Business Intelligence
Business intelligence means different things to different people. To one businessperson, business intelligence means market research,to another person, “reporting” may be a better term, even though business intelligence goes well beyond accessing a static report. “Reporting” and “analysis” are terms frequently used to describe business intelligence. Others will use terms such as “business analytics” or “decision support,” both with varying degrees of appropriateness.
BI for Improving Performance
When used effectively, business intelligence allows organizations to improve performance. Business performance is measured by a number of financial indicators, such as revenue, margin, profitability, cost to serve, and so on. In marketing, performance gains may be achieved by improving response rates for particular campaigns by identifying characteristics of more responsive customers. Eliminating ineffective campaigns saves companies millions of dollars each year. Business intelligence allows companies to boost revenues by cross-selling
products to existing customers. Accounting personnel may use BI to reduce the aging of accounts receivable by identifying late-paying customers. In manufacturing, BI can facilitate a gap analysis to understand why certain plants operate more efficiently than others.
Operational BI
While early business intelligence deployments focused more on strategic decisions and performance, BI increasingly plays a critical role in the daily operations of a company. In this regard, accessing detailed data and reviewing information may be necessary to complete an operational task. For example, as part of accepting a new order, a customer service representative may first check available inventory. Such an inventory report may be a standard report developed within an order entry system, or it may come from a BI solution, whether stand-alone or embedded in the order entry application.
BI for Process Improvement
The operations of a business are made up of dozens of individual processes. BI may support the decisions individuals make in every step of a process. It also may be used to help streamline a process by measuring how long sub processes take and identifying areas for improvement.
Performance Metrics and Key Performance Indicators
Performance metrics measure how well we perform within a particular business context. A performance metric relates an objective “score” (with a specific unit of measures) within a subjective scale of success. As an example, we might have a measure of the number of calls handled by each call center representative per hour. The unit of measure is the “number of calls,” but this measure provides only an objective score, but does not provide any subjective insight. Assessing comparative performance means determining the expected number of calls to be handled per hour, as well as specific target goals. For example, the expected number of calls to be handled is 10, while the target could be set at 15.
In the best scenarios, performance metrics are associated with every business process. In turn, these performance metrics can be accumulated or aggregated into higher-level metrics that describe a broader view of corporate success. The highest level of metric is the key performance indicator (KPI), which is some objective measurement of an aspect of a business that is critical to the success of that business.
KPIs can be collected together to provide a conceptual scorecard for a business and can be associated with a number of different business activities, especially within our four value driver areas such as financial value, productivity, risk, and trust. In fact a large number of KPIs can be defined in terms of measuring performance associated with many different BI analytical activities.
Another conceptual value of BI is the ability to capture the business definitions of the key performance indicators, manage those definitions as part of the corporate knowledge base, and then provide a visualization dashboard that reflects those KPI measurements, presented in a form for management review. This BI dashboard displays the results of the analytics required to configure the KPIs in a succinct visual representation that can be understood instantaneously or selected for drill-down. A BI dashboard will not only provide real-time presentation of the selected KPIs, but will also hook directly into the BI components that allow for that drill-down.
By looking at some sample performance metrics, we can become comfortable with engaging the business users to assess their query and reporting needs as well as determine the degree to which existing data sets can address those needs. And the categorization of business value drivers that has been presented earlier in this chapter supports the BI process by helping to clarify general business objectives and corresponding performance metrics and indicators.
- Improving the way the business is run as a result of integrating a BI framework goes beyond the technology—key stakeholders must specify what their perception of “performance” is, provide the performance measures, and then define achievable targets and use the tools to inform the decision-making processes. These measures are put in place to assess, measure, and control the degree to which the business objectives are being met. Specific programs can be designed and developed around improvements within any of these key categories. Consider these examples:
- Revenue generation via customer profiling and targeted marketing. Business intelligence reports and analyses reflecting customer transactions and other interactions enable the development of individual customer profiles incorporating demographic, psychographic, and behavioral data about each individual to support customer community segmentation into a variety of clusters based on different attributes and corresponding values. These categories form the basis of sales and profitability measures by customer category, helping to increase sales efforts and customer satisfaction.
- Risk management via identification of fraud, abuse, and leakage. Fraud, which includes intentional acts of deception with knowledge that the action or representation could result in an inappropriate gain, is often perpetrated through the exploitation of systemic scenarios. Fraud detection is a type of analysis that looks for prevalent types of patterns that appear with some degree of frequency within certain identified scenarios. Reporting of the ways that delivered products and services match what had been sold to customers (within the contexts of their contracts/agreements) may highlight areas of revenue leakage. Both of these risks can be analyzed and brought to the attention of the proper internal authorities for remediation.
- Improved customer satisfaction via profiling, personalization, and customer lifetime value analysis. Customer lifetime value analysis calculates the measure of a customer’s profitability over the lifetime of the relationship, incorporating the costs associated with managing that relationship as well as the revenues expected from that customer. Employing the results of customer profiling can do more than just enhance that customer’s experience by customizing the presentation of material or content. Customer profiles can be directly integrated into all customer interactions, especially at inbound call centers, where customer profiles can improve a customer service representative’s ability to deal with the customer, expedite problem resolution, and perhaps even increase product and service sales.
- Improved procurement and acquisition productivity through spend analysis. Spend analysis incorporates the collection, standardization, and categorization of product purchase and supplier data to select the most dependable vendors, streamline the RFP and procurement process, reduce costs, improve the predictability of high-value supply chains, and improve supply-chain predictability and efficiency.
