The Pervasiveness of the internet means that the first interaction most businesses have with a potential customer is via the company Web site or e-commerce application. Consequently, the ability to “personalize” (match and modify) Web site content to individual customer or partner preferences is now a necessity as all companies – whether operating on a business-to-consumer (B2C) or business-to-business (B2B) model – strive to make their online operations more profitable.
Business intelligence (BI) and analytical applications are playing a decisive role in enabling Web site personalization. In fact, the application of customer analytics to Web operations has become so vital that it has prompted a new analytical domain: e-business intelligence (eBI).
Personalization, though, requires considerably more than just buying a BI tool and mining a Web site’s data. In practice, personalization involves a number of considerations, including extracting, combining, and analyzing data taken from multiple sources, and integrating the results into the Web store (and other customer-facing channels).
The Business Case
The business case for applying personalization is founded in all the buzzwords associated with e-commerce, including turning first-time visitors into paying customers and facilitating greater cross-sell and up-sell activities. In reality, many of the benefits obtainable from personalization are realized over the long term, yet they are just as important to the success of a business, and include:
- Determining how to redesign and optimize Web operations.
- Assessing which content to personalize and how.
- Displaying more items of interest to a visitor.
- Analyzing the success of online marketing campaigns and applying the findings to other marketing channels.
- Determining the most popular content areas by tracking customer behavior – What specific pages are preferred (viewed) by what specific kinds of customers?
- Analyzing Web site performance to generate reports detailing traffic patterns (used to make recommendations ranging from design improvements to assessing the use of richer graphics and animation).
- Optimizing the number, frequency, and mixture of ad placements, and testing how visitors receive new ads.
- Determining why visitors are leaving a site before completing their purchases, even though they have already selected items – AKA shopping cart abandonment.
- Applying personalization across all customer channels.
Personalization Processes and Components
The “Components of Personalization” (above) provides an overview of the various processes necessary to facilitate Web personalization (indicated with circles). It also shows the components and platforms (products and services) supporting these processes. Personalization involves four main steps, conducted in a cyclical manner:
CUSTOMER INTERACTION. Personalization requires incrementally interacting with online visitors to gradually generate and assemble data that can be analyzed to determine individual preferences and tastes. This can be as simple as having visitors fill out a form stating their favorite types of music, books, software, products, and so on, or applying real-time data mining in the form of consumer profiling systems. These systems employ rule-based or collaborative filtering techniques to automatically segment them into “communities” of customers with similar tastes.
DATA COLLECTION AND INTEGRATION. This process, which includes extraction, transformation, and loading (ETL), varies greatly depending on company needs. For simple personalization efforts, some companies may only need to analyze Web visitor clickstream data in order to determine customer interests, and then tailor their Web sites accordingly. However, more in-depth customer analysis requires integrating data extracted from multiple sources and loading it into a customer information store.
BUSINESS INTELLIGENCE. Analysts apply various analytical techniques to data collected in the customer information store to determine customer preferences and segment customers into different categories based on individual preferences. A company may conduct analysis internally, or have it done offsite by an analytical application service provider (ASP).
CUSTOMER INTERACTION PERSONALIZATION. In customer interaction personalization, the results of the analyses are tied back into an organization’s e-business operations, effectively “closing-the-loop” between analysis and operation. This consists of generating personalization rules, which are incorporated into the Web store or the e-commerce platform’s personalization engine. These rules target visitors with specific content based on their behavioral profiles.
Supporting Architecture
Personalization requires implementing various Web components and the necessary data integration infrastructure that will enable the following components to interact with one another:
- Customer information store;
- BI-tools and packaged analytical applications;
- E-commerce platforms/content management systems;
- Personalization engines (which may be standalone or integrated with e-commerce and other enterprise platforms); and
- Consumer profiling systems (collaborative filtering and rule-based systems employing real-time data mining techniques).
There are a variety of approaches for analyzing and personalizing data, as well as various data sources within the company’s architecture from which to cull.
CLICKSTREAM ANALYSIS. The first option a company has for conducting Web site personalization is to analyze its clickstream data. “Clickstream” refers to lines of code that are written to a flat file (or Web log) each time a visitor views a Web page or clicks on a hyperlink. Clickstream data provides a detailed activity path that is generated when a visitor interacts with a Web site. Analyzing clickstream data lets organizations track visitors as they navigate through the site, determining what Web pages and ads visitors viewed, what they clicked on, and how long they stayed on a certain page.
Data analysis takes place in an interactive manner, in which analysts generate ad hoc queries to uncover customer preferences, demographics, and transaction patterns. For example, to identify the most frequent shoppers, analysts might query the customer information data stores. Next, they might use data mining by applying a Self-Organizing Map (SOM) neural network to identify the company’s most profitable customer. (SOM algorithms are useful for automatically clustering data into naturally occurring clusters, making them ideal for segmenting customers into similar categories). Next, analysts might apply statistical analysis to determine the average income level, age, and sex of a company’s most profitable customers.
To identify the best cross-sell and up-sell opportunities, analysts typically segment customers into different categories based on user profiles, Web site behavior, and traditional customer interactions such as sales transactions and call center records. As an example, an online retailer might segment customers into one of 10 categories, obviously depending on the business. For instance, basic segmentation could place a customer in the category of graphical designer. A subcategory might also include her under artist. The use of third-party demographic and household data might indicate that she is married, has two grade-school children, operates her own design business, and belongs to a four-member family household with an overall income in excess of $300,000 a year.
