Black Friday sales are just around the corner. Both online and traditional retailers have geared up to race past each other in the ultimate shopping frenzy of the year. Although both brick and mortar retailers and online platforms will generate high sales, online retailers will sweep past the offline platforms. Why? In case of online retailers, the best part remains the fact that shopping online, customers don’t have to deal with pushy crowds, traffic, salespeople, and long queues. Online shoppers have access to a much larger array of products. They can also switch between stores, by just switching between tabs on their smart devices.
Considering the surge of shoppers expected on such peak seasons, Big Data Analytics is a helpful tool for online retailers. With the advances in Machine Learning, Big data analytics is no longer confined to the technology landscape, it also represents a way how retailers connect with consumers in a purposeful way. For retailers, both big and small, adopting the right ML powered Big Data Analytic strategy would help in increasing their sales, retent their customers and generate high revenues.
Here are 17 reasons why data is an important asset for retailers, especially on the 24th, this month.
A. Improving site infrastructure
The first thing that a customer sees when landing on an e-commerce website is the UI, ease of access, product classification, number of filters, etc. Hence building an easy to use website is paramount. Here’s how ML powered Big data analytics can help:
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1. E-commerce site analysis
A complete site analysis is one of the ways to increase sales and retain customers. By analyzing page views and actual purchases, bounce rates, and least popular products, the e-commerce website can be altered for better usability. For enhancing website features data mining techniques can also be used. This includes web mining, which is used to extract information from the web, and log files, which contain information about the user. For time-bound sales like Black Friday and Cyber Monday, this is quite helpful for better product placement, removing unnecessary products and showcasing products which cater to a particular user base.
2. Generating test data
Generation of test data helps in a deeper analysis which helps in increasing sales. Big data analytics can give a helping hand here by organizing products based upon the type, shopper gender and age group, brands, pricing, number of views of each product page, and the information provided for that product. During peak seasons such as Black Friday, ML powered data analytics can analyze most visited pages and shopper traffic flow for better product placements and personalized recommendations.[/toggle]
B. Enhancing Products and Categories
Every retailer in the world is looking for ways to reduce costs without sacrificing the quality of their products. Big data analytics in combination with machine learning is of great help here.
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3. Category development
Big Data analytics can help in building up of new product categories, or in eliminating or enhancing old ones. This is possible by using machine learning techniques to analyze patterns in the marketing data as well as other external factors such as product niches. ML powered assortment planning can help in selecting and planning products for a specified period of time, such as the Thanksgiving week, so as to maximize sales and profit. Data analytics can also help in defining Category roles in order to clearly define the purpose of each category in the total business lifecycle. This is done to ensure that efforts made around a particular category, actually contribute to category development. It also helps to identify key categories, which are the featured products that specifically meet an objective for e.g. Healthy food items, Cheap electronics, etc.
4. Range selection
An optimum and dynamic product range is essential to retain customers. Big data analytics can utilize sales data and shopper history to measure a product range for maximum profitability. This is especially important for Black Friday and Cyber Monday deals where products are sold at heavily discounted rates.
5. Inventory management
Data analytics can give an overview of best selling products, non-performing or slow moving products, seasonal products and so on. These data pointers can help retailers manage their inventory and reduce the associated costs. Machine learning powered Big data analytics are also helpful in making product localization strategies i.e. which product sells well in what areas. In order to localize for China, Amazon changed its China branding to Amazon.cn. To make it easy for Chinese to pay, Amazon China introduced portable POS so users can pay the delivery guy via credit card at their doorstep.
6. Waste reduction
Big Data analytics can analyze sales and reviews to identify products which don’t do well, and either eliminate the product or combine them with a companion well-doing product to increase its sales. Analysing data can also help in listing products that were returned due to damages and defects. Generating insights from this data using machine learning models can be helpful to retailers in many ways. Some examples are: they can modify their stocking methods, improve on their packaging and logistic support for those kinds of products.
