David Raab Article Archive

Vtrenz Inc. iMarketing Automation
by David M. Raab
DM News
November, 2006

Sales and marketing organizations have been among the most enthusiastic adopters of hosted software (also known as on-demand, software as a service, or application service providers), which allows companies to use systems that are run for them by an external vendor. This is particularly true at smaller firms, where technical resources tend to be stretched thin and where sales and marketing systems can be effective without extensive customization or integration with the other company processes.

Many hosted sales and marketing systems resemble their on-premise counterparts: they provide sales force automation, customer service, marketing management, or several of these combined. But some don’t fit into those standard categories. Consider Eloqua Conversion Suite (www.eloqua.com), reviewed here in May 2006, and Vtrenz iMarketing Automation (www.vtrenz.com), reviewed below. Designed primarily for lead generation and nurturing, they combine campaign management, email, and Web execution with links to external sales and service software. They differ from conventional “enterprise marketing management” in using a simple database structure—typically a contact profile supplemented by interaction history—rather than a complete marketing database with extensive transaction details. They also lack advanced budgeting, project management, and analytics capabilities of a full-fledged enterprise marketing system.

Vtrenz iMarketing Automation (Vtrenz Inc., 701-478-7704, www.vtrenz.com) is particularly impressive for its campaign capability. Campaigns can include multiple tracks, each with a sequence of steps. Entry criteria determine which contacts move into each track and each step. Each step has its own execution schedule, which can be a fixed date or relative to the time a participant reached the preceding step. Each step also has an action, such as receiving a particular email message, that is applied to the people who reach it.

Users can also define criteria to move prospects from their current track to the start of another track or campaign. This allows the system to react to prospect behavior or profile changes after the campaign begins. Additional criteria at the campaign, track, and step levels can send prospects to an external destination such as a sales automation system. Users can define business logic to choose the recipient of the exported records. They can also set the timing, data elements and an accompanying message.

None of this is particularly glitzy: the tracks and steps are simply listed in sequence rather than displayed on a graphical flow chart. But it does let non-technical users define complicated treatment processes that adjust to each prospect’s behavior. Since the system can automatically migrate customers from one campaign to another, it could in theory execute a completely automated set of contact programs throughout the customer life cycle.

All types of selection criteria are created with the same user interface. Users define one or more rules, each having one or more statements. The statements are built by selecting from a list of categories including database tables and campaign resources such as email, direct mail, surveys and Web sites. Users then select from a list of statements appropriate to the category, such as having recently clicked on a link in an email. Finally, they apply parameters such as the specific link and time period to check. Statements can draw on profile data, campaign participation, or contact behavior. Statements are linked with ‘and’ or ‘or’ conditions.

This approach allows non-technical users to build complicated statements and presents the result in an English-like format. But it is limited to the 250+ statements that the vendor has predefined: even technical users cannot add their own.

Contacts for a one-shot campaign can be selected by creating a list, while on-going campaigns can execute their entry criteria on a user-defined schedule. On-going campaigns also have start and end dates and rules for whether the same person can receive the campaign more than once. Users can specify the hours of the day and days of the week during which the campaign is active, typically to limit responses to normal business hours.

Campaign actions are associated with resources including email, direct mail, fax messages, and Web pages, surveys and forms. Users can create these with the system’s HTML generator, which has an interface similar to Microsoft Word, or they can import HTML created elsewhere. Messages can be personalized and forms pre-populated by pulling information from the underlying database. Emails can also contain links to personalized Web pages. By the end of 2006, system-generated pages will be able to include business logic to display different message blocks based on contact profiles or behavior.

Survey and other form results can be posted to the contact database—which is limited to 40 standard fields and 40 user-assigned fields—or a separate survey response table. Users can publish or disable campaign resources and see which campaigns use them. A separate “assets manager” catalogs elements such as graphics files.

Contact names enter the Vtrenz database through file imports, system-generated Web forms, or automated data exchange with sales automation systems. Vtrenz has a Application Program Interface (API) to accept such data, plus a specialized API tailored to Salesforce.com. Another API is planned for Microsoft CRM. Duplicate identification is limited to exact matches on full or partial strings.

The Vtrenz database contains the 80-field contact table, survey table, and history of campaigns, messages, and responses. Vtrenz assumes more detailed information, such as account data and purchase history, will reside in other company systems and be imported in summary form as needed. “Extension tables” can store more data within Vtrenz, but take custom set-up by Vtrenz staff.

The system provides basic email delivery, bounce and click-through statistics. It tracks survey and microsite visits to measure response, but lacks integrated campaign analysis or A/B split testing. Vtrenz offers a Web tracking service based on first party cookies and code snippets embedded in a Web page.

Vtrenz was founded in 1999 and currently serves more than 250 clients. Most are business to business marketers. Pricing is based on the number of profile names, users and system features. The simplest version starts at $10,000 per year plus $5,000 for set-up and implementation, although most clients spend more to include additional options.

