Most retail brands know their data is a valuable resource for decision making in business operations. As tech continues to evolve, major brands are leveraging predictive analytics to fully exploit the benefits of their data.
However, with new tech comes the need for education. Common misunderstandings of these tools include the difference between predictive analytics and machine learning and the human element that’s needed to optimize your results.
Is predictive analytics the same as machine learning?
In short, no. Although they work synergistically, predictive analytics and machine learning are not exactly the same. Let me explain.
Machine learning is a subset of artificial intelligence in which processes are automated by using data and pattern recognition to enable systems to perform tasks without being specifically programmed to do so. Algorithms are used to build models that make predictions without the need for human supervision. These algorithms are used to automate everyday tasks, such as email filtering and network security, and use iterative processes to continue to learn and adapt when provided with new data.
It’s a familiar concept, but it has evolved to become more useful than we could have imagined. Shopping suggestions, targeted ads, streaming entertainment feeds? They’ve all been developed through machine learning, using patterns learned from human input to accurately recommend products, services and entertainment.
Machine learning resides beneath the predictive analytics umbrella, which, along with other methods like data mining, uses established patterns to identify likely future outcomes. These patterns can make predictions regarding consumer behaviors, market fluctuations and more. Predictive analysis is used in industries across the board, from marketing to insurance, telecommunications to credit score calculation, even health care.
Predictive analytics, as more of a concept than an operation, predicts outcomes based on historical statistical data, while machine learning is a process — an evolution in pattern recognition. Predictive analysis software solutions use built-in algorithms to make accurate decisions backed by the data they process.
There are two common predictive models. Designed to imitate the human brain’s neurological processes, neural networks solve complicated patterns in large data sets and are able to decipher nonlinear relationships, even if some variables are unknown. Decision trees analyze data by grouping it into branch-like subsets based on input variables, tracing the path of “thought” leading to a decision.
What role does predictive analysis play in retail?
Data utilization is a multistep process, beginning with “looking” at the data to find out what happened. Once organized, the data can be evaluated and trends identified, exploring why it happened. Machine learning in analytics looks at those trends and relationships, eventually predicting what will happen.
Accelerated Analytics, the company I work for, works with retailers to determine their company’s data analysis and reporting needs. Through machine learning, predictive analysis of point-of-service (POS) data can tell a lot about a business, including:
Units On Hand: By isolating each item at the store level and filtering for the preferred minimum or maximum inventory level, average units sold can be determined during a specified time frame. That information can be used to calculate inventory weeks (or days) supply is on hand — a predictive indicator that will identify trends and alert when action is needed.
Units Sold: Identifying top-selling items at a store-by-store level over time can uncover trends in consumer behavior and preferences that are helpful in knowing what items to carry and at what price points. These predictions can help you to make smart decisions in ordering, stocking and other factors affecting the company’s bottom line.
Period And Regional Comparisons: Comparing sales and inventory for similar periods of time and by geographic region can help to determine adjustments to inventory levels.
Optimizing your data points is critical.
Most POS data arrives with basic measures for units on hand and units sold. By creating the right ensemble of algorithms, analytic solutions can do the translation, database storage and number crunching, meaning the focus can remain on business growth and ROI.
Many brands get into a pattern of ordering based on last year’s sales and inventory levels for the same time period. But what if something changed in that store this year versus last year? Heavy residential growth in the area could increase demand, for example.
It is crucial that machine learning techniques look at a four- to eight-week sales velocity run rate leading up to the time period in question for both last year and this year. If this year’s sales velocity is stronger, that pattern leads to a prediction that a higher on-hand inventory level is needed, and the current year levels are increased. Tracking sales and inventory trends from the POS data over time and analyzing the period-over-period results is critical to being able to predict consumer needs and stock accordingly.
There are other factors to consider, too.
Weather, consumer demographics and store attributes are some examples of additional data to garner deeper trend analysis and predictions. Were sales last year affected by a major weather event, such as a hurricane? Is a store in a growing urban market? Does a store have a marked difference in square footage and layout?
Providing these types of data elements can help the machine learning engine predict pattern changes in sales, allowing for more budget-friendly inventory management, avoiding excess or insufficient stock levels, boosting efficiency and allowing for increases in gross margins and ROI.
That said, while less prone to mistakes, machines still blunder from time to time. Patterns can easily be skewed with a sudden large-scale, unpredictable change; the only way to respond faster is to keep the human element present in the process.
Artificial intelligence is becoming more prevalent and beneficial in retail and other businesses, and I believe it will continue to progress exponentially in its ability to operate without human input. However, its oversight and optimization are necessary to ensure the data you collect is the data you need to make effective retail decisions.
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