The intersection of people and numbers
Analytics and the quality of your questions
Statistics and the quality of your answers
Synthesis and the quality of your decisions
Effective data science in the retail industry
Look no further than the latest dystopian novel and you will see circumstances where people are stripped of their identity and reduced to numbers. This same scenario plays out in historical and contemporary conflicts where devaluing another’s humanness is considered necessary for subduing them. In these situations, data can be seen as both an asset and an enemy. It can provide insight into everything from human psychology to machine productivity, but it can also take huge swaths of people and homogenize or bundle them into statistical groupings, and if used poorly, it can serve as justification for rendering the 1% negligible.
Science is the art of understanding the world through observing and experimenting, with a particular emphasis on understanding. Consequently, data science is not something that happens separately from people, data science is something used to understand people – a space where people and information intersect to engender enlightenment.
When we think of retail, data science is not always the first thought that comes to mind. But retail is an industry that is particularly adept at seeking to understand its consumers and utilizing data to inform decisions on how to best meet those consumers’ needs. Given this context, the science of understanding data and the science of understanding consumers are symbiotic. This mergence prevents data science problems in retail – problems that value numbers above people – and supports the consumer in ways that are essential to the retail ecosystem.
Data science in retail is best equipped to generate this symbiosis of data and people when it utilizes three important elements: analytics, statistics, and decision science.
The terms analytics and statistics are often used interchangeably, and other times they are debated with voracity. As Cassie Kozyrov, Chief Decision Scientist at Google notes, “Statistics and analytics are two branches of data science that share many of their early heroes, so the occasional beer is still dedicated to lively debate about where to draw the boundary between them.” Nonetheless they each represent a different branch of data science that is relevant to accruing robust business insights. Kozyrov goes on to explain, “Analytics helps you form the hypotheses. It improves the quality of your questions. Statistics helps you test hypotheses. It improves the quality of your answers.”
Having a strong knowledge base to draw from impacts the caliber of the questions, but identifying gaps in that knowledge base can also highlight which questions still need answers. How well do you know your business, product, consumer, industry, trends, competitors? What is missing from your understanding? Can you identify what are your business opportunities and challenges. What are your options? Then use that information to funnel your questions into a cohesive, succinct hypothesis.
Creating such a hypothesis can be a science in and of itself. While all questions have some degree of validity, not every question is useful. To formulate a strong hypothesis, it helps to understand the different kinds of questions:
Once the question has been maximized, the next step is ensuring answers are robust, reliable, and accurate. For data science in retail, consider the following factors:
When it comes to data science in the retail industry, therefore, the question should not be either-or. The answer to supporting consumer needs is not based on analytics or statistics. It is based on analytics and statistics. Kozyrkov concludes, “Choosing between good questions and good answers is painful . . . so if you can afford to work with both types of data professional, then hopefully it’s a no-brainer.” The same applies to retail data science software – if you can find a platform that integrates both quality questions and quality answers, then hopefully it’s a no-brainer.
Once questions have been asked and answered, the next step in the data science process is making quality decisions. Data science and decision science are also often segregated into two camps. But these disciplines are interconnected. Data science problems in retail often stem from placing data into a silo. Rather than considering the full scope of information, decision-makers isolate the data and fail to integrate it into applicable contexts.
For example, sometimes sales increases in one category or basket will lead to deficiencies in other categories or baskets. If the decision to rollout the initiative only focuses on that singular category, that initiative may lead to overall sales losses. Another example would be if the data shows that one geographic area is receptive to a change, but other geographic areas are not receptive, yet rollout is conducted fleetwide, that decision could fail.
The best decisions take all the information into account. They consider the people involved, the resources needed and available, the initial knowledge base, the hypothesis, the results, the domino effect of those results, the ROI, and the best forum for rollout success. Just as integrating analytics and statistics is a no-brainer, having a dedicated client-success team as part of your decision making process is a no-brainer when it comes to to data processing and synthesis.
Perhaps the most important piece to the process, however, is recognizing that scientific discovery is not linear; it’s cyclical. The best hypotheses, answers, and decisions generate more questions, leading to further evolution of knowledge and understanding.
As covid quarantines wore down, a convenience store rolled out a new fuel pay app. While they had put effort and money into the design, the lift in sales was not what they had initially hoped, and they began to second-guess themselves, and it was difficult to differentiate if the lift they were seeing was the result of fewer quarantines or the result of app usage. They started with a clear, cohesive question: What is impacting sales outcomes? The app, lifted quarantines, or both? Then they utilized effective testing tools to sift through back-data and identify a representative sample and control group based on pre-pandemic user behavior. With this methodology in place, they were able to differentiate post-quarantine behavior from app usage. They learned that the app was being utilized, and they made the decision to continue rollout – a decision that paid off for both the company and the consumer. The new app met a consumer need and that resulted in higher revenue for the company.
On a sheet of paper, consumers can be seen as just numbers. But data science in the retail industry transforms those numbers back into people – people with quantifiable needs. It asks the right questions to understand consumer behavior. It utilizes effective methodologies and software solutions to achieve high statistical confidence in results. Then it looks at the complete picture to make decisions that provide consumers with productive solutions, and enrich retail success.
If you are interested in learning more about effective data science testing solutions for brick-and-mortar retailers, check out these links:
The surprising power of a testable hypothesis
How to create a statistically valid test
Minimize bias and maximize your testing results
Unlock the value of your data with A/B testing