Marketing budget allocation tool reduced CPA by 10%

Profile

A high-street clothing retailer specialising in womenswear.

Situation

A high-street clothing retailer wanted an analytical toolset to allocate digital marketing spend across multiple channels to maximise their return on investment on advertising spend. This toolset needed to model marketing cost, profitability, and payback periods for different marketing channels including Customer Acquisition Costs (CAC), Cost Per Acquisition (CPA), and Customer Lifetime Value (CLTV).

This understanding was used to develop a strategic decision-making tool to inform marketing spend allocation for marketing executives to use on a daily basis to inform budget allocation between channels (PPC, Paid Social) and across geographies.

Action

We approached the project with data analysis to develop an understanding of the historic trends of the client’s marketing. Historical cohort analysis was used to develop an understanding of the client’s Cost Per Acquisition (CPA) and Customer Lifetime Value (CLTV) of each of their different channels and markets.

The client was provided with 3 core outputs to enable a data-driven culture of commercial decision making:

• A self-service analytics data cube with connections to Google Analytics,

• Google/Facebook Ads

• A Tableau reporting suite showing key metrics on a live basis to track performance and assess campaign success

• A bespoke decision support tool to allocate marketing budget which factors in the loyalty of customers

These were handed over to client teams to inform decisions on an on-going basis.

Tools and techniques used in this work

• Exploratory Data Analysis

• ETL Connections

• Google Big Query

• Excel Visual Basic Studio

• Tableau

Impact

By optimising digital marketing spend allocation, and targeting loyal customers, cost per acquisition was reduced by 10% while enabling the business to continue to grow.

Additionally, through enabling a self-service data culture with Tableau dashboards, and establishing a well-structured data cube, client data analysts can refocus their efforts towards generating high-impact insights to support critical decisions.

So what?

QuantSpark’s hybrid strategy and data science consultants were able to design a robust cloud-based data cube with built-in analytics layers for the client to use and create a strategic decision support tool to make recommendations for marketing budget allocation.

This ensured the data engineering and analytical tool development was rooted in commercial understanding and business practicality.