Structural analysis data required

Required: Transaction data

By definition, a structural analysis is measuring the cohort transactions over time. For this purpose we require a transaction level data set.

For consumer companies these are the actual transaction details, when and what the customer bought. For B2B companies this is subscription and payment data.

Optional: Marketing/Sales data

An LTV/CAC ratio is an important metric for companies to know. Any ratio below 1 means the company is losing money and destroying value. A ratio of 3+ is generally considered good. In addition, different channels can convert at different costs and attract different kinds of customers with higher/lower LTVs.

To extend the structural analysis with marketing data, we need all channels and spend and conversion attribution if possible.

Optional: Customer Metadata (non-PII)

Customer segmentation based on product purchase, geography is low hanging fruit and should be easy to include. However, to really understand your great from bad customers, the data can also be extended with product usage patterns, customer support interactions, CRM information, and any other metadata you may have about the customer.

Note: We almost never want to be able to identify individual customers, and sending PII (personally identifying information) can break GDPR and other privacy-focused laws, so we never want personal data like names, email addresses, social security, etc.