This column, a continuation in the series on high-end campaign management, looks at calendars, portals and integrated modeling, and optimization for the high-end campaign manager.
Calendars are ultimately a type of report rather than an independent function. However, it can be amazingly difficult for a large marketing organization to pull together a comprehensive view of its activities. As a result, campaign management systems can provide substantial value by delivering integrated promotion calendars.
The calendar features can generally show planned campaigns in several formats. These include a tabular list, a standard calendar grid and usually a Gantt chart with horizontal bars indicating start and stop dates. Most systems allow users to filter the campaigns to be included in the calendar in order to highlight activity within a specified date range, product line, geographic region, medium or other campaign attributes. The calendar can usually be viewed on-screen, and users can click on a given campaign to see the underlying detail. In large installations, security may limit which campaigns a specified user may view.
Portals, like calendars, are more about providing convenient data access than a specific operational function. However, portal interfaces are an increasingly important component of campaign management systems, particularly when the scope has expanded to include project management, workflow and other administrative functions. In these situations, many different users apply the system in many different ways. A portal interface can be customized to present each user with only the relevant functions, as well as messages, reports, task lists and other personal information. Because they are distributed to large numbers of users, portal interfaces tend to be browser-based.
Integrated modeling and optimization represent a final set of non-core functions for a high-end campaign manager. Unlike project management and workflow, modeling and optimization are integral parts of the campaigns themselves. They are not classified as core functions simply because most high-end marketers continue to execute them outside of the campaign management systems. This reflects the current reality that modeling is done by statistical specialists who have their own preferred tools, rather than the marketers who set up the campaigns.
In theory, automated modeling tools could empower marketers to do this work themselves. However, the statisticians provide valuable expertise and quality control. Thus it is unlikely that they will be replaced entirely. It does seem possible that the statisticians will increasingly coexist with automated and marketer-operated modeling functions. This would let companies generate more models in less time, allowing applications that are not currently cost-effective. The statisticians would help to set up and validate the automated modeling systems.
The modeling functions available within today's high-end campaign managers vary widely. The most integrated approach is to allow users to build models within the campaign manager, reference the models as part of a segment definition and have the system score individual records as the segments are created. At the other extreme, data is exported to a separate modeling system which builds the model and scores the records. Scores are then posted back to the marketing database and used during segmentation like any other data element. An intermediate approach extracts the data and builds the model externally but imports the scoring formula. Scores are then calculated within the system while the segmentation is run.
Although these examples describe batch-selected outbound campaigns, the same options apply to real-time interactions. In fact, it's quite common to place precalculated model scores on a customer record and use these to guide real-time decisions. The obvious disadvantage is that the current interaction itself may reflect a change that makes the score outdated; but, in many situations, this is not a problem. Real-time interactions do permit yet another scenario in which the scoring formulas themselves are continually modified to reflect new interactions. This lets the models adjust automatically to changes in customer behavior patterns. Such systems must be carefully constrained to avoid errors that would be caught by a human modeler.
The functions used for modeling depend on the approach taken by a particular campaign manager. Systems that rely on external modeling software may provide special options to select data to export, to collapse relational database tables into flat files and to post scores back to customer records. When model formulas rather than scores are imported, the campaign manager needs to translate external models into formats that its own calculation engine can understand. It may also need to connect model inputs to customer database tables rather than the extract file used for model development. Typically the scoring formulas themselves are registered with system meta data and become available to use in segmentation rules like other data elements.
Systems with internal modeling capabilities may have tools to help select important variables, identify relationships among variables, perform data transformations, execute multiple modeling techniques and assess model quality. Ease of use and intelligible presentation of results are particularly important for systems designed to be run by non-statisticians.
Optimization uses statistical analysis to pick the best action within multiple constraints. Applications in a campaign- management system include picking the best customers for a particular campaign, the best products for a particular customer or the best combination of campaigns. Typical constraints include marketing budget, profit and sales targets, inventory availability, number of customers, contacts per customer and channel capacity. The optimization analysis itself usually relies on multiple statistical models to provide inputs.
Only a few campaign management systems currently provide meaningful optimization capabilities. However, the number will increase quickly as marketers become more familiar with the benefits and vendors learn how to simplify implementation.
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