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Maxis Communications

Tobi Konitzer

Last edited 6 days ago by Tobi Konitzer

Background documents

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Source Material⁠

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Data Governance Policy⁠

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ROI calculation⁠

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Ocuboost Architecture:

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High-level Pilot Project Scope

Maxis business challenges being addressed:

Optimize acquisition costs of net new e-commerce customers

Leverage the latest in AI/ML technologies to get more value from its first-party data and enhance and extend Maxis’ existing data science capabilities.

Solutions to be deployed to deliver the pilot

Ocurate Value-based bidding on Google Ads

Access to Ocurate Portal to allow for campaign tracking

Ocuboost feature share (Ocuboost)

Real-time revenue predictions share

Maxis teams involved in the pilot

Marketing (CMO is primary exec sponsor)

Data Science (CIO, who owns Data & AI teams)

Asif Muhammad, Head of Data & AI CoE, to lead the pilot effort

Digital (reports under CMO and is responsible for Maxis and Hotlink mobile app, as well as e-commerce advertising)

Proposed pilot duration

93 days (3 months+)

Pilot Use Cases & Success Criteria

UC1 - Acquisition Optimization

Core KPI = 15% increase in Return On Ad Spend (ROAS) for net new customers on Google Ads

UC2 - Ocuboost Behavioral Features to enhance any one of the following a) Churn prediction, b) LTV/per-customer-revenues prediction and c) Prepaid to Postpaid conversion

Core KPI = % increase in performance against existing Maxis churn and LTV predictive models and baseline pre-to-postpaid subscriber conversion rate

Digital media discovery

What is the Maxis' ad spend on Meta and Google respectively, both monthly and projected for the next 12 months?

What is the average customer acquisition cost (CAC) on Meta and Google?

What is the ARPU (12-months or based on agreed-upon window for per-customer revenues) for customers acquired digitally?

Describe your current optimization efforts on Google Ads?

Do you work with an agency for digital media management?

Updated on July 3

Maxis Pilot Use Cases & Success Criteria

Maxis Pilot Use Cases & Success Criteria

Name

Solution

Differentiator

Use Case

Success Criteria

Go Deeper

UC1 - Acquisition Optimization

Ocurate VBO

Problem statement: In a highly competitive at penetrated market like Malaysia, the vast majority of service provider web ads come from competitors (i.e. switchers) and it is extremely difficult to predict - at the moment of purchase - which ones are worth acquiring and at which price point

Outcome: acquire customers with higher LTVs/revenues at less cost automatically and consistently over time

Value: reduced customer acquisition costs, increased customer LTV, increased profitability

Core KPI: 15 % increase in Return On Ad Spend (ROAS)

Validation of ML accuracy predicting per-customer revenues for newly acquired customers (initial KPI)

+15% ROAS increase vis a vis control group (core KPI)

Open

There are no rows in this table

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Maxis PoC Phases and Capabilities

Maxis PoC Phases and Capabilities

Phase

Sub Phase

Name

Outcomes

Requirements

Activities

Deliverables

Delivery Start

Duration

Phase 1 - Occurate

Phase 0

Ocurate Initial Onboarding

Integration into key data sources

Share access to Ads API (Marketing)

Install Ocuboost (IT/website owner) ~ deployment doc here

https://ocurate.docsend.com/view/8hqkti3p8ek7u6ee

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Ocuboost will reconcile languages on the backend

Share CRM data (ecommerce director) ~ overview doc here

https://ocurate.docsend.com/view/rf6byqu5fws3mggd

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ID integration

We will use UUID for ID integration, which is observable directly through Ocuboost, and can be shared as part of the CRM data, allowing Ocurate to merge the back-office CRM data with the event-type data Ocuboost collects updated on July 3

Data residency

Which region is preferred for AWS/GCL deployment? (assumption: Malaysia)

30/07/2024

1 day

Phase 1

ML development

Ocurate trains custom Deep Learning algorithm for per-customer revenues

Data assumptions meeting; agreement on the time window for LTV/per-customer revenues used to optimize ad spend updated on July 3

30/07/2024

45 days

Phase 2

Preliminary KPI: ML accuracy

Validation of ML accuracy predicting per-customer revenues for newly acquired customers based on scaled Mean Average Percentage Error (MAPE) (i.e. MAE/Actuals)

