This article originally appeared on the BeyeNETWORK.
Step 2 of 2:
By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners. You can withdraw your consent at any time. Contact TechTarget at 275 Grove Street, Newton, MA.
This excerpt from The Profit Impact of Business Intelligence describes the use of detailed and specific business information about customers’ past purchasing behavior to grow revenue
and retain profitable customers.
Some of the earliest uses of business intelligence (BI) were driven by the desire to improve revenue-generating processes. Simply put, companies wanted to understand their customers better, retain their best customers, and sell them more products or services. Achieving those goals can be relatively straightforward in some business-to-business contexts, for example, if a company has a couple dozen key customers that drive a large percentage of revenue. However, companies that have millions of customers and that annually process tens or hundreds of millions of business transactions with these customers often can’t see the forest for the trees. Many such companies can’t identify their most profitable customers, they don’t know when profitable customers are about to defect, and they can only guess at what direct marketing offers will be most attractive to which customers.
To address these gaps in understanding customers, companies in such retail-oriented industries as consumer product retailing, telecommunications, and financial services were early adopters in using BI to sift through the data about the millions of customer transactions to better understand what drives revenue. BI is commonly used to improve revenue-generating processes such as market analysis, customer segmentation, campaign management, advertising, channel management, customer relationship management (CRM), sales force management, and pipeline management. To illustrate this point, we will provide high-level descriptions and/or brief examples of some common ways BI is used to improve revenue-generating processes. Some of those ways are:
- Marketing analysis. By marketing analysis, we mean the analytical activities in which companies engage in order to understand such revenue generation fundamentals as who buys their products or services, when they buy the products, where they buy the products, how often do they buy the products, what price do they pay, how do they respond to promotional offers, which products or services generate what percentages of revenues, what are the sales trends for each product or service, which products or services tend to be purchased together, and so forth.
For companies with millions of customers, perhaps thousands of points of sale, and perhaps multiple channels, such analyses clearly depend on access to relevant business information and appropriate analytical tools, that is, on access to BI. With a well-architected BI environment, marketing analysis can be done in near real-time to see current and long-term revenue trends and to understand the underlying drivers of revenue growth. With appropriate tools, data about hundreds of millions of individual transactions can be mined to answer the fundamental marketing questions posed above. Armed with better information, companies can be more effective in attracting new customers, retaining profitable customers, and achieving a sustainable revenue portfolio. Furthermore, they can understand the relationship between channels and profitability and introduce incentives for customers to use the more profitable channels. In general, experience in a range of industries has shown that one of the most profitable uses of BI is in better understanding the relationship among customers, products or services, and revenue generation.
- Customer segmentation. With the ability to sift through millions of detailed records about business transactions with customers, companies have gained the ability to substantially extend the practice of customer segmentation. In the past, most bases for customer (market) segmentation were so-called a priori bases; that is, they were based on information one could know about customers as a group and in many cases without having any insight into individual customer purchasing behavior. So, for example, demographic segmentation grouped customers by common characteristics such as age, income, occupation, and so forth, and geographic segmentation grouped customers by where they lived. In the business-to-business world, demographic segmentation grouped customers by such common characteristics as industry, role in the value chain, and revenues. Volvo Cars of North America uses these techniques to analyze and predict the behavior of its customers and sales prospects. Among other things, BI enables Volvo to prequalify prospects by predicting their probability of buying a Volvo even if they’ve never contacted the company or walked into a Volvo showroom.
Beginning in the late 1970s, psychographic segmentation sought to group customers by such potentially common characteristics as personality, leisure activities, and values. It became common to associate zip codes with psychographic profiles, with groups often labeled with catchy phrases as “pools and patios” or “shotguns and pickups.” With all of these a priori bases of segmentation, the connection between association with the group and actual purchasing behavior was not clear. To overcome this gap, marketers began to investigate differences in customer behavior as the basis for segmentation, initially with focus groups and market pilots and, in the past decade, by using BI tools and techniques. Specifically, by mining data about millions of individual customer transactions and marrying such information with traditional demographic, geographic, and psychographic information, companies have been able to group customers by purchasing behavior and to understand the relationships, if any, between purchasing behavior and, for example, demographic variables and product characteristics. These BI tools do not replace traditional segmentation and market research tools: they simply provide powerful new tools that work well with existing tools to help companies define narrower customer segments, understand the needs and values of those segments, create products and services that better respond to those needs and values, and develop more selective and effective ways to reach and acquire new customers and/or expand business with current profitable customers.
- Advertising, direct marketing, and public relations (PR). BI-driven market analysis and customer segmentation provides a much richer understanding of customers and what they value as input for advertising, direct marketing, and PR campaigns, whether they are focused on product or service awareness, product education and positioning, brand building, countering rival campaigns, public image, or a call to purchase. Aside from effective presentation, advertising, direct marketing, and PR campaigns are about message, and BI provides an effective means of understanding the intended recipients of a given message. Further, BI can provide the ability to measure the effectiveness of advertising and direct marketing that is directed toward increased revenues. In some cases, this is done by observing changes to a product or product family’s sales trend line after an advertising campaign has run. In other cases, the specific purchasing behavior of targeted individual consumers in response to a direct marketing campaign can be identified. For example, a hotel chain that sends a time-limited promotional offer to a specific known individual can determine from its reservation system or property management system if the individual accepted the offer. The power of such BI is substantial because it promotes both revenue increases, by targeting those who are most likely to accept an offer, and reduced costs, by narrowcasting the offer, which reduces campaign execution costs. Firms as different as Harrah’s Entertainment, which runs casinos, and Capital One, which provides financial services, use BI to measure and improve the cost-effectiveness of their marketing and PR efforts.
