TTF: A Trapezoidal Temporal Fusion Framework for LTV Forecasting in Douyin

TL;DR

This paper introduces TTF, a novel framework for early-stage, channel-level LTV forecasting in Douyin, addressing challenges of unaligned multi-time series, SILO, and volatility, leading to improved prediction accuracy.

Contribution

The paper proposes the Trapezoidal Temporal Fusion framework with a multi-time series module and MT-FusionNet structure, specifically designed for volatile, non-stationary LTV data in online systems.

Findings

MAPEp decreased by 4.3% with TTF

MAPEa decreased by 3.2% with TTF

Framework deployed successfully in Douyin online system

Abstract

In the user growth scenario, Internet companies invest heavily in paid acquisition channels to acquire new users. But sustainable growth depends on acquired users' generating lifetime value (LTV) exceeding customer acquisition cost (CAC). In order to maximize LTV/CAC ratio, it is crucial to predict channel-level LTV in an early stage for further optimization of budget allocation. The LTV forecasting problem is significantly different from traditional time series forecasting problems, and there are three main challenges. Firstly, it is an unaligned multi-time series forecasting problem that each channel has a number of LTV series of different activation dates. Secondly, to predict in the early stage, it faces the imbalanced short-input long-output (SILO) challenge. Moreover, compared with the commonly used time series datasets, the real LTV series are volatile and non-stationary, with more frequent fluctuations and higher variance. In this work, we propose a novel framework called Trapezoidal Temporal Fusion (TTF) to address the above challenges. We introduce a trapezoidal multi-time series module to deal with data unalignment and SILO challenges, and output accurate predictions with a multi-tower structure called MT-FusionNet. The framework has been deployed to the online system for Douyin. Compared to the previously deployed online model, MAPEp decreased by 4.3%, and MAPEa decreased by 3.2%, where MAPEp denotes the point-wise MAPE of the LTV curve and MAPEa denotes the MAPE of the aggregated LTV.