Validation meeting

Validation collateral

14/09/2024

1 day

Phase 3

Integration into Maxis data lake

Integrate real-time revenue predictions into Maxis data lake for free use

Access to real-time data stream of revenue predictions

14/09/2024

1 day

Phase 4

Integration into Google Ads APIs

Integrate real-time revenue predictions into Google ads API to train Google’s bidding algorithm

14/09/2024

1 day

Phase 5

Integration into Ocurate Portal

Integrate data into Ocurate portal to allow tracking of campaign performance including value-basedbidding

Access to Ocurate Portal

14/09/2024

1 day

Phase 6

Train Google impression bidding algorithm based on real-time revenue predictions

Google Ads API ready to place bids based on Ocurate ML

15/09/2024

30 days

Phase 7

A/B test: ROAS increase

Matched market test to compare ad effectiveness in terms of ROAS, i.e. predicted LTV/per-customer-revenues with a time-window agreed upon in the data assumptions meeting: CAC, of value-based bidding vs. conventional/current bidding updated Jul 3

Carve out a single acquisition campaign or a blend of acquisition campaigns anywhere within the alphabet ad real estate (Search, Video/Youtube, Shopping Google PMAX, Youtube, Performance Max, Smart Ads) that generates at least 500 conversions every two weeks. updated Jul 3

Ocurate dashboard allowing tracking of ROAS increase

15/10/2024

15 days

Phase 8

Core KPI: ROAS increased by 15%

Currently, the KPI for Maxis’ acquisition campaigns is CPA (Cost per acquisition/customer acquisition cost), for this pilot, KPI will be moved to ROAS (predicted LTV/per-customer-revenues with a time-window agreed upon in the data assumptions meeting: CAC) for both the treatment and control arm updated Jul 3

01/11/2024

1 day

Control

Project Management

Regular cadence created to capitalise on early momentum

Successful delivery of outcomes and deliverables

documented risks and issues and mitigation actions monitored and tracked

weekly status meeting allowing early intervention in case of issues or delays

risks and issues log

project management plan

project progress reports

meeting minutes

30/07/2024

93 days

Phase 2 - Feature Share

Phase 0

Data Discovery

Assessing and evaluating fitness for purpose of source data (Ocuboost and derived shared features from CRM) to enable and drive the commercial outcomes for Maxis

Discovery: Understand Maxis data platform?

Prioritization of features identified

Data Discovery

Data Review

Definition / write-up of initial key decisions / KDR around clients data

Initial Data Dictionary /ICD

02/08/2024

30 days

Phase 1

Data ingest refinement

Evaluate source data initially shared

Analyze predictive power of features collected in phase 0 for various Maxis production models

Refine feature share

02/09/2024

15 days

Phase 2

Enrichment Service

Production Deployment Enrichments

Share data back to Maxis data lake

Data share pipeline from Ocurate VPC to Maxis production servers

02/10/2024

15 days

Control

Project Management

Regular cadence created to capitalise on early momentum

Successful delivery of outcomes and deliverables

documented risks and issues and mitigation actions monitored and tracked

weekly status meeting allowing early intervention in case of issues or delays

risks and issues log

project management plan

project progress reports

meeting minutes

02/08/2024

88 days

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Timeline

Timeline

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Resourcing

Ocurate will allocate

Dedicated data scientist

Dedicated marketing specialist for campaign integration

Dedicated dev ops support/data engineer

Compute and privacy

Ocurate embraces practices that promote security, observability, and accountability. We choose technologies and services which are standard within the industry, make heavy use of managed cloud services, and ascribe to the principle of least privilege.

Ocurate uses managed, serverless cloud infrastructure to build its products and data processing systems. All of Ocurate’s cloud resources are hosted in AWS and Google Cloud in the appropriate geographic regions necessary to comply with local regulations (e.g. GDPR data in Europe, PDPA data in Singapore, etc.). Client and customer data is encrypted both in transit and at rest. Access to such data is strictly limited only to the Ocurate personnel who need it to perform the specific data processing contracted by our clients, and all accesses are auditable and can be traced to the individual who initiated the access.

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