  This paper studies cooperative data-sharing between competitors vying to
predict a consumer's tastes. We design optimal data-sharing schemes both for
when they compete only with each other, and for when they additionally compete
with an Amazon -- a company with more, better data. We show that simple schemes
-- threshold rules that probabilistically induce either full data-sharing
between competitors, or the full transfer of data from one competitor to
another -- are either optimal or approximately optimal, depending on properties
of the information structure. We also provide conditions under which firms
share more data when they face stronger outside competition, and describe
situations in which this conclusion is reversed.
