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Naturally, the next best way to bring your strategies together is to tap into structured data and services to enrich the value of unstructured content Sometimes this enrichment means using structured data about users and their history to present a more useful interface for the end user Sometimes it means using services and applications normally reserved for structured content, and adjust them to also consume unstructured content
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Once you store a massive amount of information in your repository, your content access problem is mostly solved The problem now shifts to the question, how do I help my users to find new content Should I create an easily browseable interface so they can actively find content, or should I push relevant related documents so my users can passively find content Sometimes this problem is simple: if a content item belongs to a specific project group, and a user views that item, the odds are good that they may be interested in more information about that project Therefore, you may wish to display a sidebar with links to popular content in that project: download pages, whitepapers, or FAQs If you organize content items into a taxonomy, the odds are similarly good that you ll be interested in items that are close to that item according to your taxonomy Similarly, many Web pages contain a list of related articles arranged by topics that are manually controlled However, this is just the tip of the iceberg It is still too focused on the content and isn t considering the context, nor the user s needs For example, based on this user s search history, which of the documents might be more important When searching for documents about Project Alpha, would the user be more interested in marketing literature, sales reports, or research documents Would he desire different documents depending on the time of year, or on what campaigns are currently running What would you like to push to the user so they don t have to spend any energy looking for new information at all In short, how do you convert a search into a find Perhaps the most powerful combination here is between Oracle Universal Content Management and Oracle Real-Time Decisions Oracle Real-Time Decisions is a business intelligence product that makes recommendations based on a predictive analytics model When combined
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with enterprise content management, a predictive analytics engine can aggregate data from multiple data sources to provide a personalized, persuasive content to customers, suppliers, or employees In the customer relationship management (CRM) world, this type of technology can play a major role in a concept often called next best activity Next best activity is essentially the ability of a company or organization to know what the ideal proposition or interaction is with any given customer, at any given time, or any channel of communication For this to be accomplished, we must know all of the options available for the interaction, have knowledge of previous interactions, be aware of the customer s data in our various systems, and ideally know the customer s preferences about the types of offers we may make during this interaction In practice, the Real-Time Decisions (RTD) engine acts as an adaptive, learning-based personalization engine, as shown in Figure 7-2 Based on data available in enterprise databases, cookies, and other sources, RTD will recommend a specific item from a list of available options to an end user in order to achieve business objectives The engine monitors the success of different options against success criteria and proactively adapts the rules it uses
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Letters
Next Best Activity
PDA Web Oracle RTD
Kiosk / ATM
Call Center
Branch
FIGURE 7-2
Oracle Real-Time Decisions helps determine the next best activity on Site Studio Web sites
Transforming Infoglut!
to make recommendations to optimize the output of the recommendations This is a key element of customer services initiatives known as next best activity In other words, RTD gathers information from your Siebel call center, analytics from your public-facing Web site, your data warehouse, and other available sources in order to suggest what the next best activity would be for this specific customer, partner, or employee These recommendations can be used to drive persuasive content to users about new services or products that may apply to them, billing or account options that may be relevant, or simply educational information that is targeted to the users Traditionally, this type of personalization was done via hard-coded rules and programs established by marketing teams While these rules did provide some personalization, they required a very manual process of analyzing log files and trend reports to see which offers are working, then manually adjusting the hard-coded rules based on those results Using a predictive analytics engine, we can use a great deal more data while also eliminating the time delay and manual intensiveness of hard-coded personalization rules Because RTD works across a wide range of data sources, we also have personalization capabilities that can span the organization and customer touch points Our rules can understand that if the customer is calling our call center with issues, we may want to offer them a different product or services, perhaps even with a sale or discount, when they visit our Web site next For example, assume that a cell phone customer called his provider because of an issue with his old phone It turns out that his hands-free headset is broken, and no longer covered by warranty, so he needs to purchase a new one The next time he visits your public-facing Web site, your RTD engine will know of this history, and ensure the customer sees ads or even coupons for new headsets In practice, this means significantly less work than maintaining a list of related content for every item: you use analytics to determine what is popular, instead of employing an administrator to do market analysis and make guesses on what might be popular This works whether the audience is your customers, your partners, or your employees Naturally, this application is only as good as the historical data it begins with In other words, if the first person cannot find a piece of content, odds are that the next person won t be able to find it either, and your prediction engine will not have enough information to be useful Therefore, it is still a good idea to spend time making your site easy to browse and easy to search
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