How predicting customer lifetime value can optimize marketing investments
Introduction
Understanding the long-term value of customers is critical to any food delivery business. Lifetime Customer Value (CLV) represents the total value of a customer over the entire duration of their relationship with a company. By predicting this metric, you can make smarter decisions about how to allocate marketing resources to maximize impact.
In this article, we will explore how predicting CLV can allow any food delivery business to optimize their marketing spend, focusing on strategies within Search Engine Advertising (SEA). We'll examine how using predictive machine learning models improves efficiency and why this data-driven approach is critical to any company's growth and marketing strategy.
The Role of Advertising on Search Engines
Search Engine Advertising (SEA) plays a vital role in how food delivery companies connect with potential customers. It is a technique used to place ads on search engine results pages, where people are already looking for meal delivery services. The beauty of SEA is that it can drive high-intent traffic to the website: people ready to make a purchase.
A key component of the SEA strategy is Google's bidding target return on ad spend (tROAS). This tool allows you to automatically adjust bids on your ads to achieve a targeted return on investment. We essentially tell Google the average amount we aim to earn for every dollar we spend on ads, and Google's algorithms try to make it happen. This smart bidding strategy helps us use our advertising budget efficiently, leading to more effective marketing campaigns.
For this to work as intended, you need a measure that evaluates each conversion appropriately to fully leverage the power of Google's tROAS algorithm. This is where expected Customer Lifetime Value (CLV) comes into play. By understanding the long-term value of each converted customer, you feed Google's tROAS algorithm with data that goes beyond the first purchase, allowing the system to optimize the conversions that deliver the most value over time.
Predictive Models for Customer Value
ML/AI product teams develop and maintain thousands of models, powered by the behaviors and transactions of millions of customers and billions of data points. Models are built on machine learning platforms that facilitate training, validation, deployment and monitoring of scalable systems. Additionally, feature stores centralize management of model inputs, simplifying retraining and ensuring consistency and reusability.
Predictive modeling is crucial to predicting Customer Lifetime Value (CLV) and Customer Campaign Value (CCV). The capabilities of machine learning algorithms are exploited to anticipate future customer behaviors. These models take into account a variety of customer characteristics, such as acquisition channel and ordering behavior. Using these characteristics, you can paint a detailed picture of your customers' patterns, preferences, and potential longevity.
By evaluating the model's predictions against actual results, you constantly refine your approach. This measurement helps you understand the difference between what a customer's value is expected to be and what it actually is, allowing you to make data-driven decisions that improve the efficiency of your marketing spend.
Positive Impacts on Marketing Efficiency
Our investment in predictive modeling for CLV and CCV has produced tangible results, particularly in the sphere of marketing efficiency. One of the most significant results was the reduction in customer acquisition cost (CAC). Through the strategic application of these models, we achieved an increase in return on advertising investments (ROAS) of 14%.
The reduction in CAC means we are able to attract new customers at a lower cost than before. By predicting which prospects are most likely to become loyal users, we more precisely target our marketing efforts. The result is that our marketing spend is not only more efficient (better return on investment), but also more effective (greater overall value creation).
The positive impact extends beyond cost savings. The efficiencies achieved through these models allow us to reinvest resources into further refining our marketing strategies and improving the overall customer experience. This cycle of continuous improvement drives growth and strengthens our position in the market.
Conclusions
Our journey in predictive modeling for Lifetime Customer Value has taken us to new horizons in marketing efficiency. By accurately predicting the value of our customers, we have optimized our marketing spend, significantly lowered our customer acquisition cost, and laid the foundation for broader application across various marketing platforms.