What Is Customer Insight?

The commonly used concepts of customer data collection and analysis, customer data mining, and quantitative marketing are all part of customerinsight in Accenture's value framework.

Customer Insight

Right!

The commonly used concepts of customer data collection and analysis, customer data mining, and quantitative marketing are all part of customerinsight in Accenture's value framework.

Such a generalization has been recognized by more and more companies and experts. Customer insight does not refer to the ability of a customer service person or customer contact person to personally understand and understand the customer. It refers to the comprehensive grasp of customer data at the enterprise or department level and the effective application in marketing and customer interaction.

Chinese name

Customer Insight

Foreign name

customerinsight

Part 1

Customer data management

Part 2

Customer Analysis

Part 3

Insight application

The customer insight framework proposed by Accenture is mainly composed of three parts: customer data management, customer analysis and insight application.

Work on customer data management includes data extraction, conversion and upload, data quality management, customer privacy management, enrichment of customer and business data, data comparison and correlation, and establishment of a unified customer view.

Work on customer segmentation includes strategic level segmentation, customer value analysis, tactical product or campaign specific segmentation, segmentation-based offers and customer experience development.

In the modeling work, there are predictive models to support customer acquisition, customer retention and cross / up-selling, dynamic model building, and propensity and responsive models that include continuous learning capabilities.

Work in battle management includes customer and channel integration, workflow and business process integration, closed-loop marketing, process and organization models, and campaign quality management.

Work on customer interaction includes priority list management, intelligent recommendation for production and script development, and customer experience management corresponding to customers and agents.

Customer assets are the most precious and often the least utilized assets for an enterprise. In order to generate more value for customers, enterprises should learn to take a more holistic view of customers, which requires that businesses can surpass business and functions. Limitations of the department, the establishment of overall planning and operation of customer insights, and the ability to apply sophisticated segmentation and forecasting methods to find regular knowledge of large amounts of customer data, and to use customer knowledge to drive customer insights Strive to serve marketing and customer interaction.

The historical evolution of modern marketing and services reflects an effort from mass marketing to one-to-one marketing. We can use Figure 2. From sweeping marketing, to differentiated marketing, to so-called cell marketing, the maturity of modern markets and technological advancements require companies to minimize customer marketing and service units, provide at least differentiation as much as possible, and ideally be personalized Communication, products and services.

Using customer insights, we can answer a range of questions that companies are interested in, but often difficult to answer accurately.

Questions in marketing include: How to apply price strategies to attract high-end customers? How to adjust prices to target customers in different regions? How to respond to competitive prices? How to route incoming calls and handle each customer contact? How should customers sell upwards? Questions in product management include: How to maximize the benefits of product launch? Which product will most likely be brought to market quickly and profitably? What kind of customers can be attracted by newly developed products?

Questions in advertising and promotion include: how to allocate budget between mass marketing and direct marketing? Which promotion combination has high return value? How to design a marketing campaign for high-lifetime customers? What kind of product subsidies can be provided to the appropriate segment customers?

Questions in the sales channel include: What kind of channel strategy can reach high-end customers? Which channel provides the highest return on investment? Which store location attracts the target market? What product strategy should the self-operated store adopt? Questions in customer service include: Do customer loyalty programs need to be implemented to reduce churn? How to deal with increasing call center traffic? What are the main reasons for customers to come? How to better improve service? How to cross-sell calls for various reasons? How to reduce the bad debt rate by focusing on special customer groups?

These questions revolve around marketing and customer service. In fact, the cultivation of customer insight can also help companies answer a series of questions such as technology configuration, alliance establishment, supply chain management, and resource allocation.

Why do customer analysis often fail to achieve the desired results?

In the process of observing many enterprises in recent years, I often notice two contradictory phenomena. On the one hand, many companies have spent a lot of money to establish data centers, data warehouses, and data exploration. At least I can come up with a lot of reports and charts. Full-time staff often talk about the means used. On the other hand, these enterprise marketing departments or customer service departments continue to be overwhelmed by some of the issues we have listed above. They often think that many data investments are a waste of money and cannot solve practical problems at all. This may contain a variety of misunderstandings of knowledge and operation. such as:

The complexity of correlating the results of market segmentation with overall business strategies and multi-component strategies has led to emptiness of data analysis.

Companies often lack overall customer attempts or choose the wrong analytical techniques to understand the market, customers, and business and financial implications.

The relative isolation of departments results in data being sectoral rather than enterprise-level. The analysis is IT, not business-oriented.

· I don't know how to balance the dimension selection and segmentation granularity

The nature of customer insights cannot be understood, and they are often satisfied with short-term, incremental changes rather than identifying the best market growth and positioning strategies through customer insights.

· Lack of support in processes and human resources, and generally poor execution capabilities in the application of insight.

At the same time, the definition of customer insight is unclear, and the capability and impossible of data analysis and data mining are not understood. It is often a common practice to ask for the role of data at the wrong time and place. Reality. When we recently implemented a marketing customer acquisition project using direct replication, some people constantly thought that we needed to do a comprehensive data mining of the existing data, and some people still thought that since we have performed data analysis on customer acquisition, we can solve customers Reserved for analysis questions. A lot of clarification work in this regard