Data science is a large field covering everything from data collection, cleansing, standardization, analysis, visualization and reporting. Marketing and CRM (customer relationship management) science is part of this larger field, employed by companies to deliver higher returns on marketing investment (spend), ROI.
Be it for online or offline marketing campaigns, keeping existing customers engaged or acquiring new customers, this field could save companies millions on their marketing spend by employing simple models like segmenting their customers base by recency, frequency and value (RFV). More complex models could save much more and give high marketing spend companies an edge over their competitors
Popular models include for customer acquisition and retention:
- Lifetime value (LTV)
- Marketing Mix Modelling and Attribution
- Retention (likelihood of a customer to churn and time to next purchase)
- Next Best Action, recommendation engines
Whilst there are many statistical methods that could be employed on each model, some more accurate than others. For its simplicity and ease of application, econometric regression modelling such as logistic regression, linear regression and survival analysis. With these models, a marketing data scientist can score their company’s customer database and quickly action for numerous campaigns.
In 2018, marketing data scientists are not only experts in modelling but also in extracting and forming complex datasets, as well as visualising this data. The core skills include SQL (i.e for Hadoop and Teradata), R, Python or SAS for modelling and Statistics, Tableau and Salesforce for visualisation and reporting.
In my blog, I will explore different areas related to marketing data science, as well as discussing new marketing technologies. Stay tuned in for more.