Remember the American retailing giant that famously figured out a teenage girl was pregnant before her own father did? This was Target corporation, the #2 discount retailer in the USA, engaged in a fierce battle for market share with #1 Walmart. Target was looking to data science for competitive advantage and had started combining data science and business intuition in an ingenious way.
It started with intuition
Target didn’t start with a cool technology (e.g. Apache Spark) or a trending data science technique (e.g. deep learning). Target started with a simple intuitive insight. In this case, it was the insight that habits dictate consumer behavior. They realized the key to winning customers from competitors was to catch them at key habit-forming moments. Starting with this intuition, Target determined to identify customers approaching life-changing, habit-forming moments. They could then focus marketing efforts to secure these people as loyal customers for years to come.
The most important habit-forming moment was the birth of a child. But how could Target identify expecting mothers and market to them at precisely the right times during the pregnancy? This is the point where they turned to data science.
Enter the data scientists
Target’s data scientists were given a clear business goal: use data and analytics to tell us which shoppers are pregnant. Do this, and we’ll work hard to secure them as loyal shoppers of maternity and children’s products who will continue to spend money with us over the coming years.
Note that the data scientists started with a clear purpose and well-defined goals; goals that were rooted in business intuition. It was this business intuition that started the team of analysts working on analytic models that would eventually identify pregnant shoppers with a surprising degree of accuracy, even estimating due dates to within a small window.
Target made a huge profit from this data science project. The executives had an intuition for strategic growth, and the analysts made it real by collecting data and building accurate models. The results, although questionable in terms of privacy, resulted in significant financial gains that extended over several years. 1
A Cycle of Continuous Improvement
Business intuition and data science should together walk through a series of insight-analysis-value cycles.
At a project level, make sure your data scientists are working closely with your business experts. There are certainly people in the company who have extensive experience with the customer, the product, and the market. They should share their intuition with the data scientists. Keep these sets of people talking with each other. They should speak together every few days to discuss data and initial results. The data scientists will quickly learn if they’re doing something blatantly wrong. Oh, and everyone should be using the company products, subscribed to customer mailing lists, etc.
At an executive level, make sure your key discussions include both business and data experts. To illustrate, some time back I was consulting with a company that had been thrown into crisis by a sudden drop in revenue. Sitting in a war room with the company’s senior leaders, one after the other attributed the drop to various intuitive, albeit speculative causes. We only identified the true cause of the drop after several days of digging into data. Once analytics put the company on the right scent, an executive familiar with business trends realized the same situation would re-occur within 6-9 months. We then refocused our analysis on that future period to forecast damage and decide on mitigating measures.
It was another case of business intuition and data insights sitting at the same table and together producing maximum value for the company. Analytics guides insight and insight directs analytics. In terms of Gartner’s Analytics Maturity Model, we can say descriptive and diagnostic analytics serve to validate and inspire business intuition, while predictive and prescriptive analytics provide the means to make business intuition operational.
To quote Simon Uwins during the glory days of Tesco’s club card:
“We don’t forget our intuition, but better data lead to better thinking, and our data give us the confidence to ask the right questions”2
Let’s keep asking the right questions, looking to both data as well as to intuition for the answers.
- Humby, Clive, Terry Hunt, and Tim Phillips.Scoring Points: How Tesco Continues to Win Customer Loyalty Ed. 2. N.p.: Kogan Page, 2008. Print.
David Stephenson PhD is an internationally recognised expert in the data science and big data analytics. He is the author of new book Big Data Demystified: How to use big data, data science and AI to make better business decisions and gain competitive advantage.