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A white paper on how companies should analyze customer data for better business intelligence, and how they can use that insight. In an increasingly competitive world, using your customer database wisely to gain a better understanding of your number one asset, your customers, can make or break your company’s success. Most companies use databases to store information about their current customers, past customers, business partners, and potential customers. The challenge lies in finding a way to harness the useful information contained in these high-volume databases to produce intelligent business solutions. Business intelligence (BI) refers to the process of increasing a company’s competitive advantage through the intelligent use of available data in decision making. Business intelligence is about getting the data, filtering out the unimportant information, analyzing the data, assessing the situation, developing solutions, analyzing risks, and then supporting the decisions made. This whitepaper describes the business intelligence process, some basic data mining methods, and how you can use business intelligence in your company. Database Improvement The first step in gaining business intelligence is to start with a ‘clean’ database. Incomplete and inaccurate data invariably translate into bad management decisions. Duplicate data is also a problem, as it can misbalance management decisions to one side. While a good quality database does not automatically lead to intelligent management decision making, it is a prerequisite for all types of analysis that attempt to achieve intelligent management. We could draw an analogy with cooking, where starting with the right ingredients doesn’t guarantee you’ll bake a good cake, but there’s very little chance you’ll bake a good cake if you start with the wrong set of ingredients. One of the main reasons why companies do not realize the potential competitive advantages they can gain from their own databases is the lack of proper integration of data sets between departments. While all information may reside within the enterprise, it may remain elusive due to data fragmentation across incompatible databases. Bundling all internal data into a single data set or a series of interconnected data sets could be the most useful step a company could take to provide a solid foundation on which to build quality business intelligence. In some cases, data entry errors and/or missing data can also severely affect the quality of information that can be derived from corporate databases. The classification of these problems can range from very simple solutions (for example, matching one list with another) to more time-consuming processes (for example, contacting all client companies to update the contact details of the people who they work there). Ideally, all inaccuracies should be removed from the databases. However, limited time and monetary constraints dictate that you must consider how this database will be used. The level of precision required will vary greatly depending on the intended use of that data. Data cleansing and database integration can provide significant advantages for a company in the medium and long term. However, both are time-consuming activities and can create a significant strain on internal resources, making it difficult for a company to justify them. Hiring a third party to do this work is often the best solution, as it allows you to gain valuable insights without interrupting your daily business activities. Data Mining Analyzing the information your business stores regarding all customer interactions can reveal a lot of remarkable insights about your customers’ buying behavior, what motivates them, and what might stop them from buying from you. It also provides a scientific method to monitor the performance of your business. When deciding to extract information from a database, one is faced with a large number of available techniques. Some of the more popular data mining methods are described below: Statistical models
Basic statistical measurements, such as means, variances, and correlation coefficients, are useful in the early stages of data analysis to gain an overview of the structure of the data. By revealing simple interrelationships within data, statistical modeling can show which in-depth technique is likely to yield the most information relevant to your interests. Group
Clustering is a technique that aggregates data according to a predetermined set of characteristics. It can be used to differentiate groups of customers who behave similarly on certain factors, for example, you can classify customer behaviors based on creditworthiness, income, age, or any other factor of interest. CHAID Analysis
CHAID, which stands for Chi-square Automatic Interaction Detection, can be seen as the opposite of clustering, in the sense that CHAID analysis starts with the overall database and then partitions it according to the most important variable until it achieves subdivisions. homogeneous. groups that cannot be further divided. A great advantage of this technique is that the results can be presented as an easy-to-read classification tree; each division in the tree is credited to a single variable (eg creditworthiness, income, age, etc.). propensity models
Propensity models, also known as predictive models, have proven to be very valuable in predicting which customers are most likely to buy a certain product based on a set of current customers. The results of such a model can be used directly to develop more appropriately targeted marketing campaigns. Other recognized techniques for extracting information from data sets are database segmentation, neural networks, and wavelet analysis, among others. It can be intimidating to choose which method will provide the best results. As shown above, analysis tools can differ greatly in their approach to the problem. Therefore, it is very important that a company consult someone with extensive experience in data mining processes before undertaking a business intelligence project. The best method to use will vary greatly depending on the time available to perform the analysis, what the results will be used for, and the type of data available for the analysis. An important point to consider is whether or not your analysis is guided by pre-defined questions. Predefined points of analysis aim to understand certain types of behaviors by analyzing the relationships between various predetermined influencers. For example, a predefined customer service vs. sales analysis would illustrate the effect of good and poor customer service on sales and answer questions like how important customer service is to customers and how much it influences future sales. Rather, the goal of an open analysis is to discover trends that are not anticipated by ordinary immersion in day-to-day business. Conducting an open analysis internally is often hampered by the expectations generated by people working within the company. The techniques used to analyze the data are complex. In order for your business to be able to use the results of data analysis, it is critical that the results are not clouded by the complexity of the calculations, but are delivered in a simple manner.
Smart Marketing It is important for a company to recognize that a good understanding of its customers is useful only to the extent that this knowledge can be translated into actual business practices. Business intelligence refers not only to data analysis itself, but also to how you relate the results of data analysis to daily business decisions and how you translate recommended actions derived from the analysis into live campaigns. Therefore, it is important that you ensure that your company’s marketing department interacts with data analysts constantly throughout the process. That way, when the data analysis is complete, marketing staff will already be attuned to the issues facing the business and can develop campaigns to capitalize on opportunities and strategies to fix weaknesses quickly and effectively. Detailed analysis of your customer data will provide you with insights into their needs and desires. The exercise will analyze and segment customer buying patterns and identify potential services that are in demand. You can use this information to shorten response times to market changes, which then allows for better alignment of your products and services with your customers’ needs. A deep understanding of your customers, delivered through comprehensive data analysis, will also enable you to choose and target better prospects, achieve a higher response rate from marketing programs, and at the same time identify the reasons for customer attrition. customer and create or modify programs and services accordingly. Understanding how external market conditions affect your business will allow you to react quickly to future changes in the market. Ultimately, understanding customer behavior and how they use your products and services will allow your business to better serve your existing customer base, as well as target new business more effectively. Visit http://www.accuracast-marketing-agency.co.uk/business-intelligence.shtml for more information on getting business intelligence.

About AccuraCast AccuraCast is an integrated marketing, business intelligence and data analytics agency providing UK small and medium-sized businesses with a more accurate picture of their business environment through comprehensive data analytics, business intelligence and consultancy services. of marketing. AccuraCast helps companies better understand their customers and market their products and services more effectively. The company uses high-tech data analysis methodologies to intelligently research customer databases and proven sales and marketing methods to reach target markets. AccuraCast delivers customer-specific marketing insights and solutions based on personalized database analysis, enabling businesses to gain the necessary edge over the competition. © AccuraCast Limited 2004