Data is everywhere and growing. As a marketer, this is a dream world. Your audience is generating tons of data from your web visits, mobile apps and you have partners giving you data. This is rich customer journey data.
However commonly used terms like Big Data and Analytics can mask the complexity of realizing your ROI gained from analyzing all this data. The promise of the visual tools and Hadoop based platforms is all great but if you don't have a data strategy in place all the tools are just shelf ware.
Many smart CMOs and analysts I have worked with, spend most of their time on getting their data right to begin with, working closely with their data team.
The key, is to have a data strategy for your marketing data using the 3 M’s – Management, Metrics and Metadata.
1. Managing your data: Is all of your data organized such that it’s easy for your analysts to access. Often times there are tons of SQL and NO-SQL /Hadoop instances and then there are a hundred spreadsheets and the analyst, with limited tools, has to make sense of all this data. Incomplete data can lead to inconclusive analysis and you might as well flip a coin to make decisions! We marketers like clean data sets and to go from raw events to aggregated data sets takes a lot of work.
2. Metrics: Do you have a sense of the key metrics to measure? Dashboards, showing nice looking trends of your daily visitors don’t count (even if you call them KPIs!)
Have you identified important metrics with deeper analytic value such as campaign ROI by segments, retention by segment, customer journey metrics by source of acquisition: metrics that matter to your business? These kinds of deep metrics are the ones you want to organize, measure and monitor periodically. Campaign attribution and analysis of all your metrics using behavioral segmentation of your visitors/users can lead to a huge ROI when done right.
3. Metadata: Are you happy with the quality of analysis and your confidence in the accuracy of the data? What derails many analytics projects is a lack of faith in the data. Where does the data come from and what was the context, how does it get transformed and aggregated and can you trace an insight to the raw data source? If you maintain your metadata well you can trace and reconcile your analysis easily. What is called as “ETL”, especially in the Big Data world is essentially a black box, with the metadata hidden away in esoteric scripts. In the absence of such metadata the source data for an insight is hard to trace and so managers can’t validate the insights and talk about them convincingly.
Marketers and Marketing heads need to look deep into their data and go beyond easy “low hanging fruit” solutions to tackle the hard problems of data management, metrics and metadata in order to derive real ROI on marketing spend.
Originally posted on Data Science Central