DecisionPower Inc. MarketSim
by David M. Raab
DM News
October, 2006

Agent-based modeling uses computer programs to simulate quasi-independent entities as they interact with their environment and each other. It is frequently used to model things like ecosystems and network traffic, although it is probably most familiar from computer games such as SimCity. In an agent-based model, the actions of each entity (agent) are determined by rules that process inputs including the agent’s own state (“hungry”), external factors (“food on that rock”), and other agents (“crowd around the food”). Results over time can produce complex patterns of behavior that are not explicitly defined in the rules themselves. In the example I just gave, the creature might balance how hungry it is, the distance to the food, and the size of the crowd to decide whether to move towards or away from the rock.

MarketSim (DecisionPower Inc., 408-379-9200, www.decisionpower.com) applies agent-based modeling to consumer behavior. In particular, it is used to predict results in a product category such as snack foods or headache remedies. In a model of this sort, the primary agents are consumers, while the environment includes brands, products and channels. Rules describe how consumers make purchase decisions, taking into account factors such as product attributes, consumer preferences, marketing activities, distribution, and external influences like the weather. When the model is run with specific inputs such as particular set of products and marketing activities, the system simulates what consumers will buy over time and generates outputs for market share, revenue, profits, unit sales, and other business measures.

This may sound quite simple, but in practice it is not. MarketSim’s must match the results of actual consumer markets with enough accuracy for businesses to determine the likely results of a particular marketing plan or product launch. This means models all the competitors in a product category, as well as the different distribution channels and types of customers. Each has its own characteristics.

The most complex entity is the consumer. MarketSim provides over 100 prebuilt rules, of which 25 or so might be used in any particular model. These rules describe how customers make their purchase decisions, taking into account their preferences for different product attributes; information gathered from advertising and personal contacts; consumption volume and frequency; shopping behavior such as channels used and responsiveness to in-store displays and price differences; responsiveness to coupons; and previous experiences. Each model includes multiple customer segments with their own settings for rules such as the weight assigned to different product attributes.

The number of agents assigned to a segment would be proportionate to the size of that the segment in the actual marketplace, although the total number of agents need not equal the number of actual customers. A market of many millions could be accurately modeled with 100,000 to 150,000 agents.

A model would also include dozens or even hundreds of specific products, depending on the complexity of the marketplace and the degree of detail required. Products are linked to brands. Each brand will have its own attributes, which use the same categories as consumer preferences. This provides the connection between brands and consumers that is needed to model consumer choice. Each brand also has a marketing plan with price, display, distribution, media, and coupon details for each time period.

All these rules and attributes must be set so they result in an accurate prediction. DecisionPower does this by gathering historical data—typically three year’s worth—for actual sales, marketing activities, product attributes, distribution, and external factors. This data covers all competitors, not just the model sponsor. It is fed into the model and the rules are tuned until the system gives acceptably realistic results. Modelers usually feed in two and a half years of actual data and then compare simulated results for the final six months with the known actual results for the same period.

This calibration process occupies most of the three to four months it takes to build a major MarketSim model. Most of the work involves adding new data sources or events to reduce anomalies in results. A calibrated model can give reliable predictions about one year into the future, and will remain valid—assuming the inputs such advertising spend are updated—for one or two years before it must be rebuilt.

Constructing a MarketSim model is largely a task for experts, either employed by DecisionPower or in the research department of a client. The system offers a graphic user interface that is more than adequate for such purposes. A separate module called BrandManager lets non-technical users specify model inputs, such as alternative marketing plans, on an Excel spreadsheet. BrandManager then imports the spreadsheet, loads the data into MarketSim proper, and runs a model with the new assumptions. Users can save their inputs as scenarios, allowing them to easily compare different options. They can view results on the screen, selecting the scenarios, products and measures to compare, and displaying them as a table or graph. Results can also be exported back to Excel for further analysis.

MarketSim was introduced in 1996 and has been the primary focus of DecisionPower’s business since 2001. The end-user portion of the system runs on a Windows PC, while data—which can be quite voluminous because of the all the historical detail—is typically stored on a central server. Small models can run on the end-user system but larger ones are often sent to a separate modeling server. DecisionPower offers hosted options for clients that prefer not to install the system in-house. Running a single scenario may takes about ten minutes for a simple model and 45 minutes to an hour for a very complicated one. Companies often run multiple scenarios—sometimes hundreds—as they explore alternatives to identify optimal marketing strategies.

DecisionPower charges from $100,000 to $300,000 to develop a MarketSim model, depending on the project. Clients can license the completed model to run in-house for $100,000 per year. DecisionPower has sold 60 to 80 MarketSim models to date, mostly to consumer package goods manufacturers. A lower cost option, MarketSimExpress, is available for quicker, simpler projects.