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It is often believed that the consumer sentiment index has
predictive power for future consumption levels. While Granger
causality tests have already been used to test for this, no attempt
has been made yet to quantify the predictive power of the consumer
sentiment index over different time horizons. In this paper, we
decompose the Granger causality at different time lags, by looking
at a sequence of nested prediction models. Since the consumer
sentiment index turns out to be cointegrated with real consumption,
we resort to Error Correcting Models. Four consumption series are
studied, namely total real consumption, real consumption of
durables, nondurables and services. Among other findings, we show
that the consumer sentiment index Granger causes future consumption
with an average time lag of four to five months. Furthermore, it is
found that the consumer sentiment index has more incremental
predictive power for consumption of services than for consumption of
durables or nondurables, and that the index is not only useful as a
predictor at the very short term, but keeps predictive power at
larger time horizons.

In this article, we discuss the state of the art of models for customer engagement and the problems that are inherent to calibrating and implementing these models. The authors first provide an overview of the data available for customer analytics and discuss recent developments. Next, the authors discuss the models used for studying customer engagement, where they distinguish the following stages: customer acquisition, customer development, and customer retention. Finally, they discuss several organizational issues of analytics for customer engagement, which constitute barriers for introducing analytics for customer engagement.