Last Updated Jun 7, 2007 6:12 PM EDT
Customer data can provide a wealth of information about a business, the market, and the success of various programs and customer-facing activities. This information, in turn, can be leveraged to grow profitability and improve the business. To realize these benefits, customer data must be collected and analyzed, with the results and conclusions made readily available to the people who need them. Review customer data and focus on questions important to your business. Create a data analysis team to be responsible for identifying the data required, collecting it, analyzing it, and distributing the results.
Business intelligence software, which is designed to help companies gather, store, access, and analyze data about company operations, can be an essential part of this process. By increasing knowledge about factors affecting the business, business intelligence applications aid in making better business decisions. These tools can make valuable contributions across a wide array of business areas, including customer service, product profitability, market analysis, inventory and distribution analysis, and customer profiling.
Investigate the possibility of assembling or recruiting a research and analysis team from other departments. This approach has the added benefit of ensuring the data's availability and relevance to the company as a whole. Also, it is possible to speed up and simplify the process of data analysis with business intelligence software.
Manual techniques can be used to analyze and distribute customer information. Software does streamline and enhance the process, but it is not an absolute requirement. A good information network can ensure that the right employees have access to the same, up-to-date customer information.
Although it seems like a good idea, universal access can create its own problems. Too much data can overwhelm people. High-quality software offers selective reporting functions, allowing you to customize reports and tailor the appropriate data set for specific audiences. In this way, all employees can be provided the level of information that they need.
Proper security procedures must always be taken to protect confidential customer data. Many companies find that a secured intranet or other internal network provides useful and accessible storage of customer data. For example, restrict access to a secure area of the network by issuing passwords to authorized users.
To launch a customer data analysis program, create a network of qualified members of the company who know—or can determine—which business questions need to be answered and quantified with customer data. They should consider questions that are important to different groups across the company.
Next, form a data analysis team to determine the data which needs to be collected and analyzed in order to answer those business questions. The members of this team should have the expertise to determine the metrics important to the organization, and how they are created and applied. This team should also have responsibility for supplying information to the rest of the organization.
It is important to package the data and conclusions with the appropriate level of detail. Most people really just want the key points; users can drill down through the data if they require more detailed information. If other employees will be conducting further analysis of the data, ensure that the reports can be exported into tools such as spreadsheets, graphics, and slides. If time or budget is scarce, consider automating the more frequent processes and keep the level of analysis simple. Finally, decide how frequently analysis will be provided.
Maximize the utility of customer data by enabling employees to access reports and reference the latest statistics whenever they require. Make self-service reports available on a secure area of the company intranet. Store the reports in a format that can be imported easily to standard spreadsheet applications.
By allowing other members of the company to access to your reports and data, you will encourage helpful input. Create a discussion group for reviewing the results; use the feedback to refine the analysis process and metrics used.
The data becomes more valuable as you continue collecting and analyzing it. As the analysis base broadens, the conclusions you draw are better supported, and new trends may appear. It becomes possible to track not only how profitable the company has been, but also to project how profitable it can become in the future through best practice in data management. As the volume of data grows, storage and archiving requirements may become more complex; networked storage can increase flexibility and access.
Business intelligence tools can contribute significantly to the development of highly profitable marketing strategies. These software applications allow you to understand and analyze issues such as the effectiveness of marketing campaigns, or profit levels by customer. Business intelligence tools enable marketing teams to explore any combination of data—for example, revenues by customer, product, or region—making it easy to spot trends. It becomes possible to uncover significant, but often hidden, factors that have an impact on market share—such as price, product design, or packaging. These tools also provide access to transaction-level data, so that marketing teams can easily explore detailed information to determine the facts behind the trends.
Using customer data to perform strategic marketing analysis, marketing teams can analyze revenue by products, distribution channel, materials, and other factors. This makes it possible to see which products are driving sales and profitability in each market; thus, strategies can be focused accordingly. Strategic analysis can help identify and gauge the factors driving profitability, from the product attribute level up.
Marketing teams can evaluate the effectiveness of marketing campaigns by using customer data to perform tactical marketing analysis. The impact of marketing messages in different parts of the country can be explored by industry type, or by the buyers being targeted. By comparing response profiles against the profile of high-profit customers, messaging and media mixes can be adjusted for maximum impact.
Another important area of analysis concerns the customer portfolio. Valuable trends and conclusions can be derived from customer data by categorizing (also known as profiling) customers by factors such as profitability, and charting their lifetime value to date. For example, marketing managers can analyze customers by profitability tier, view trends in the profitability mix, and develop strategies to address unprofitable customers.
With the appropriate metrics, business intelligence software can identify the early warning signs of customer dissatisfaction—such as late shipments—as well as the reasons behind complaints, returns, and claims. With this information, customer service teams can increase retention rates for high-profit customers, spot early indicators of customer dissatisfaction, and maximize the profitability of each service relationship.
Customer retention depends on factors that cannot be tracked by mainstream indicators of customer dissatisfaction, such as declining revenue or slowing growth. With a customer data analysis program, however, companies can identify issues early and take action to retain customers for the long term.
Measuring and tracking on-time delivery statistics enables customer service teams to focus on one of the primary issues behind customer dissatisfaction—late shipments. Data and analysis can show performance by product line, by geographical area, or by individual customer. Using business intelligence software, customer service teams can easily identify delivery issues or patterns before they grow into larger problems.
Analyzing the reasons behind customer complaints enables customer service teams to be more responsive. Typically, most individual returns, claims, or complaints are based on a relatively small number of reasons. Establishing problem categories, rather than analyzing problems case by case, can allow customer service teams to quickly trace complaints to their source and identify trends. For example, a cluster of seemingly unrelated complaints may actually all stem from a problem related to a specific product, plant, or production run. By drawing this conclusion early, the problem can be addressed before more complaints occur and customer relationships become jeopardized.
Customer data analysis can also help companies measure the cost of service relationships. Service activities such as returns, exchanges, orders changes, and claims all have an impact on the bottom line. When the true cost of a service relationship is known, pricing can be adjusted to maintain or increase profitability.
In addition to customer service costs, data analysis can also be applied to other customer-facing functions, such as marketing programs or sales activities. With the right metrics, you can monitor the effectiveness of a customer marketing or communication program. For example, compare the cost per lead generated by two different sources—such as the Web versus direct mail. Or, analyze the success of cross-selling over the Web versus the call center. Calculate metrics and analyze trends in all channels and distribution mediums.
Customer data becomes valuable when it is both analyzed and used to address a specific question; raw data alone is of limited value. Software tools allow you to conduct analysis quickly and easily so as to provide relevant results to answer critical questions.
It may be easy to collect data on a specific activity, but if the activity is unimportant, then the effort is wasted. To identify the most useful data to collect, solicit input and obtain buy-in from customer-facing groups throughout the company.
When distributing customer data and analysis, ensure that the data is in a useful form for the recipients. Avoid wasting team members' time by forcing them to sift through masses of unstructured or irrelevant data to find the information they need.
The more you analyze data, the more valuable it becomes—different levels of detail are uncovered, trends can be spotted, and recurring problems will be identified. Ensure that the information is up to date by continuously collecting data, and avoid artificially restricting analysis to a limited set of questions.
Berry, Michael J.
Stone, Merlin, et al.
Marketing Research Association: www.mra-net.org