sales: By understanding customer needs and preferences, businesses can create personalized offers and promotions to drive sales. This helps businesses increase revenue and expand their customer base.

Limitations of Analysis of customer

Analysis of customer is a common method used in marketing for acquiring basic information about customer or groups of customers. It involves basic steps such as data collection, data analysis, and interpretation of the results. However, there are certain limitations associated with this approach which include:

• Poor data quality: If the data collected is of poor quality, it can lead to inaccurate results. Poor data quality can be caused by various factors such as sampling errors, incorrect data inputs, etc.
• Inability to capture dynamic changes: Analysis of customer is a static approach and cannot capture dynamic changes in customer behavior or preferences.
• Limited scope: The scope of analysis of customer is limited to the data collected and the questions asked. If the scope of the analysis is limited, the results may not be comprehensive.
• Subjectivity: As the interpretation of results is done by individuals, there is a risk of subjective biases that could affect the results.
• Time consuming: The process of analysis of customer is time consuming as it involves multiple steps such as data collection, data analysis, and interpretation of results.

Other approaches related to Analysis of customer

One-sentence introduction: There are several other approaches related to customer analysis.

• Market Segmentation: This approach involves dividing a larger market into smaller segments based on certain characteristics such as demographics, interests, income level, and so on. This can help marketers better understand their target customers and develop appropriate marketing strategies for each segment.
• Customer Profiling: This approach involves gathering data about customers and using it to create detailed profiles of customer groups. This can help marketers better understand customer behaviors and preferences, and develop effective marketing campaigns.
• Customer Journey Analysis: This approach involves tracking the journey a customer takes from initial contact with a brand to purchasing a product or service. It can provide valuable insights into customer behaviors and preferences, and help marketers identify potential areas of improvement.
• Customer Lifetime Value Analysis: This approach involves analyzing customer data to determine the value of each customer over time. This can help marketers identify customers with the highest potential for long-term profitability and develop appropriate strategies for each type of customer.
• Competitive Analysis: This approach involves analyzing competitors' customer data to gain insights into customer behavior and preferences. This can help marketers develop strategies to better meet customer needs and gain a competitive edge.

In summary, customer analysis is a useful tool for gaining insights into customer behaviors and preferences. There are several other approaches related to customer analysis including market segmentation, customer profiling, customer journey analysis, customer lifetime value analysis, and competitive analysis. These approaches can help marketers develop effective marketing strategies and gain a competitive edge.

References

• Bahari F., Sudheep E.M., (2015). An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour. Procedia Computer Science 46, 725 – 731.
• Bijmolt, T. H. A., Leeflang, P. S. H., Block, F., Eisenbeiss, M., Hardie, B. G. S., Lemmens, A., & Saffert, P. (2010). Analytics for Customer Engagement. Journal of Service Research, 13(3), 341-356.
• Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S. (2011). Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Computer Science, 3, 57-63.
• Radulescu V., Cetina I., (2012).Customer analysis, defining component of marketing audit. Procedia - Social and Behavioral Sciences 62, 308 – 312.

Footnotes

Author: Justyna Kurnik