Each of these examples can be viewed in both the operational and the strategic business perspectives. The operational view provides insight into existing conditions and performance comparing existing activities to expectations. From the strategic perspective, we can evaluate the degree to which any potential measurements impact future corporate value.
Horizontal Use Cases for Business Intelligence
It is interesting to note the different uses of data and the contexts of each use as it pertains to the advantageous use of information. Data use can be segmented into operational and analytical activities, and organizations are increasingly seeking ways to transition from being solely operational to embracing both operational and strategic use of information. This section enumerates some typical strategic uses of information that are not specific to any industry. The types of analyses presented here are useful for many different businesses. 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.
Vertical Use Cases for Business Intelligence
Opportunities for improvement manifest themselves in different ways and at different times, often depending on the industry in which the organization operates. We can adopt the same set of corporate value drivers and still determine ways the company can benefit from reporting and analytics specifically tuned to its industry, such as these “vertical” examples:
- Health care. Monitoring business process performance permeates all aspects of quality of care. For example, understanding why some practitioners are more successful at treating certain conditions can lead to improved quality of care. Analytics can help to discover the factors that contribute to success of one approach over others, and see whether those successes are dependent on variables within the control of the practitioner or factors outside their control. Improved diagnostic approaches can reduce the demand for high-cost diagnostic resources such as imaging machinery, and better treatments can reduce the duration of patient stays, freeing up beds, improving throughput, and enabling more efficient bed utilization.
- Logistics/supply chain. Integrated analysis for transportation and logistics management sheds insight into evaluation of many aspects of an efficient supply chain. For example, BI is used to analyze usage patterns for particular products based on a series of geographic, demographic, and psychographic dimensions. Predictability becomes the magic word—knowing what types of individuals in which types of areas account for purchases of the range of products over particular time periods can help in more accurately predicting (and therefore meeting) demand. As a result, the manufacturer can route the right amounts of products to reduce or eliminate out-of-stocks. At the same time, understanding demand by region over different time periods leads to more accurate planning of delivery packaging, methods, and scheduling. We can map the sales of products in relation to distance from the origination point; if sales are lower in some locations than others, it may indicate a failure in the supply chain that can be reviewed and potentially remediated in real time.
- Telecommunications. In an industry continually battling customer attrition, increasing a customer’s business commitment contributes to maintaining a long customer lifetime. For example, examining customer cell phone usage can help to identify each individual’s core network. If a customer calls a small number of residential land lines or personal mobile phones, that customer may be better served by a “friends and family” service plan that lowers the cost for the most frequently called numbers. Identifying household relationships within the core network may enable service bundling, either by consolidating mobile accounts, or by cross-selling additional services such as landline service, Internet, and other entertainment services. On the other hand, if the calls from the customer’s individual mobile phone are largely to business telephone numbers and have durations between a half hour to an hour, that customer may be better served with a business telephony relationship that bundles calling with additional mobile connectivity services.
- Retail. The large volume of point of sales data makes it a ripe resource for analysis, and retail establishments are always looking for ways to optimize their product placement to increase sales while reducing overhead to increase their margins, especially when market baskets can be directly tied to individuals via affinity cards. Understanding the relationship between a brick-and-mortar store location and the types of people who live within the surrounding area helps the store managers with their selection of products for store assortment. Strategic product placement (such as middle shelf or end-cap) can be reserved for those items that drive profitability, and this can be based on a combination of product sales by customer segment coupled with maps of customer travel patterns through the store. Product placement is not limited to physical locations; massive web logs can be analyzed for customer behavior to help dynamically rearrange offer placement on a web site, as well as encourage product upselling based on abandoned cart analysis, through collaborative filtering, or based on the customer’s own preferences.
- Financial services/insurance. In both insurance and banking, identifying risks and managing exposure are critical to improved profitability. Banks providing a collection of financial services develop precise models associated with customer activities and profiles that identify additional risk variables. For example, analyzing large populations of credit card purchases in relation to mortgage failures may show increased default risk for individuals shopping at particular shopping malls or eating at certain types of fast food restaurants. In turn, recognizing behaviors that are indicative of default risk may help the bank anticipate default events and reach out to those individuals with alternate products that keep them in their homes, reduce the risk of default, and improve predictability of the loan’s cash flow over long periods of time.
- Manufacturing. Plant performance analysis is critical to maintaining predictable and reliable productivity; tracking production line performance, machinery downtime, production quality, work in progress, safety incidents, and delivering measurements of operational performance indicators along the management escalation chain so that adverse events can be addressed within the proper context within a reasonable timeframe.
- Hospitality. Hotel chains assess customer profiles and related travel patterns, and know that certain customers may be dividing their annual “night allocation” among the competitors. By analyzing customer travel preferences and preferred locations, the company may present incentive offers through the loyalty program to capture more of that customer’s night allocation.
- Energy services. Increasing deployment of “smart meters” not only enables comparative reporting and analytics with customers to reduce demand while increasing efficiency through utility-managed powering of residential machinery (such as air conditioners), but understanding demand and usage patterns can help drive acquisition and delivery strategies as well as help in identifying scenarios driving maintenance activity such as transient “flickers” indicative of imminent outages (dues to trees or animal damage).
The examples for these industries are similar in that the analysis ranges from straightforward reporting of key business performance indicators to exploring opportunities for optimizing the way the organization is run or improving interactions with customers and other business partners. Investigation of the business processes and performance measures from any industry will yield suggestions for ways to specifically benefit from reporting and analytics.