Imagine yourself as a young planning director at a media agency.
A client enters your agency, complaining about the performance of the last campaign. You check the campaign reports and find the key metrics to be within industry benchmarks. You reassure the client that your team is doing the best to help the client.
As humans, our judgements are constrained by limited, subjective experiences and incomplete knowledge. This impairs our decision-making process and results in inaccurate conclusions.
This is where Data Science can help.
Instead of relying on an individual’s judgement, data science techniques allow us to harness information from more data sources to make better decisions. For instance, we could check the historical records of the client’s previous campaigns to uncover possible issues previously overlooked.
Data Science for Marketers: Getting Started
A study conducted by a leading retail grocery store found that men who buy diapers for their kids are most likely to have beer in their carts. This study motived the grocery store to move the beer aisle closer to the diaper aisle, and they noted a 35% increase in sales of both.
The study has become a popular folklore among marketers evangelising the adoption of data science for predicting consumer behaviour and preferences. To fully appreciate how data science can help marketers, we must start from the basics.
There are four key steps in any data science study. First, the data must be processed and prepared for analysis. Next suitable algorithms must be identified. Following which parameters of algorithms have to be tuned to optimise results. This finally culminates in the building of the model that are then compared to select the best one.
Applications of Data Science for Marketing
Micro-Targeting & Segmentation
Machine learning techniques like Clustering allows marketers to sort customers into groups. This enables them to deliver target marketing campaigns to a niche segment. Take for instance a person who likes Avengers – it is highly like that he or she would also enjoy similar movies such as The Guardians of the Galaxy. Clustering helps by identifying common preferences or characteristics in your dataset.
Data-driven marketing is transformative for businesses of all sizes. For smaller organizations and agencies, which were once cut out of costly television advertising. Digital provides a new cost-effective marketing channel.
Marketers can leverage text mining techniques to understand customer sentiments. Past data is increasingly used to test scenarios and experiments in real-time rather than in hindsight or on an intermittent basis. Google partnered with NCAA last year to create real-time ads that predicted the outcome of the basketball match using historical data. The campaign helped Google to win ‘Creative Marketer of the Year’ at Cannes 2018.
When you go grocery shopping, you will bring along a list of things to purchase based on your needs and preference. A bachelor might buy beer and chips while a homemaker will buy ingredients for a family dinner. Association rules allow marketers to uncover how items are associated with each other.
Social Network Analysis
Most of us are part of multiple social circles; our actions influence people in a group based on our degree of closeness. Social Network Analysis (SNA) helps you to examine how people relate to each other. SNA has applications in content and viral marketing.
Machine learning algorithms like Regression, Random Forest and Decision Tree enable you to build predictive models using existing data set. This allows marketers to build predictive models to determine channel effectiveness and determine ROI from marketing campaigns.
Imagine as a marketer you can predict an outcome why wouldn’t you pay for it. Author Geoffrey Moore, says “Without big data, you are blind and deaf and in the middle of a freeway.” Data science opens up new possibilities for marketers. It’s about time we start learning data science.