Actionable Knowledge—Return on Investment
It is important to remind you at this point that an asset retains its value only if we do something with it. In the world of BI, some investment is probably required to build the environment where data can be turned into knowledge, but the real benefit occurs when that knowledge is actionable. That means that an organization cannot just provide for the information factory; it must also have some methods for extracting value from that knowledge.
This is not a technical issue—it is an organizational one. To have identified actionable knowledge is one thing, but to take the proper action requires a nimble organization with individuals empowered to take that action. Although this book is not meant as a replacement for business school, it should be clear that before embarking on building a BI program, every included BI activity should be accompanied by some return on investment (ROI) strategy.
The components of this strategy include analyzing costs, increases in revenues that are related to the activity, and other distinguishable benefits. This strategy should itemize:
- The fixed costs already incorporated into the BI infrastructure (e.g., database or query and reporting tool purchases)
- The variable costs associated with the activity (e.g., are there special software components required?)
- The ongoing costs for maintaining this activity
- The value of the benefits derived by taking actions when expected knowledge is derived from the activity
- The costs and benefits of other BI components that need to contribute to this business activity
- The value model expected from this activity
- The probabilities of successful applications of these actions to be applied to the expected value
- The determination of the time to break even as well as a profitability model
Let’s look at a simple example: building a CRM data warehouse for the purpose of increasing the lifetime value of each customer within a company’s customer base. The goal is to build a data warehouse that encapsulates all the data related to each individual customer. Building this data warehouse incurs costs associated with physical computational hardware, a database system, additional software tools, and integrating those components into the enterprise. Next there are the additional costs associated with the design and implementation of the warehouse model(s), as well as identifying the data sources, developing the processes for extracting data from its sources and loading it into the data warehouse, and ongoing maintenance of the data warehouse. The expected benefit of the data warehouse is a 30% increase in each customer’s lifetime value by the end of the third year following the launch of the data warehouse into production. The ROI model must offset the costs just described with the overall benefit value associated with the increase in lifetime value. If there is no breakeven point, the cost to build the data warehouse is more than the value derived from it; in that case, it is probably worth looking for additional value that can be derived from the project before pitching it to senior management.
Business Intelligence Applications
It is interesting to note the different uses of data and the contexts of each use as it pertains to the exploitation of information. For the most part, we can break those into two areas. The first area is operational data use, and the other is strategic use. The predominant use of information today is operational: how data helps run the business, as opposed to strategic information use, which helps improve the business.
Clearly these both are valuable, and without the operational use of information a business could not survive. But it is up to the information consumer to determine the extent of the value to be derived from the strategic use of information as well as what strategic uses are of importance. In this section we review some of the strategic uses of information as manifested through BI analytics. Note that although many of these analytic applications may be categorized within a specific business domain, many of them depend on each other within the business context.
Customer Analytics
A common, overused term is customer relationship management (CRM), which has become a buzzword implying an all-encompassing magic bullet to turn all contacts into customers and all customers into great customers. The magic of CRM is actually based on a number of customer analytic functions that together help people in a company better understand who their customers are and how to maximize the value of each customer. The results of these analytics can be used to enhance the customer’s experience as well.
Following are different aspects of customer analytics that benefit the sales, marketing, and service organizations as they interact with the customers.
- Customer profiling—The bulk of marketing traditionally casts a wide net and hopes to capture as many individuals as possible. Companies are realizing that all customers are not clones of some predefined market segment but are thinking individuals. To this end, customer analytics encompass the continuous refinement of individual customer profiles that incorporate demographic, psychographic, and behavioral data about each individual.
- Targeted marketing—Knowledge of a set of customer likes and dislikes can augment a marketing campaign to target small clusters of customers that share profiles. In fact, laser-style marketing is focused directly at individuals as a by-product of customer analytics.
- Personalization—As more business moves online, the browser acts as a proxy for the company’s first interface with the customer. Personalization, which is the process of crafting a presentation to the customer based on that customer’s profile, is the modern-day counterpart to the old-fashioned salesperson who remembers everything about his or her individual “accounts.” Web site personalization exploits customer profiles to dynamically collect content designed for an individual, and it is meant to enhance that customer’s experience.
