Our client wanted us to enhance their marketing effectiveness.
They asked us to establish insights on how they could best achieve their commercial growth targets by developing a systematic marketing spend strategy and an operating model.
Our client’s app is available for a wide range of services such as ride hailing, grocery food delivery and payments. In their core region, they are the leading player in this field but still must face a highly competitive landscape.
They were particularly looking at ways to reallocate their marketing budgets between their different business units to avoid the danger of cannibalization: For the team it was unclear how to ideally allocate the marketing spend considering the cost of engagement and customer acquisition across multiple channels and countries.
They approached us, to help them win more customers, open more cross-selling opportunities and gain more revenue with the ultimate goal of increasing the overall customer life-time value.
Our experts used an AI-driven optimization technique powered by machine learning (ML) to help our client find the optimal marketing spend allocation.
Our team approached this by following an easy three-steps plan, by:
- Assessing the current marketing KPIs and measuring their effectiveness
- Developing a minimum viable product to optimize budgets across the different business units and services, channels, and marketing activities
- Empowering our client to readjust and optimize the marketing budgets on an ongoing basis for continued growth as well as higher revenue and profits
We could help our client increase their expected revenue growth by 18% and with a 20% increase in expected net contributions – just with the right marketing reallocation and without increasing the budget.
Our experts supported our client with a long list of key deliverables that optimized their business growth. In total, we helped them:
- Define six new marketing KPIs to measure their marketing effectiveness (e.g., the ideal advertising spends on new customers, promotions spend on customer activations)
- Develop a unified data view based on multiple combined input data sources.
- Create a marketing spend optimization model build for their four business units and two different geographical regions. Then we simulated our results across three different market scenarios to account for market fluctuations.
- Lay out eleven concrete initiatives and a roadmap for the coming years. From a machine-learning-driven spend optimization model to multi-touch attribution measurement.
- In the last step, we provided concrete recommendations on optimal spend levels across their business units, marketing spend types (offline, ad and promotion), user types (new, recurrent, cross-sell) as well as their different marketing channels.
Merging these five key deliverables, we could significantly help our client to achieve better growth.
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