Benefits of using real time AI and anomaly detection in retail

While applications of AI cover a full range of functional areas, it is in fact in these two cross-cutting ones – supply-chain management/manufacturing and marketing and sales – where we believe AI can have the biggest impact. (Harvard Business Review, JULY 20, 2018) In retail organizations, for example, marketing and sales has often provided significant value. HBR research shows that using AI on customer data to personalize promotions can lead to a 1-2% increase in incremental sales for brick-and-mortar retailers alone.

Source: Insights from hundreds of use cases, McKinsey Global Institute, April 2018

McKinsey estimates that AI can improve performance beyond that provided by other analytics techniques in 87% of the use cases. 40% of potential value created by analytics would use deep learning techniques. Real time AI and anomaly detection techniques will help the retailers achieve the potential incremental value over other analytics techniques.

Let us take the MIT Media Lab’s experiment to see whether it could estimate retail sales performance on “Black Friday,” the day following the US Thanksgiving holiday. Instead of waiting for data from the stores themselves, they used location data from mobile phones to infer how many people were in the parking lots of major retailers. Combining this with data on average spend per shopper enabled them to estimate a retailer’s sales, even before the company had recorded it themselves. (Harvard Business Review, JULY 31, 2017) Decision making that depended on experts are now supported by data analytics. Business in the 21st century is being redefined by a data-driven revolution. Using the power of deep learning in real time for decision making enables retailers to man-up and beat the competition.

For example, in supply chain, stock pile ups happen and at times they end up as waste and had to be written off the books. This is because the products were not there in the right place at the right time. When an artificial intelligence engine identifies the gap and alerts all the relevant stakeholders in marketing, supply chain, distribution and category directors, action could be taken from a central location and it will not only decrease waste but also result in revenue growth.

In sales and marketing identifying a stellar promotion that beats the competition is one the important factors of commercial success. Using real time AI for detecting the anomaly and using it to gauge the interest for the product with the early adopters in a market segment and amplifying it with the use of marketing efforts focused on that segment would lead to increased revenues and market share.

Managing retail operations from a central location lowers costs through economies of scale and improves productivity through reduction of duplicated efforts. One of the key issues in centralising operations is the specific risk of relying on the strength of key personnel. When a trained resource leaves and is replaced by a new resource, for a moment agitation takes place. Like an eddy in the ocean it takes a while for the new person to settle down. This systematic risk requires the retailers to put various processes in place and have the right organisational structure. Using artificial intelligence that learns on its own, analysing all the data generated by the system and alerting when an anomaly takes place, empowers the managers with the organisational knowledge. You can use the power of real time AI to prioritise the focus areas for that day and add more value to the company by managing more tasks in less time.

AI assisted middle managers will be able to increase the productivity and decrease the management costs. The managers can use their time and energy into more important issues that are highlighted by exceptions. Since they focus more on exceptions, the frequency of decision making comes down and it saves invaluable time and energy.

Hence, real time AI and anomaly detecting solutions provided by deep learning AI has the potential to predict new trends, identify latent waste in the form of stock, help spot pricing errors at a granular level and stop revenue leakage. We at CrunchMetrics exclusively focus on using AI for detecting anomalies in KPIs and metrices and assist organisations to grow faster.

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  1. Most of AI’s Business Uses Will Be in Two Areas by Michael Chui, Nicolaus Henke, Mehdi Miremadi – Harvard Business Review, JULY 20, 2018
  2. Notes from the AI frontier: Insights from hundreds of use cases, McKinsey Global Institute, April 2018

Data Can Do for Change Management What It Did for Marketing by Michael L. TushmanAnna KahnMary Elizabeth PorrayAndy Binns – Harvard Business Review, JULY 31, 2017

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