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本帖最後由 Juthi5019 於 14:44 編輯
Select the data you want to analyze. In this case we will use: Active users Total revenues Average revenue from purchases ARPU (average value of active users) ARPPU (Average Value of Paying Users) Thanks to this, you will gain knowledge about the value of your customers. All right. But how can such data help us increase customer value? By creating a second segment! Look at the average purchase.
Revenue amount and base the second segment on that. We create it in the same way as the first one, with the difference that this time we will have to choose the value that interests us. And it is supposed to be more than $112. Finally, our C Level Contact List comparison according to customer value looks like this: Pareto's law in practice Of our buyers, 479 people are the most valuable customers who left approximately $300 in our store.
That's more than half the price of all buyers! In this simple way, we have already selected big fish. By the way – see what the ratio of the most buyers to the entire population of buyers is. They account for more than half of all revenues. Does this remind you of anything? Pareto's Law is not a fantasy. However, its use in scaling a business is a topic for completely different considerations.
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