D365 Business Applications

Success with Customer Data Platforms

winning_with_Customer_Data_platforms

With the increasing need to gain deeper insights into customer behavior – the concept of a Customer Data Platform (CDP) is still relatively new but is rapidly evolving. The challenge for business and IT leaders alike, CDPs seem to overlap with established programs such as Business Intelligence and Customer Relationship Management. Arguably the first challenge when embarking on an enterprise CDP program is positioning the initiative internally and building the business case. 

Why Launch a CDP Program?  

The primary goal CDPs serve is to build an aggregated, accurate and full view of the customer for more effective segmentation and customer targeting. If your organization has large customer data sets residing in multiple places, and business requires days or even weeks to extract specific data queries (such as transactional behavior) then building a customer data platform could be a good fit and accelerate your overall digital transformation.   

CDP Success Factors

The following factors should be considered non-negotiable success factors to ensure long-term success with a CDP initiative.  

Project Sponsor should be Head of Marketing / Head of Customer Experience

Although building out your CDP is a significant undertaking from an IT perspective the return on investment will be derived from leveraging customer insights in ways the organization hasn’t been able to do so before. Marketers should be well versed in what a robust CDP can offer them and should be crafting the initial use cases. Ultimately – business and marketing leaders should be championing the initiative.  

Use Cases Should be Defined Up-Front

Following on from the above, clear, executable use cases need to be defined upfront. The real value of a CDP is not the aggregated view of a customer, but rather what that view allows your business and its marketers to achieve. Unique ways to segment your customers, gain insights and run highly personalized campaigns are some examples of value that should be clearly understood in advance. There will be an element of discovery once the platform is in place, as the data gets richer and opportunities to leverage it become broader, but the initial use cases should be well understood at the project level and your initial ROI should be measured against those use cases.  

If you have a narrow use case, like being able to send personalized email campaigns to different segments, adopting a CDP might be like using a samurai sword when all you need is a bread knife. 

Understand your Digital Marketing Execution Approach

CDP technologies will enable organizations to extract, transform, merge, match and ultimately unify data into a single platform. However, the process and technology required to execute various campaigns will need to be understood to achieve ROI. There are various APIs and Connectors which assist with integrating CDPs to marketing execution tools. Ideally one doesn’t want to be stuck running data exports out of the CDP in order to upload them into a marketing application, not only is this time consuming but data will reside outside of the CDP.  

Plan for ETL Effort and Data Modelling

Although CDP tooling comes with a smart map, match, and merge functionality extracting the underlying data in an efficient way still requires the appropriate effort, diligence, and in some cases ‘classical’ ETL work. Security, data quality, and governance will need to be considered just like any project which involves moving large sets of data. Tools such as Microsoft Data Factory, Data Lake, LogicApps and Azure Service Bus help accelerate this process however there is no magic wand to landing the data into the CDP. To achieve quicker time to value, consider identifying the core systems for extraction to meet the initial use cases and expanding source data systems over time. It’s crucial to build out a common data model upfront so that the data attributes are known. The development that follows will then use the common data model as the requirement for the structure. 

Consider External Data

Consider external data to enrich insights and provide context outside of the business domain. For example, if an existing subscription with an external data provider exists, such as Experian, this can enrich your data even further. This will provide insights that are impossible to gain from internal sources. Other examples of this include brand affinity information, share-of-voice information, and location-centric data.  

What’s next for CDP?  

Looking ahead at this rapidly evolving technology these platforms will become real-time or closer to real-time to fully meet the demands of digital marketers. Likewise, we will see the convergence of marketing automation applications for seamless marketing execution from CDP data. Currently, Microsoft offers end to end capability within the Microsoft cloud although customer segments need to be exported into D365 Marketing. Finally, AI capability will provide more ‘out of the box’ predictive modeling and insights into customer behavior.  

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