- In many cases firms possess accurate penetration data, which allow them to generate adoption curves for each product. However, sales data disclose nothing of how many people consider, yet eventually decline to adopt a new product. We term these individuals decliners. Although usually invisible, decliners leave traces on the adoption pattern by diminishing the pool of potential adopters. A method for uncovering decliners' growth, as well as estimating the forces affecting their behavior based on adoption data is developed. Using data on adopters and decliners for four software products, we show that negative forces (i.e. internal and external) are much stronger than positive ones confirming word of mouth literature. In those markets the proportion of decliners is very high. However, perhaps surprisingly, decliners' direct influence on decline decision of others was found negligible compare to adopters influence. Study 1 validates the proposed model and its accuracy in estimating the growth of decliners, demonstrating more than 90% accuracy on average. In Study 2 we test the model's ability to forecast decliner development rates.