- Channel management. The nature of channels varies by industry and position within the value chain. For product retailing and retail services, channels used to mean stores or branches and sometimes mail-order and/or telephone channels. Today, the Internet, ATMs, kiosks, and other point-of-sale mechanisms are also in the mix. For consumer product manufacturers, channels include both distribution channels (how the product gets to the store) and the different types of retail stores. For industrial product manufacturers, channels include direct sales and a variety of often industry specific distributors and wholesalers.
Except for enterprises that are completely vertically integrated, a rarity in most industries these days, companies face strategic decisions about what channels to use and which partners to use within a given channel. They also face the task of evaluating channel and/or channel partner effectiveness over time. All of these channel management tasks can be made more effective through adoption of BI. For example, we talked earlier about using BI for marketing analysis, from which we gain insight into how much revenue comes from which channels; through which channels volume is increasing, decreasing, or holding steady; and how different products fare in different channels. If we can integrate that information with appropriate channel cost information, we can determine which channels are most cost-effective for us as a means to margin optimization. By use of BI, we can also assess channel partner performance. For example, suppose a company uses different distributors in different parts of the country. With appropriate BI tools and techniques, we can assess their relative performance in terms of revenue growth and customer service. All of these BI opportunities, and others not detailed here, can enable more effective channel selection and management, which supports more effective revenue generation and growth.
- CRM. CRM means different things to different people. To the vendors who coined the term, CRM is the packaged enterprise software they sell, which has both transactional and BI functionality relating to customers and to sales activities. For BI vendors, CRM means (among other things) prepackaged software applications for analyzing customer behavior and sales force performance. To companies that want to improve their revenue generation processes, CRM may have both a BI appeal and a transactional system appeal, and there is an associated idea of a centralized repository (database) of customer information that can be used for cross-selling and/or up-selling customers. Setting aside the potential of CRM to automate such transactional functions as campaign management, the BI appeal of CRM is basically that it helps companies better understand their customers (as discussed earlier under “Marketing Analysis” and under “Customer Segmentation”). In that sense, the technologies enable more effective marketing analysis and customer segmentation. If coupled with appropriate changes to marketing and sales business processes, those can lead to more effective revenue generation and revenue growth. Harrah’s Entertainment and Continental Airlines are two outstanding examples of using BI to tune up customer service and relationships to a high pitch of excellence and profitability.
- Category management. With the consolidation in consumer product retailing that’s been driven by Wal-Mart’s successful competitive strategy, more and more retailers and consumer product manufacturers are using category management techniques to optimize revenue and margins. The fundamental principle of category management is that retailers want to optimize contribution margin per cubic foot of retail shelf space. Accomplishing this goal is a function of pushing inventory and shelf stocking costs onto suppliers, avoiding stockouts, and optimizing the allocation of shelf space to product categories based on understanding customers’ purchasing habits and on knowing the revenue and gross margin characteristics of each product and product category. IT in general and BI in particular have dramatically advanced the state of the art in category management. By bringing point-of-sale data into a BI environment, retailers can understand product-level demand trends and how they vary by relevant dimensions such as geography and service area demographics. Further, they can track the revenue increase (“lift”) associated with promotions, which can be more effectively targeted by using BI to analyze customers’ past purchasing behavior in response to promotions. The combination of multidimensional demand trend data and the ability to track the effectiveness of promotions allows retailers to have the right product mix on the shelves, to optimize shelf space allocation to product categories, and to optimize revenues and gross margin at the store level. Category management BI also can be used to optimize supply chain performance, which of course improves both margins and profits.
A key strength of using a business-centric BI approach is that
it explicitly connects the information BI delivers with the
revenue-generating processes that pay back your BI investment.
Because of that explicit connection, you get a clear picture of
what kind of ROI your BI efforts generate.
Summary: Business Intelligence for Revenue-Generating Processes
In this section, we have illustrated some of the typical ways that BI is used to improve key revenue generation processes. Fundamentally, these BI opportunities are about using detailed and specific business information about customers’ past purchasing behavior to better understand their needs and preferences and, thereby, to become more effective at growing revenue and retaining profitable customers. Having such information is especially valuable for companies with millions of customers. It enables application of marketing concepts such as customer lifetime value analysis, needs-based segmentation, and collaborative filtering. Using BI to improve revenue generation processes is also important in business-to-business contexts because it allows companies to evaluate product sales trends in the aggregate, customer purchasing patterns in particular, product mix with specific customers, product sales by relevant dimensions such as geography and customer demographics, and customer profitability. In some distribution industries, having BI about product sales to end customers has enabled the distributors to offer BI to upstream and downstream supply chain partners as a value-added service that has resulted in competitive advantage and increased revenues. All in all, using BI to improve revenue-generating processes is one of the most effective ways BI can be employed to drive profits.
Printed with permission from Morgan Kaufmann, a division of Elseiver. Copyright 2007. The Profit Impact of Business Intelligence by Steve Williams and Nancy Williams. For more information about this book, please visit http://www.books.elsevier.com/.