While the retail sector has had its share of ups and down since the pandemic, there are still chances it can evolve through transformation and analytics adoption. Most retailers are at the early stages of transformation. Let us try and understand the problem statements, and why it is needed in the first place before we get into the benefits of using predictive analytics in retail. Some of these reasons are:
- Today every business wants to transform and provide unique experiences to their customers which can distinguish them from competitors.
- Marketing Campaign optimization
- Improve Inventory and Operations
- Fraud Risk Management
- Determine a price the customer is willing to pay for the product, also called Price optimization.
- Derive quicker and more accurate decisions for Go-to-Market
- Enhance the business Intelligence as data volumes are getting huge and thus scale this up to identify future outcomes based on historical data through statistical and machine learning algorithms.
If descriptive analytics reveals the “what” of what is happening in your company and diagnostic analytics reveals the “why” predictive analytics tells you about “what’s next”. There are numerous sorts of retail sales and stores, including grocery, convenience, discount, independent, department, electrical, and specialized stores, that can be found all over the world.
By using bigdata, retail analytics aims to identify the factors affecting business outcomes. Predictive analytics effectively uses the information by combining descriptive and diagnostic analytics.
Predictive analytics solutions detects clusters and exceptions automatically and predicts future trends using complex algorithms and statistical methods.
Retail predictive analytics employs a combination of AI, advanced analytics, business intelligence, and automation to accurately forecast the future likelihood and account for the most important correlations. Additionally, it can assist you in learning consumer behavior so that you can see customers as they move through a business and ascertain where they intend to make purchases.
Now that we have a fair understanding of why analytics is so important in retail sector, let’s see a few use cases where predictive analytics could be used to address these business problems.
Overview of Topics Covered
Usage of Predictive Analytics
Price Optimization and Management
This will help to determine how the customer would view to various pricing levels that can maximize sales and profitability. Price optimizations are implemented to forecast several factors like change in volume based on price change, or optimization across product lines, brands, or channels, it can also vary based on seasonality or markets (B2B, B2C etc.). Few basic steps that any retail must have to implement:
1. Gather data
Data is the fuel to decision-making in today’s world, without data we would be going directionless and purely based on assumptions. It is necessary to have diverse information, the richer the data set, the better results in training the model. For instance, employee details, customer details, transaction history, customer ageing, location, customer feedback, customer segment etc.
2. Create a goal or strategy
Create scenarios and use cases that you would want to achieve. Define the problem statement and devise a strategy how data could solve this issue and there could also be possibility of limitations as well.
3. Modeling and training
Once the basic data set is sampled, the next crucial step is to train this and create the machine learning model to get a desired accuracy. Run through numerous scenarios, hypothesis testing and tweak the prediction algorithms accordingly.
Companies that are successfully able to transition from “what they are able” to a forecasting model of “what is likely to happen” will have dynamic pricing strategy along with plan of execution.
For instance, Walmart employs Data Café at its corporate headquarters to analyze more than 40 petabytes of transactional data and uncover customer trends. The grocery team at retailers uses this information to better understand how price and promotional offers affected sales of a specific product category. For improved analytics, it also has an automated warning system for notifications about sales declines.
Inventory Management
As the name implies, this involves managing the important things for the future. Retailers strive to satisfy client needs at any time, in the appropriate location, in good shape, etc. The key to generating business insights that can assist you in making data-driven decisions for enhanced efficiency and profitability is currently held by today’s inventory control systems.
Even for large shops with enormous datasets, an inventory system may give you unmatched insights into consumer behavior, product performance, and channel performance. The analyst recognizes patterns and trends and controls the stock using the information obtained.
For instance, Amazon manages its inventory using analytics tools like a recommendation engine that suggests related products based on consumer demand. Additionally, it informs customers of the availability of products once they are added to the stock. To improve CX, it takes advantage of existing customer history.
Fraud Risk Management in Retail
While we are aware that obtaining customers’ trust is the most crucial element, but we also see high chances of customer frauds happening on online platforms. Then, as a result of these actions, the reputation of these companies gets damaged due to loss of customer trust and suffer significant losses.
Following some financial setbacks, businesses are increasingly turning to machine learning and neural network concepts in new digital technology to predict and monitor customer buying patterns, payment methods etc. These predictive models will help in identifying potential frauds, reduce failure while paying with a credit card, increase conversion and sales, secure online retail.
There are several outlier techniques to isolate potential frauds and prevent fraudsters from taking advantage of the system. Organizations can deploy supervised ML algorithms like logistic regression or time series for transaction monitoring and forecasting, unsupervised ML and statistical techniques as well.
Business Intelligence
These days BI tools efficiently manage and organize retailer data, as well as aid in data structuring and visualization. To conduct basic BI, most retailers rely on native ERP (Enterprise Resource Planning) system features.
More sophisticated retailers will use BI software such as Tableau, Power BI, SAP, Spark, and QlikView. These applications provide access to multiple data sources, appealing data visualizations, and some data manipulation.
Most sophisticated BI involves data scientists who use programming languages (Python and R) to increase flexibility in data manipulation, visualization, and modelling.
Personalize in-store experience
The proliferation of e-commerce has reduced sales in retail stores. Leading stores are shut down, and brands are losing customers to online shopping convenience. People-tracking technology, analysis of in-store customer behavior, and merchandising assessment will enhance the buyer’s shopping experience. The retailers will personalize offerings and incentivize loyal customers to boost product purchases across stores.
For instance, clients at Amazon Go-stores scan the code using the Amazon Go app, and the money is immediately deducted from their accounts. The information is put to use to increase customer satisfaction and strategically arrange favorite products throughout different aisles of the store.
Social Media has become a powerful tool these days to assess customer behavior, spot purchasing cycles, and find patterns. Organizations can actively engage with customers through campaigns, polls, gather feedback/reviews, sentiments etc. These engagements produce rich amount of data which can fuel the decision-making of retailers for creating their next marketing strategy using data science techniques.
One such case is using Natural Language Processing, which uses this sentiment analysis model to extract feelings from the text. NLP can tell whether a client is reviewing a product favorably or unfavorably.
For instance, Nordstrom, a high-end shop in the US, uses Facebook, Pinterest, Twitter, and Instagram in conjunction with NLP to identify the most talked-about products and advertise them in its physical locations.
So, why do you think you should upscale your Retail analytics?
To get the right product, in the right location, at the right time, and right quantity, any retailer ranging from medium to large enterprise businesses need certain level of analytics in order to succeed in the long-term. The approach should be based on your problem statements, not fall into the common path of analysis. One can start with your own proof of concept with the data they gather and test it on a small segment of their business. As we converge into the digital era of Retail business, advanced analytics is the “need” and no more a “want” if you really want to grow businesses.
Author’s
Saurabh Rai is an industry-recognized thought leader, an experienced advisor on GIS, Geospatial Technology, Process Initiatives, Data Science, Data Architecture, Data Engineering, Artificial Intelligence, Machine Learning, Technology, Analytics, User Experience, Data-driven businesses, Customer Success, and Consumer Insights.
