In a crowded retail marketplace, organizations increasingly compete for consumer time, attention and spend. Gone are the days where broadstroke advertisements and bulk email solicitations pay off. Consumers demand personalized messaging catered to their needs and preferences and delivered through their favored channels, requiring organizations to scrutinize every touchpoint for clues on how best to engage.
For those with the ability to glean customer insights from disparate data sources and the agility to tailor their engagement accordingly, the payoffs can be immense. For organizations unable or unwilling to adapt to these elevated customer demands, the choice leaves them watching their customers simply slip away.
Critical Areas that Comprise a Modern CDP
To deliver personalized customer experiences, organizations are investing in the development and deployment of specialized Customer Data Platforms (CDPs). These systems enable marketers to extract timely insights from customer data and direct tailored messaging to customers at just the right place at just the right time. The key areas that comprise the modern CDP are:
|Collect and integrate customer data
|Unify and model data to make it usable by other applications
|Deduplicate records to build a private ID graph with a single view of the customer
|Control data access and permitted actions on the data
|Create and execute models predicting user behaviors such as purchase or churn
|Use a self-service UI to build rule-based or model-based audiences
|Define and optimize the customer journey and interactions with the brand across every channel and every phase of the customer lifecycle
|Integrate seamlessly with delivery systems for both inbound and outbound customer experiences
|Understand audience and customer journey performance
The Decision To Build or Buy Your CDP
The key question facing many organizations is whether it's best to build or buy the CPD. In making the case for buying, organizations can rapidly deploy a system capable of allowing their marketers to immediately engage across a wide range of scenarios. Common data sources can be easily ingested and data assets transformed into the information needed by marketers.
Marketers can use these data in guided workflows that allow them to quickly identify customers aligned with a given marketing motion and then trigger the desired action on those customers to create engagement. Built-in dashboards and reports then allow the organization to monitor the impact of these efforts and take the insights learned into the next round of engagement.
As appealing as this narrative is, the reality is that most out-of-the-box CDPs perform better in some of these areas than in others. And as more non-standard data sources are employed, more timely access to customer information is required and more advanced analytics are recognized as offering differentiating opportunities for the organization, more and more organizations find themselves leaning on their data engineers and data scientists to build key capabilities. There's a strong case for building as organizations consider the full breadth of their needs.
A New Option for the Build vs Buy Debate
In this tug of war between building and buying, it's important to acknowledge a long history of attempts at the internal development of customer-360 solutions that have left marketing organizations eager to purchase out of the box solutions. In many organizations, the complexity of reconciling customer data from different sources and the lack of support for business-aligned workflows have left a gap between what has been delivered and what is needed to enable effective customer engagement.
That's why we advocate for a hybrid approach, where organizations buy a core set of functionality to enable the CDP architecture and augment it with bespoke functionality that closes the functional gaps in vendor solutions. This build-and-buy approach provides organizations with functionality to immediately get their customer engagement programs off the ground and the flexibility to adapt to changes and new opportunities that arise.
Maintain Governance with User-Friendly Functionality for Business Teams
With most CDPs, a build-and-buy approach requires organizations to run the customer data platform side-by-side with a general purpose data and analytics platform. Redundant copies of sensitive customer data create opportunities for out-of-sync information and inconsistent data governance that can leave organizations exposed.
The ideal solution would then seem to be a CDP that runs within the data and analytics platform, presenting marketers with out-of-the-box functionality but presenting the information assets as part of a single, centrally-governed infrastructure. And this is exactly what ActionIQ has delivered with their CDP solution, purpose built for deep integration with the Databricks Lakehouse platform.
With ActionIQ, organizations running the Databricks Lakehouse can deploy the CDP in one of three modes for deeper integration with the analytics platform. (Figure 1)
In the bundled mode, the two systems run independently while Databricks can serve as a key data source for marketing workflows enabled through the ActionIQ solution. In the Composable mode, the ActionIQ CDP and the Databricks Lakehouse are tightly coupled, creating a seamless data integration between the two systems. In the hybrid configuration, ActionIQ is deployed to a separate infrastructure but live integrations supporting federated queries allow information assets in the Databricks Lakehouse to be readily exposed to CDP workflows. In the Lakehouse-only configuration, ActionIQ is deployed directly within the Lakehouse, eliminating the need for separate data infrastructures.
The choice of deployment options allows organizations to find an infrastructure and data management configuration that works best for their organization. But regardless of which path is taken, the coupling of the ActionIQ CDP with the Databricks Lakehouse gives organizations out of the box CDP functionality as well as the full flexibility of the lakehouse to support complex data engineering challenges and address custom data science initiatives. It's the best of both the build and buy approaches.
For more details on how ActionIQ integrates with and extends the Databricks Lakehouse and enables organizations to best meet the fullest range of their customer engagement needs, read The CDP Build vs. Buy Guide.