One NZ Achieves a Business Single View of Customer with dbt Cloud and Data Domain

This is the story of how New Zealand's largest telecom company tracks business and cross-sell enablement

3.91K

unique dashboard views

within the first month of launch

75%

reduction

in time to collect and research data

One New Zealand (formerly Vodafone New Zealand) was part of the global Vodafone business network. It ceased to be a subsidiary of the UK-based parent Vodafone Plc in 2019. One New Zealand (One NZ) is the largest wireless carrier in New Zealand, accounting for 38% of the country’s mobile market share in 2021 with 2.4 million customers.

In 2022, One NZ launched a business initiative aimed at elevating customer engagement and marketing for its business customers. Partnering with data consultancy Data Domain, the initiative focused on the following business value:

  • Cost reduction: Reducing reliance on external teams to access customer information and reporting, along with automation and consolidation of existing reports.
  • Improving customer experience: Reducing lead time to call customers, and enabling more proactive conversations through easier access to relevant data
  • Increasing revenue and reducing churn: Using business data to create cross-sell opportunities, targeted campaigns, and customized solutions.

Problem Statements

  • The data and analytics for business customers were disparate and inconsistent.
  • No single view of business customers for One NZ to drive strategic sales and campaigns.
  • The team lacked a single source of truth in the data to drive growth and retention initiatives, resulting in manual duplicated effort and analytics errors.
  • To build a view of accounts and engage with customers, the sales teams needed to manually collate customer information, searching across multiple systems.
  • Existing data sources, structures, and quality were complicated and poor. The solution required automated testing to resolve core data issues and expedite delivery velocity. 

To address these issues, the team created data products to consolidate service and account data for business customers. With new tools and processes, the team could ensure the build quality met the standard required to support customer communication, campaigning, and in-application capabilities.

The Solution

One NZ implemented an agile squad, consisting of a Product Owner and a team with multi-disciplinary technical capabilities. Data Domain led the discovery, design, analysis, build, and testing dimensions.

The team aimed to provide value quickly, iteratively, and collaboratively. Due to the complexity of the source data, the approach was to split the delivery into two phases: Proof of Concept and Productionisation. The solution had to accommodate core requirements and enable small incremental changes, modifications, or additions to be included in future phases.

The requirements were initiated with all stakeholders, including end users, to ensure the data product would be fit for purpose. The build was carried out with input from sales teams to identify changes early and incorporate them into the development phase.

Development occurred collaboratively with other vendors and internal stakeholders, while delivery management, across multiple workstreams, ensured value promptly. The development cycle ensured end users could immediately use the data product, understand the scope of upcoming development cycles, and receive comprehensive training to achieve high satisfaction.

The Outcomes

  • The Data Domain team deployed a pioneering solution for One NZ using dbt Cloud and Snowflake Data Cloud—now the build standard for the Tribe.
  • The team introduced new ways of working, with a new template-driven dbt development framework, and baseline build. The dbt’s automatic code generation framework increased developer productivity and enforced best practices across data engineering squads within One NZ.
  • The project’s success initiated a roadmap for continuous funding of similar initiatives and the establishment of an ongoing dedicated squad for Business, with the potential to expand into Machine Learning and Artificial Intelligence programs of work.
  • The data products became valuable assets, providing customer-facing teams with an easily accessible, consolidated view of information previously challenging to obtain or entirely missing. The consolidated customer view reduced CS data collection and research from 20 minutes to less than 5 minutes.
  • The sales teams at One NZ use the customer dashboard to make timely decisions and inform conversations, yielding 3.91K unique dashboard views within one month.

“By mastering the data we have on complex and often bespoke customer solutions, our single view of the customer capability has empowered our sales and customer success teams to more efficiently and effectively serve our customers.”

— David Redmore, Head of Enterprise Product and Commercial, One NZ

  • The Power Bl dashboard saw frequent and high usage, with positive feedback from stakeholders, making it a business-as-usual (BAU) tool. Customer-facing teams now have easy access to consolidated views with the top users consistently interacting with the dashboards hundreds of times per day.
  • The successful implementation of the One NZ Business Customer Initiative not only improved operational efficiency and customer engagement but also set a benchmark for future data initiatives within the organisation. The project’s results, including cost reduction, enhanced customer experiences, increased revenue, and reduced churn, underscored its importance as a strategic move towards data-driven growth and retention.

“This has been a game changer for our teams! We have everything we need to understand the services our customers have at a click of a button.”

— Simone Cuthbert-Scott, Head of Customer Success, One NZ

The initial success of the data platform led to new data products leveraging the same workflow and solutions. For example, customer loyalty program dashboards and enterprise account management. The team is now working on retiring legacy data products.