A white paper on how companies should analyze customer data for better business intelligence and how they can use that knowledge. In an increasingly competitive world, using your customer database wisely to better understand 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 smart 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 the unimportant information, analyzing the data, assessing the situation, developing solutions, analyzing the risks, and then supporting the decisions made. This white paper describes the business intelligence process, some basic data mining methods, and how you can use business intelligence in your business. Database improvement The first step to getting business intelligence is to start with a “clean” database. Incomplete and inaccurate data invariably leads to incorrect management decisions. Duplicate data is also a problem, as it can wrongly weigh management decisions aside. While a good quality database does not automatically lead to smart management decisions, it is a prerequisite for all types of analytics attempting to get smart management. We could draw an analogy to cooking, where starting with the right ingredients doesn’t guarantee you’ll bake a good cake, but there is very little chance that you will bake a good cake if you start with the wrong set of ingredients. One of the main reasons that companies do not fully realize the potential competitive advantages they can gain from their own databases is the lack of proper integration of data sets between departments. Although all information can reside within the company, it can still be difficult to reach due to data fragmentation in incompatible databases. Regrouping 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 seriously alter the quality of information that can be derived from corporate databases. The classification of these problems can vary from very simple solutions (for example, matching one list with another) to processes that require more time (for example, contacting all client companies to update the contact details of the people who work there ). Ideally, all inaccuracies should be removed from the databases. However, time and monetary constraints require that you consider how this database will be used. The level of precision required will vary greatly depending on the expected use of that data. Data cleansing and database integration can provide significant benefits for a business in the medium and long term. However, both are time-consuming activities and can create 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 obtain valuable information without interrupting your daily business activities. Data Mining Analyzing the information your business stores regarding all customer interactions can reveal many remarkable facts about your customers’ buying behavior, what motivates them, and what could cause them to stop buying from you. It also provides a scientific method for monitoring your business performance. When deciding to extract information from a database, one is faced with a large number of techniques available. 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 get an overview of the structure of the data. By revealing simple interrelationships within the data, statistical modeling can show which in-depth technique is likely to provide 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 rank customer behaviors based on their 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 that the CHAID analysis starts with the general database and then divides it according to the most important variable until it achieves sub- groups that cannot be divided further. One of the main advantages 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 invaluable 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 wave analysis, among others. It can be intimidating to choose which method will provide the best results. As shown above, the analysis tools can differ greatly in their approach to the problem. Therefore, it is very important that a company consult with someone with extensive experience in data mining processes before going ahead with 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 your analysis is guided by predefined questions or not. The predefined analysis points are intended to understand certain types of behaviors by analyzing the relationships between various predetermined influencers. For example, a predefined analysis of customer service Vs sales would illustrate the effect of good and bad customer service on sales, answering questions such as how important customer service is to customers and how much it influences future sales. Rather, the goal of open analysis is to discover trends that are not anticipated by ordinary immersion in the day-to-day business. Conducting an open analysis internally is often hampered by the expectations generated by the people who work within the company. The techniques used to analyze the data are complex. In order for your business to use the results of data analysis, it is essential that the results are not clouded by the complexity of the calculations, but that they are delivered easily.
Smart Marketing It is important for a business to recognize that a good understanding of its customers is only useful 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 it relates the results of data analysis to daily business decisions and how it translates the 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 entire process. That way, when the data analysis is complete, the marketing staff will already be in tune with the issues facing the business and can develop campaigns to capitalize on opportunities and strategies to repair weaknesses quickly and effectively. Detailed analysis of your customer data will provide you with insight into their needs and wants. The exercise will analyze and segment customer purchasing patterns and identify potential services that are in demand. You can use this information to shorten response times to market changes, allowing you to better align your products and services with the needs of your customers. A deep understanding of your customers, provided through comprehensive data analysis, will also allow you to choose and target better prospects, achieve a higher response rate from marketing programs, and at the same time identify the reasons for the loss. customers 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. Lastly, understanding customer behavior and how they use your products and services will allow your business to better serve its current 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 obtaining 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 consulting services from 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 investigate customer databases and proven sales and marketing methods to reach target markets. AccuraCast offers customer-specific information and marketing solutions based on customized database analytics, enabling companies to gain the necessary edge over the competition. © AccuraCast Limited 2004