Getting on top of your data is a fundamental prerequisite
Posted: Sun Dec 22, 2024 9:35 am
The increasing importance of known and unknown (anonymous) digital identities and corresponding challenges adds to the complexity. Of course, data quality also plays an important french email address list role in making sure that the resultant data is usable.
Our own data trends report 2022, a study that we conducted among more than 200 data, campaign and marketing professionals, showed that for a major group of 41%, data quality is the biggest challenge when it comes to using MarTech stacks efficiently.
for MarTech success. Whilst challenges may exist, it is important to get started and potentially start small rather than delay.
Missing customer insights
Missing customer insights
A common hurdle to overcome is to generate customer insights (to drive your marketing communications). There are two issues to tackle here: data and tools. You need to be able to turn your first-party data (see above) into meaningful data to generate insights.
This is where data calculations and derivations come in: it is possible to literally multiply your collected first-party data from, say, 20-50 variables coming from upstream data sources to dozens if not hundreds of new variables for insight creation.
For example, a date of birth field could help derive an age range variable (e.g. to 30 to 40 year olds), a postal address could help derive the distance to your nearest branch or store, a purchase date and amount could help derive a series of Recency, Frequency and Monetary (RFM) value variables.
Our own data trends report 2022, a study that we conducted among more than 200 data, campaign and marketing professionals, showed that for a major group of 41%, data quality is the biggest challenge when it comes to using MarTech stacks efficiently.
for MarTech success. Whilst challenges may exist, it is important to get started and potentially start small rather than delay.
Missing customer insights
Missing customer insights
A common hurdle to overcome is to generate customer insights (to drive your marketing communications). There are two issues to tackle here: data and tools. You need to be able to turn your first-party data (see above) into meaningful data to generate insights.
This is where data calculations and derivations come in: it is possible to literally multiply your collected first-party data from, say, 20-50 variables coming from upstream data sources to dozens if not hundreds of new variables for insight creation.
For example, a date of birth field could help derive an age range variable (e.g. to 30 to 40 year olds), a postal address could help derive the distance to your nearest branch or store, a purchase date and amount could help derive a series of Recency, Frequency and Monetary (RFM) value variables.