- Collaborative filtering—We have all seen e-commerce Web sites that suggest alternate or additional purchases based on other people’s preferences. In other words, the information on a Web page may suggest that “people who have purchased product X also have purchased product Y.” These kinds of suggestions are the result of a process called collaborative filtering, which evaluates the similarity between the preferences of groups of customers. This kind of recommendation generation creates relatively reliable cross-sell and up-sell opportunities.
- Customer satisfaction—Another benefit of the customer profile is the ability to provide customer information to the customer satisfaction representatives. This can improve these representatives’ ability to deal with the customer and expedite problem resolution.
- Customer lifetime value—How does a company determine who their best customers are? The lifetime value of a customer is a measure of a customer’s profitability over the lifetime of the relationship, which incorporates the costs associated with managing that relationship and the revenues expected from that customer. Customer analytics incorporates metrics for measuring customer lifetime value.
- Customer loyalty—It is said that a company’s best new customers are its current customers. This means that a company’s best opportunities for new sales are with those customers that are already happy with that company’s products or services. Customer analytics help.
Human Capital Productivity Analytics
One way to attain value internally from BI is to be able to streamline and optimize people within the organization, including:
- Call center utilization and optimization—If you have ever dawdled while on hold, waiting for a customer service representative to pick up the telephone, you can understand the value of analyzing call center utilization to look for ways to improve throughput and decrease customer waiting time. When a company’s management realizes that inbound calls are likely to be from unsatisfied customers, making them stew on the phone is not going to improve customer satisfaction. In the more advanced cases, quick access to customer profile information may also affect the level of support provided to each customer (e.g., high level to high-value customers, minimal support to low-value customers).
- Production effectiveness—This includes evaluating on-time performance, labor costs, production yield, etc., all as factors of how staff members work. This information can also be integrated into an information repository and analyzed for value.
Business Productivity Analytics
Another popular analytic realm involves business productivity metrics and analysis, including:
- Defect analysis—While companies struggle to improve quality production, there may be specific factors that affect the number of defective items produced, such as time of day, the source of raw materials used, and even the individuals who staff a production line. These factors can be exposed through one component of business productivity analytics.
- Capacity planning and optimization—Understanding resource utilization for all aspects of a physical plant (i.e., all aspects of the machinery, personnel, expected throughput, raw input requirements, warehousing, just-in-time production, etc.) through a BI analytics process can assist management in resource planning and staffing.
- Financial reporting—Stricter industry regulatory constraints may force companies to provide documentation about their financials, especially in a time when companies are failing due to misstated or inaccurately stated results. In addition, financial reporting analytics provide the means for high-level executives to take the pulse of the company and drill down on particular areas.
- Risk management—Having greater accuracy or precision in tracking business processes and productivity allows a manager to make better decisions about how and when to allocate resources in a way that minimizes risk to the organization. In addition, risk analysis can be factored into business decisions regarding the kind of arrangements that are negotiated with partners and suppliers.
- Just-in-time—The concept of just-in-time product development revolves around the mitigation of inventory risk associated with commodity products with high price volatility. For example, the commodity desktop computer business is driven by successive generations of commodity components (disk drives, CPUs, DRAM memory chips, to name a few). Should a vendor purchase these items in large quantity and then come up against a low-sales quarter, that vendor might be stuck with components sitting on the shelf whose commodity value is rapidly declining. To alleviate this, the knowledge of how quickly the production team can assemble a product, along with sales channel information and supplier information (see Sales Channel Analytics and Supply Chain Analytics on page 21) can help in accurately delivering products built to customer order within a predictable amount of time.
- Asset management and resource planning—Utilization, productivity, and asset lifecycle information can be integrated through business analytics to provide insight into short- and long-term resource planning, as well as exposing optimal ways to manage corporate assets to support the resource plan.
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