<｜begin▁of▁sentence｜>### Instruction:
In the context of analyzing hierarchical structures within our insurance policyholders' database, consider we have a representation of customer referrals as a directed graph (akin to a family tree). The connections in this graph are represented by edges, indicating who referred whom. The data for this scenario is reflected in the following referrals: Customer 1 referred Customer 3, Customer 2 referred Customer 4, and Customer 1 also referred Customer 2.

Utilizing NetworkX as our analytical tool, we're interested in identifying key referral intersections — that is, the common origins of customer referrals within our database. To be more specific, how might we employ NetworkX to ascertain the primary referrer, or "lowest common ancestor," for every combination of customers in this network?

Furthermore, for strategic insights, we need to pinpoint the foundational referrer for specific customer pairings. Could you describe how one might use NetworkX to determine the lowest common ancestor for given pairs, such as between Customers 1 and 4, as well as between Customers 2 and 3, within this referral structure? 

This analysis is vital for optimizing our referral reward system and understanding the influence patterns among policyholders. Please frame this query so that it can be effectively implemented using the tools within NetworkX, without providing the implementation details.
### Response:
import networkx as nx

# Create a directed graph (tree)
G = nx.DiGraph([(1, 3), (2, 4), (1, 2)])

# Compute all pairs' lowest common ancestors
for (u, v), lca in nx.tree_all_pairs_lowest_common_ancestor(G):
    print(f'The lowest common ancestor of {u} and {v} is: {lca}')

# Specify particular pairs for which to compute lowest common ancestors
defined_pairs = [(1, 4), (2, 3)]
lcas = dict(nx.tree_all_pairs_lowest_common_ancestor(G, pairs=defined_pairs))
print(lcas)
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