Today’s brands require ad agencies that provide data-driven marketing based on predictive decision making that identifies their target customers. The fragmented consumer marketplace makes this approach a necessity.
Instead of taking broad strokes, we help agencies create and segment their audiences so they can deliver even more targeted content. We then use data to predict how successful campaigns will be with those target audiences.
The leaders in the agency business recognize a need to improve their predictive analytics, but find it difficult to organize the three primary pillars of support necessary for success:
An effective team has both statistical practitioners and data experts who run hygiene and integration.
Most forward-looking enterprises are migrating to the cloud and AI/machine learning.
Nothing is more valuable than the first party data collected by the brand. Yet, to truly understand the consumer and create personalized campaign messages, you often need third-party databases.
The predictive marketing cloud platform, Reach, is available in grey label and offers a unique technology for your agency. By identifying consumers most likely to become customers, agencies with Reach can generate real purchase results, not just topline vanity metrics like clicks or unqualified leads. Unlike a DMP, we give you total control by using real consumer data (name, address, email, phone plus demographics, behavior, purchase transaction, media preferences, propensities) for improved customer acquisition both offline and online.
Primary research market segmentation is the only method for defining the unserved audience. At DDG, we refer to this as needs-based segmentation. We start with the broad, heterogeneous audience of consumers that most agencies attempt to market to and refine it into smaller discrete, homogenous, and identifiable groups to which smart agencies can direct precise messaging for their unique needs.
Many agencies are dependent on transactional segmentation of the brand’s purchase data. Data like this tells you what consumers did, but not why. Knowing why consumers did what they did enables agencies to develop and deliver messaging that’s relevant and meaningful to those clearly-defined segments of consumers.
The simple input of a customer file into the Reach platform will create a descriptive audience for marketing. The platform has a database of individual and household values that can create a 360-degree view of the consumer.
Predicted Response Model
The input of campaign contacts and campaign responders/buyers into the Reach platform will create a model that predicts the response rate and the ROI for a campaign. The platform will append third-party data to the input record and then apply machine learning to produce a predictive model for the desired outcome.
Both the Lookalike and Predicted Response Model allow the generation of a Prospect audience to facilitate customer acquisition.
Offer industry leading predictive capabilities that set your shop above the playing field.
Make your smart people even smarter. Start fast with plug-and-play managed services and move capabilities in-house with predictive skills training.
Build your business by delivering increased results for your clients by offering the best, targeted audiences.
Not only can we give you up-to-date data, we can process and add insights to the data you already have. But that's not all; we'll help you automate your omnichannel marketing campaigns, too.
Build your marketing database with lists for any industry and any dimension of information (demographics, shopping behaviors, and attitudes). Process the data you already have to ensure it's updated, relevant, and accurate.
Take advantage of data-driven research methods, business intelligence tools, and models to get the most value out of your data and draw insights that will help you market like a ninja.
Whether you're marketing digitally or through offline channels, predictive analytics can improve your outcomes. The ability to understand who is responsive to your offer drives the correct messaging.