Adobe Experience Platform – Building Connections with In-House Business Intelligence

CMOs investing into marketing technology platforms will often take into account how well developed their company’s in-house business intelligence (BI) capabilities are. That assessment usually involves understanding how well their BI teams support decision making. As companies have moved towards reliance on in-house data capabilities, Marketing organizations are enhancing the use of advanced analytics and extending the input that BI teams have into Marketing operations.

The barriers that exist to connecting Business Intelligence to the Marketing organization often stop this collaboration from happening in many companies. Namely, the subject matter experts in the two fields have very different skills and responsibilities, and the existing base of tool sets used by the respective teams are designed to solve for different problems and for different levels of technical orientation. Adobe Experience Platform (AEP) introduces features designed to structure collaborations between Business Intelligence to the Marketing organization, thereby lowering barriers. 

Operationalizing the use of Business Intelligence means enabling the BI team. Data scientists can apply traditional statistical and machine learning models to marketing optimization. In addition, BI governance over a broad range of marketing activities can introduce rigor to analysis of acquisition and engagement activities. The key to enabling BI is improved visibility into marketing and commerce data.

Adobe Experience Platform is the foundation on which all tools in Adobe Experience Cloud sit. The Experience Cloud includes the individual marketing, advertising, analytics, CMS, and commerce functions that marketers use every day in channel execution, audience planning, customer analysis, and personalization. Tools in Adobe Experience Cloud such as Analytics, Audience Manager, Campaign, Experience Manager, and Target are well established and familiar to marketers.

Adobe Experience Platform facilitates ease of working with data in the standard tools that Business Intelligence teams use. The platform’s Query Service easily connects AEP with a data workbench such as RStudio. Data scientists can pull data into a familiar and powerful analysis environment and begin to play a role in marketing decisions. For speed of collaboration across teams with different skill sets, Adobe provides a place to meet in the middle.  

Query Service acts as a point of integration between AEP and RStudio. A data scientist can select any data within AEP as with any other data source. Building models in RStudio comes down to working in the R language and drawing upon standard R packages. Most of these packages encapsulate statistical or ML tools, but this year, Adobe highlighted the use of graphics packages in R that allows BI power users to declaratively create graphics. Users who are already heavily invested in the R language and ecosystem of tools can generate PowerPoint slides directly from RStudio. 

Marketing often relies on Business Intelligence for Customer Lifetime Value (CLV) calculations.  By determining whether each customer has a high, medium, or low lifetime value, marketers can determine which customers to focus on. Whether these calculations depend solely on data from AEP or also require bringing in sales data from external systems, AEP allows a Business Intelligence team to follow its established working processes to deliver the outputs of the CLV models. 

 Once the models are delivered, Adobe again provides a point of integration so that marketers can use the output of the data scientists’ models for further analysis.  Returning the output of the BI models into AEP is easy with custom data schemas and direct data uploads. Custom schemas ensure that data attributes developed as part of the models will find a home in AEP.  All of the customer data in AEP is connected through Adobe’s core Customer Identity framework, one of the most mature, sophisticated ID management and audience sharing solutions in the market. As long as the outputs of the models maintain Adobe’s universal ID, the model outputs will connect to all of the data collected in AEP across channels.

With the CLV calculations imported back into AEP and by maintaining the identifiers of the customers, Marketing can examine data from various channels with respect to these high, medium, and low buckets. Once the model outputs are brought back into AEP, marketers can then access the CLV data in Adobe Analytics, Audience Manager, Campaign, or any number of other tools. AEP’s Customer Journey Analytics is a highly effective tool for looking at different touch points along the customer journey and quickly discovering insights. Bringing CLV to bear as a dimension, in conjunction with other dimensions like gender or fulfillment method or marketing channel, allows marketers to unearth customer behaviors and performance highlights that otherwise would have been impossible to detect. 

For the sake of speed to insights, an organization must find a way to bring their Business Intelligence team into Marketing work streams. In order to provide data scientists the flexibility of working within familiar environments, Adobe has turned to external workbenches like RStudio rather than building such a tool within AEP itself. All of AEP’s native datasets are available in RStudio through Query Service integrations, and new data attributes are brought back into AEP via custom schemas. While companies still need to actively foster a sense of community between data scientists and marketers, AEP does a lot to lower barriers to doing so by allowing each to work with familiar tools. This is one more box a CMO can thoughtfully check when looking forwards, ensuring that their marketing technology will not present an obstacle along their organization’s maturity roadmap.

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