- Why did one user convert and another not?
- Why does one campaign perform better than another?
- Why did a customer cancel their order or subscription?
- Why did a user require assistance from support?
- Why is tool A reporting 20% more than tool B?
- Why are certain data points missing in tool C?
- Why is X higher than Y, if that’s logically impossible?
- Why is the value of Z suddenly a string, not a number?
When aiming to maximize conversion rates, increase average cart values or set individual prices for products and services, optimization and personalization efforts require data.
Identifying what drives conversions allows funds to be allocated efficiently. Consent-aware cohort-based or fully anonymous tracking can provide a full picture.
Most companies accept churn and are only trying to reduce future loss. However, there are usually clear indicators that customers may churn that can be used to prevent it.
Instead of having many tools create redundant, usually inconsistent, and often low-quality data, it’s better to create data only once and focus on its quality.
“Garbage in, garbage out” is a huge problem, but the costs increase exponentially, because the further downstream the more effort it takes to fix data, if it’s even possible.
Instead of admitting that the implementation is not right, data teams often blame the tool. However, a new tool without an improved implementation won’t produce better results.
Even though most never do, some new implementations may produce quality data at first. However, it requires constant effort to maintain that level of quality.
Due to more focus on privacy, browsers and mobile apps make it increasingly difficult to collect behavioral data, which requires sophisticated data collection strategies.