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Disrupt yourself with a real-time, customer-centric data platform

Disrupt yourself with a real-time, customer-centric data platform

A real-time, event-driven, customer-centric data platform is a powerful and enlightening way to change how you do business. This is about how surfacing and using real-time data from the plethora of your modern and legacy applications, silos, warehouses and your interactions with customers, is a fresh way of finding those opportunities to disrupt. What you'll gain by having an event-driven, customer-centric data platform as a key foundational layer to your architecture. And how a central real-time data hub will really help you get the most out of your sales, marketing, BI and operational strategies, among others. Plus some pragmatic advice on how you can easily build, nurture and grow it to fit your business.

Steven Ringo

July 11, 2018

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  1. 2016 → Single-vertical Mobile 2017 → Multi-vertical Mobile Energy Broadband

    Devices 2018 → v.Next Consolidation Efficiency Rationalisation
  2. Our data lives in four verticals (and other supporting systems).

    Getting meaningful, contextual access to their data is hard — each system has a different domain and data models and ways (or not) to get to that data Our systems each have their own way (or don’t) to provide promotions, incentives, rebates and discounts each with their own quirks and features. We have no central place with a reliable compound aggregated view of customer data. We have no central co-ordinating function to manage multi- vertical initiatives We make do by creating workarounds and suboptimal processes
  3. Make sense of data after the fact Rely on data

    warehouses for operational exercises and targeting Lose valuable history of the data Lose context as to how the data got in there originally Finding the signal from noise ETL Hell
  4. Signed up Steven, broadband, premium Bought a device iPhone X,

    256GB, Paypal Changed mobile plan UNL 15 → UNL 20 Clicked an incoming link Google search results → mobile landing page Redeemed a promo code Mobile, 1 month free, code 18649 Consumed data 100MB, RP 6 Called the service centre Mary, Rate plan change, 11 minutes Made a call while overseas India → Australia, 3 minutes, 55378008 Moved house 34 Eurack Court NSW 2587, Electricity Paid bill $89.44, on-time, 1 Feb 2018
  5. vs

  6. • Great for one-on-one conversations • Conceptually easy • Vernacular

    • Conference calling impossible • Conversation ends when string snaps • Tightly coupled Point-to-point
  7. By nature, point-to-point protocols “lock” 
 that data to the

    parties having the conversation Information relevant to others is hard to surface Point-to-point
  8. Broadcast • Group or one-on-one conversations • Interaction conceptually more

    complex • Common language for a conversation • Selective listening to relevant conversations • Conference calling intrinsic • Conversation continues even if one walkie-talkie malfunctions
  9. Stream readers Parse each event, identify named fields to build

    structure, and transform them to converge on a common format for easier, accelerated analysis and business value Dynamically transform and prepare your data regardless of format or complexity, e.g • Derive structure from unstructured data with grok • Decipher geo coordinates from IP addresses • Anonymize PII data, exclude sensitive fields completely • Ease overall processing independent of the data source, format, or schema.
  10. Event processing • (Id)entity resolution and matching • Relationships and

    linking • On-the-fly ETL • Segmentation • Personalisation • NLP • Sentiment • Statistics
  11. Time is of the essence • Natural triggers • Scheduled

    • Near real-time • Synchronous • Asynchronous • Report/batch • Eventual consistency
  12. Data enrichment Number of visits to x page in the

    last 3 days Number of products owned Number of times called call centre Demographic for address from third-party Address cleansing Should receive offer
  13. Snapshots and lenses • Snapshots are the key to a

    single view of the customer. • We can combine multiple sources of truth to create fit-for-purpose “lenses” • Relational • Document • Graph • Lake
  14. Databases fit for purpose Caching → front-ends Reporting → finance

    & accounting Indexing → search Aggregation → usage Athena Redshift
  15. APIs everywhere • APIs no longer defined by vertical •

    APIs optimised and fit-for-purpose • Enabling marketing automation • Back-end for front-end (BFF) • Alexa, AI • Third-party integrations • Consolidated views, e.g. dashboard, call-centre • Speedier customer experience • Keep devs happy :-) • Easier to respond to business requirements
  16. The CDP is a system designed to ingest and unify

    customer data from all sources in real-time, and make complete and linked customer data easily accessible to the business and digital services — a single customer view. This will be a key enabler in the migration of platforms by having better quality unified data readily available for import to new systems. As the key for better engagement and marketing, a CDP provides visibility into the entire customer lifecycle. A CDP enables engineering the optimal customer experience in real time by being a central repository for everything that is knowable about a customer. Takeaways
  17. A central location and clearinghouse for everything that is knowable

    about a customer. Identity resolution to ensure that discrete customer records in silos resolve to the same individual or household through deterministic matching. Pre-built integrations with popular and other third-party vendor systems as well as APIs and SDKs to enable integration with custom-built or in- house developed systems. Dynamic, on-the-fly segmentation for marketers and other individuals to instantly discover and create segments, and push segmented data into marketing technology platforms. Democratisation of data by providing anyone in the business with access to unified data in both batch and streaming formats. Takeaways