1. CRM DMP – a marriage of two acronyms

2. 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Strategy is obsolete (unconstrained player evolved and realized the advantage)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

3. 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

4. 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

5. 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.

6. 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)