A white paper on how companies should analyze customer data for better business information and how they can use this knowledge. In an increasingly competitive world, using your client database smartly to get 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 clients, business partners and potential customers. The challenge lies in finding a way to leverage the useful information contained in these large 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 the decision-making process. Business information consists of acquiring the data, filtering unimportant information, analyzing the data, assessing the situation, developing solutions, analyzing risks and then supporting the decisions made. This white paper describes the business intelligence process, some basic methods of data mining, and how to use business intelligence in your business. Expanding databases The first step towards getting business information is to start with a ‘clean’ database. Incomplete and inaccurate data can always be translated into wrong decisions about administration. Duplicate data is also a problem as they can incorrectly weigh management decisions to one side. Although a good quality database does not automatically lead to intelligent decision-making, it is a prerequisite for all types of analysis that attempt to induce intelligent management. We could draw an analogy with cooking where starting with the right ingredients does not guarantee that you bake a good cake, but there is very little chance that you bake a good cake if you start with the wrong set of ingredients. One of the primary reasons why companies are not fully aware of the potential competitive advantage they can get from their own databases is the lack of proper integration of data sets across departments. While all information may exist in the enterprise, they may remain elusive due to fragmentation of data across incompatible databases. Re-grouping all internal data into a single data set or a series of interconnected datasets can be the most useful step a company can take toward creating a solid foundation upon which to develop quality, business information. In some cases, data entry errors and / or missing data can also seriously degrade the quality of information that can be derived from enterprise databases. Sorting these issues can range from very simple fixes (such as matching one list to another) to more time-consuming processes (e.g. contacting all client companies to update contact information about people who work there). Ideally, all inaccuracies should be eliminated from the databases. However, time constraints and monetary constraints require you to remember how this database will be used. The level of accuracy required will vary greatly depending on the expected use of this data. Data cleaning and database integration can bring significant benefits to a business in the medium to long term. However, they are both extremely time-consuming activities and can create a significant strain on internal resources, making them difficult for a business to justify. Hiring a third party to do this job is often the best solution that will allow you to get valuable information without interrupting your day to day business. Data Extraction Analyzing the information that your company stores in all customer interactions can reveal a lot of noteworthy facts about your customers’ buying behavior, what motivates them and what can stop them from buying from you. It also provides a scientific method to monitor your business performance. When deciding to utilize information from a database, you are faced with a wide range of available techniques. Some of the more popular data recovery methods are described below: Statistical models
Basic statistical metrics – such as means, variances, and correlation coefficients – are useful in the early stages of data analysis to get an overall view of the data structure. By revealing simple connections within the data, statistical modeling can show which in-depth technique is likely to bring additional information relevant to your interests. clustering
Clustering is a technique that aggregates data according to a predetermined set of properties. It can be used to differentiate groups of customers who behave similarly to certain factors, for example, it can classify customer behavior by creditworthiness, income, age or any other factor of interest. CHAID analysis
The CHAID, which stands for Chi-square Automatic Interaction Detection, can be seen as the opposite of clustering, in the sense that the CHAID analysis starts with the aggregate database and then splits it according to the main variable until it achieves homogeneous sub – groups that cannot be further divided. A major advantage of this technique is that the results can be presented as an easy-to-read classification tree; each division in the tree is accredited to a single variable (e.g., credit rating, income, age, etc.). Prediction
Condition models – also known as prediction models – have proven to be very valuable in predicting which customers are most likely to buy a particular product based on a set of current customers. The results of such a model can be directly used 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. Choosing which method produces the best results can be daunting. As shown above, analysis tools can differ greatly in their approach to the problem. It is therefore very important for a company to consult someone with extensive experience in data mining processes before moving on to a business information project. The best method of 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 predefined questions. Predefined points of analysis are aimed at understanding certain types of behavior by analyzing relationships between various predetermined influencing factors. Eg. Would a predefined analysis of customer service Vs sales illustrate the effect of good and bad customer service on sales and answer questions such as how important customer service is to customers and how much it affects future sales. On the contrary, the goal of an open analysis is to discover trends that are not expected by ordinary immersion in daily business. Performing an open analysis internally is often impaired by the expectations of individuals working in the company. The techniques for analyzing data are complex. In order for your business to use the results of the data analysis, it is important that the results are not flooded with the complexity of the calculations, but delivered in a straightforward manner.
Intelligent 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 real business practice. Business intelligence refers not only to the data analysis itself, but also to how you relate the results of the data analysis to business decisions every day, and how to translate the recommended actions derived from the analysis into live campaigns. Therefore, it is important for you to ensure that the marketing department of your business interacts with data analysts constantly throughout the process. That way, once the data analysis is complete, the marketing staff will already be aligned with the issues facing the business and will be able to develop campaigns to leverage opportunities and strategies to quickly fix weaknesses. Detailed analysis of your customer data gives you insight into their needs and desires. The practice 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 allow for better customization of your products and services to your customers’ needs. An in-depth understanding of your customers provided through comprehensive data analytics also allows you to select and target better prospects, gain a higher response rate from marketing programs, and at the same time identify causes of customer wear and tear or create programs and services accordingly . Understanding how external market conditions affect your business allows you to respond quickly to future market changes. Finally, understanding customer behavior and the way they use your products and services will allow your business to improve its service to its current customer base as well as target new business more effectively. Visit http://www.accuracast-marketing-agency.co.uk/business-intelligence.shtml to learn more about getting business information.
About AccuraCast AccuraCast is an integrated marketing, business intelligence and data analytics company that gives SMEs a more accurate picture of their business environment through comprehensive data analytics, business intelligence and marketing consulting services. AccuraCast helps companies gain a better understanding of their customers and market their products and services more effectively. The company uses high-tech data analytics methods to research client databases smart and proven sales and marketing methods to reach the target markets. AccuraCast provides customer-specific marketing solutions and information based on tailor-made analysis of the databases so that companies can gain the necessary advantage in the competition. © AccuraCast Limited 2004