Consequently, more in-depth analysis of this rich blend of data would result in this customer (along with thousands of others) being segmented into a high-income category. In shirt, the objective is to fine tune categorization schemes to earmark content, advertising, and promotions to support the categories a company defines.
APPLYING DATA ANALYSIS RESULTS TO PERSONALIZATION. Once organizations have analyzed customer data, they need to apply the results of the findings to personalize Web operations. This process depends largely on the type and sophistication of a company’s e-commerce platforms, BI tools, and analytical applications.
Different e-commerce platforms use personalization engines based on various approaches to deliver personalized content. For example, BroadVision Inc., Redwood City, Calif., and Vignette Corp., Austin, Texas, both provide template-based approaches that allow marketers to earmark content, promotions, and ads for various categories of visitors that they have defined. Other platforms use rule-based (inference engine-based) personalization approaches, such as platform from Blue Martini Software, San Mateo, Calif., and Art Technology Group, Cambridge, Mass. Still others, like Net Perceptions Inc., Edina, Minn., offer outsourced personalization services based on collaborative filtering techniques. (Collaborative filtering consists of analyzing customers’ historical and current buying behavior. Based on this behavior, customers are sorted in a “buying community” consisting of other customers that have established similar profiles. The more a customer shops, the more likely the algorithms will produce effective results. In addition, collaborative filtering algorithms can learn in near-real time.)
Some analytical products offer tight integration with popular e-commerce platforms like those from BroadVision and Vigenette. This means an organization can use its output (analytical models) to generate personalization rules, which are then fed directly back into the Web store, thereby automating the process of matching content to users. For example, the Broadbase Enterprise Performance Management analytical application from BroadVision e-commerce server’s personalization rules engine to personalize content.
Blue Martini features a data mart, data mining, and data analysis tools that are tightly integrated with its rules-based server that runs on the Web store. This allows merchandisers to enter rules detailing customer behavior uncovered by data mining directly into the Web store to dynamically update the platforms with specific personalization rules and content that drive promotional, advertising, and other merchandising activities targeted at online visitors.
Products and Services
There are a number of options for companies seeking to personalize their e-business operations. These include purchasing one of the many BI tools on the market, implementing a packaged analytical application, or outsourcing to one of the new analytical ASPs offering personalization services.
All the major BI tool vendors (including OLAP, data mining, data visualization, etc.) have enhanced their products with new capabilities for analyzing clickstream data. Organizations that decide to go with a tools approach should go for a product that provides prebuilt Web analysis templates. Templates provide a “road map” that can help get a Web analysis application up and running more quickly. Products for analyzing clickstream data and combining it with other data sources are available from vendors such as Hyperion Solutions, Sunnyvale, Calif.; Cognos Inc., Burlington, Mass.; Angoss Software Corp.; Toronto; SPSS Inc., Chicago; and SAS, Cary, N.C.
Another choice it to use a packaged analytical application that bundles application-specific data models, prebuilt interfaces (for extracting and transforming data), a metadata repository, and predefined reports and metrics in the form of key performance indicators tailored to specific industries and domains. Packaged applications can accelerate implementing advanced analytical application that provide a platform for applying personalization across all customer and partner channels. Their main drawback is that they are initially more expensive than buying a BI tool. And, although they provide considerably more functionality, many organizations may not require all of their features.
The most visible products in this category include those from Broadbase; E.piphany Inc., San Mateo, Calif.; Informatica Corp., Mountain View, Calif.; MicroStrategy Inc., Vienna, Va.; Business Objects Inc., San Jose, Calif.; and Blue Martini. In reality, the market for packaged analytical applications is overcrowded with products.
Another option is to outsource Web analysis requirements to an analytical ASP such as WhiteCross Systems Inc., San Fransisco; Interelate. Eden Prairie, Minn.; digiMine Inc., Kirkland, Wash.; WebMiner.com, New York City; or WebTrends Corp., Portland; Ore. The benefit to outsourcing is that an organization avoids having to buy, install, and manage the necessary hardware and software, and can leverage the ASP’s expertise in conducting and applying data analysis to online operations. The drawback is that a company is essentially putting its trust in a third party. In addition, by outsourcing, the company will not gain the long-term benefit of building up data analysis expertise within the organization.
Removing the Guesswork
Personalization consists of various processes, and the degree of personalization that can be performed depends on the data an organization collects or has access to. Personalization may take place in an automated near-real-time manner immediately at the initial point of first customer contact by applying collaborative filtering and rules-based data mining techniques. “Deeper” personalization requires using richer customer data culled from multiple source, which are typically stored in a data warehouse and analyzed using BI techniques.
BI plays a key role in personalization because it provides the analytical capabilities to help determine the important customer and what an organization should do to keep them. BI isn’t cheap; however, neither is going out of business. Without Bi, an organization is just guessing as to what makes its marketing operations successful or a flop. Lastly, BI and personalization will substitute for a well-defined business plan, as too many dot-coms have come realize. However, they can procide business managers with the ammunition needed to measure and interpret how well the company is operating and what areas need attention.
By: Curt Hall
Source: Software Magazine