CRM DMP – a marriage of two acronyms

Narrow, transferrable solution The problem in data expansion environment Inadequate time-to-market in traditional analytical value chain Narrow scope of the fit-for-purpose advertisement DMPs (Data Management Platforms) Narrow, transferrable solution Advertisement DMP methodology Wide scope of potential needs Warehouse Analytics Strategy Execution By the time this value chain is realized: Data is insufficient (more data appeared in the value chain and it is not captured) Analytics is constrained by the Warehouse structure Strategy is obsolete (unconstrained player evolved and realized the advantage) Analytics supports narrow range of decisions feasible in implementation and is not supporting growth opportunities Analytics can not realize strategic requests due of the limitations of the warehouse Underlined by statistical methods incompatible with legacy CRM Does not allow understanding the person “as a whole” Does not provide sufficient support for purposes other than web advertisement Excludes existing analytical solutions Requires breaking marketing processes that work well

The solution: CRM DMP – a child of two parents Combines merits of two approaches, allowing to design wide-scope and fast-to-market analytics Fast Advertisement DMP CRM DMP Time to market Traditional CRM Slow Narrow Solution Scope Broad

How Is designed as “open data ecosystem” (e.g. allows ingestion of new data sources, continuously). Can ingest from systemic layers “closer to the processing core”, bypassing aggregated analytical environments Remains highly customized and is driven by business strategy and tactical objectives (not one purpose-fit-all) Optimizes for tradeoffs unique to a specific business (data volume, speed of decision, flexibility) Incorporates existing analytical solutions and models (segmentations, scores etc.) Developed via simultaneous process where data repository and applications are created simultaneously. Underlined by “temporary connectors datalake” approach (as opposed to set-in-stone connectors of relational databases) Supported by data storage and data manipulation technological advancements (some open source) Requires collaboration between functions (architecture, engineering, data science, analytics, strategy) Can be housed in cloud based and hardware based environments

On the process side… is also called design thinking and in software it is called Agile. Stage 1 Stage 2 Stage 3 Stage 4 From this Warehouse Analytics Strategy Execution Stage 1 Stage 2 Stage 3 Build datalake Build basic data frames Develop scalable and verified data organization modules Build early R&D prototypes * Iterate on early R&D and move into analytical development Commercial analytical development To this Build high level strategy Translate high level strategy into tactical level Specific offer development or customer treatments (or other data driven decisions) Discuss (not do) production Start working on production Production environment and DevOPs Discuss (not do) execution Add integration or in market execution layer Integration, independent execution and testing This looks humongous. Can we do it cheap and fast? Yes. How come? Technologies are there to support. New tools (I call them shovels :) allow building analytical solutions from raw data dumps. I do not need warehouse. I already have first models, from the dump I took 3 months ago. Compare this to $30 MM cleansing exercise and building warehouse for 3 years.

Platform Functionalities On technology side – processing high velocity and large volume transaction data, for fast-to-market bespoke analytics Architecture is dictated by the needs of analytics and data science Extensive usage of new and open source technologies The data lake stores raw data, in the same form the data source provides, without definition of schema at all. Each data source can use whatever schema it likes. It’s up to the data consumers to make schema of that data for their own purposes. Platform Functionalities Data Sources Data Preparation Decision Support Processing Relational Data Sources Representation (steady) Data Exploration Alerts, Push Notifications UI CRM Manager Spark Streaming Dashboard CRM, Policy Manager, Topology / GeoRef Spark Recommendations Elastic Search Latest state (low velocity) Stream Network Logs Permanent Storage Pull GUI Profile Builder Data Transformation CDR, Probes ETL Profile data: Alerts, Reporting Stats Actions (high velocity) Kafka Data Modelling Actions Push Notifications Prepaid Descriptive, Predictive CRM, Rules User State: Counters Thresholds Third Party Data Non-PII volume-intensive data source Geo Fencing (including Offer Management)