The decision: A useful test distinguishes competing explanations.
Examine the situation
Once the distributor knows customers call about delivery dates, two explanations remain: customers cannot find an existing update, or the update is unreliable. Sending more messages will help only if the information behind them can be trusted. The choice of test follows the uncertainty that could change the investment.
A way to work through it
Separate assumptions about the customer’s need from assumptions about the business’s ability to supply the outcome. Decide what observable result would support or challenge each explanation. A manual message experiment can test whether clearer communication reduces a particular question; a data audit can test whether a promised date is dependable.
Follow the example
Fictional teaching example. For two weeks, the team tags call reasons, checks delivery data against actual dispatch, and sends a clearer confirmation to a small set of repeat buyers. It records what remains unresolved. This can inform the next decision; it cannot prove that a full portal will succeed.
Try it with your team
Write three assumptions. Select the one whose failure would most change the plan. State the test, participants, observation, decision threshold and review date before running it.
What this does not establish
A small convenience sample may be biased. A change in call volume can have other causes, so record the context and avoid a confident causal claim.
Output: one test protocol and a decision rule.