Personalization for Always-on Marketing | SQream

Personalization for Always-on Marketing (Next-Best-Action)

Tier O in Europe has adopted SQream to create a scalable, cost-effective, ultra-fast processing layer that can join batch and streaming data, and run complicated transformations with the ability to store data on the Cloud in relational structure.

Contributes up 5x faster
to 5% to your net profit
ROI of one
5x faster
Industry Vertical: Telecom
Economic Buyer: CMO; CEO
Enabler: Head of Engineering

Why Do Anything?

The solution the telecom had in place was failing, not stable, and required a lot of patching and ad-hoc resources, and still didn’t deliver smooth operation. The Tier 0 operators were losing customers to the competition. In addition, the solution they had in place didn’t have the capacity to incorporate new parts of the business, such as cable customers and IoT.

Why Now?

The customer lost approximately one-million Euros per day, due to inaccurate data. This could continue from days to weeks.

Why SQream?

• Ability to rapidly join many tables with large datasets.
• Ability to recognize only delta of changes
(incremented instead of full load).
• Ability to run on- and off-premise and link between
them.
• Real-time streaming Pub/Sub with Kafka, trigger
transformation based on business events.
• SQream runs 5x faster than the other competitors.

Fastest time to insight on any size data

Business Challenge

By tailoring actions to individual customers, personalization helps telecoms differentiate themselves in an increasingly competitive market. That’s a compelling proposition for both fixed and mobile operators, who are seeing less space to differentiate based purely on network and infrastructure. Consumers expect personalized experiences, offers and services, and tend to recommend a service when it’s personalized. Personalization is a powerful way to communicate with customers and tailor various messaging / services to their needs. A personalization strategy allows the organization to segment and distinguish their customers. Personalization can work inbound and outbound – for example – when a customer has reached their capacity or wants to cancel their contract. Personalization can be achieved the right efforts from various resources, including sales people and the customer service representatives.

Situation/Pain Business Impact

Situation – At the beginning of the process, the bank performed OFSAA in a single box. 10TB of compressed data were analyzed daily. It wasn’t an efficient process, taking 18-hours to complete. Reports were not generated on time and data was not available for decision makers to meet regulatory requirements in timely fashion.

Pain – The bank needed an ultra-fast platform that was able to shorten time to insight, was greener, and would reduce Total Cost of Ownership. The IT department had a goal to accelerate the data preparation phase using GPU technology to enable rapid and ad-hoc reporting, while also reducing the current footprint and saving CAPEX and OPEX.

Business Impact – Using SQream’s OFSAA acceleration solution, the bank managed to reduced the overall process to 5.5-hours, having only 1.5-hours allocated to data preparation. Lead time for report generation was reduced significantly, taking a fraction of the time.

SQream Benefits

Tier 0 in Europe has adopted SQream to implement the process described above. With SQream plaftorm they get the right data at the right time in the right structure to create marketing campaigns. SQream utilizes data flow from multiple data sources: customers, subscription, and household and matches this with their billing system. SQream identifies an actionable entity. Once completed, the recommended NBA is matched by SQream.

SQream Solution Components

SQream Kafka sink; SQream Kafka connect; SQream Avro connector; SQream ad-hoc query module; SQream for Multi-Cloud; SQream SQL Joins.

Trusted by:

Architecture Considerations

1.

SQream platform ingests data (files, events, structured data) from multiple resources.

2.

SQream engine performs various joins and aggregations in order to prepare the data for Pega.

3.

The processed results are ready to be sent to the CRM various data stores.

4.

The agent has an up-todate Next-Best-offer for every customer.