7. Supply chain optimization
Big Data analytics also have a role to play in Supply chain optimization. This includes using sales and forecast data to plan and manage goods from retailers to warehouses to transport, onto the doorstep of customers. Top retailers like Amazon, are offering deals under the Black Friday space for the entire week. Expanding the sale window is a great supply chain optimization technique for a more manageable selling.[/toggle]
C. Upgrading the Customer experience
Customers are the most important assets for any retailer. Big Data analytics is here to help you retain, acquire, and attract your customers.
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8. Shopper segmentation
Machine learning techniques can link and analyze granular data such as behavioral, transactional and interaction data to identify and classify customers who behave in similar ways. This eliminates the guesswork associated and helps in creating rich and highly dynamic consumer profiles. According to a report by Research Methodology, Walmart uses a mono-segment type of positioning targeted to single customer segment. Walmart also pays attention to young consumers due to the strategic importance of achieving the loyalty of young consumers for long-term perspectives.
9. Promotional analytics
An important factor for better sales is analyzing how customers respond to promotions and discount. Analyzing data on an hour-to-hour basis on special days such as Black Friday or Cyber Monday, which have high customer traffic, can help retailers plan for better promotions and lead to brand penetration. The Boston consulting group uses data analytics to accurately gauge the performance of promotions and predict promotion performance in advance.
10. Product affinity models
By analyzing a shopper’s past transaction history, product affinity models can track customers with the highest propensity of buying a particular product. Retailers can then use this for attracting more customers or providing the existing ones with better personalizations. Product affinity models can also cluster products that are mostly bought together, which can be used to improve recommendation systems.
11. Customer churn prediction
The massive quantity of customer data being collected can be used for predicting customer churn rate. Customer churn prediction is helpful in retaining customers, attracting new ones, and also acquiring the right type of customers in the first place. Classification models such as Logistic regression can be used to predict customers most likely to churn. As part of the Azure Machine Learning offering, Microsoft has a Retail Customer Churn Prediction Template to help retail companies predict customer churns.[/toggle]
D. Formulating and aligning business strategies
Every retailer is in need of tools and strategies for a product or a service to reach and influence the consumers, generate profits, and contribute to the long-term success of the business. Below are some pointers depicting how ML powered Big Data Analytics can help retailers do just that.
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12. Building dynamic pricing models
Pricing models can be designed by looking at the customer’s purchasing habits and surfing history. This descriptive analytics can be fed into a predictive model to obtain an optimal pricing model such as price sensitivity scores, and price to demand elasticity. For example, Amazon uses a dynamic price optimization technique by offering its biggest discounts on its most popular products, while making profits on less popular ones. IBM’s Predictive Customer Intelligence can dynamically adjust the price of a product based on customer’s purchase decision.
13. Time series analysis
Time series analysis can be used to identify patterns and trends in customer purchases, or a product’s lifecycle by observing information in a sequential fashion. It can also be used to predict future values based on the sequence so generated. For online retailers this means using historical sales data to forecast future sales, analyzing time-dependent patterns to list new arrivals, mark up prices or lower them down depending events such as Black Friday or Cyber Monday sales etc.
14. Demand forecasting
Machine learning powered Big Data analytics can learn demand levels from a wide array of factors such as product nature, characteristics, seasonality, relationships with other associated products, relationship with other market factors, etc. It can then forecast the type of demand associated with a particular product using a simulation model. Such predictive analytics are highly accurate and also reduce costs especially for events like Black Friday, where there is a high surge of shoppers.
15. Strategy Adjustment
Predictive Big Data analytics can help shorten the go-to-market time for product launches, allowing marketers to adjust their strategy midcourse if needed. For Black Friday or Cyber Monday deals, an online retailer can predict the demand for a particular product and can amend strategies in between, such as increasing the discount, or placing a product at the discounted rate for a longer time, etc.
16. Reporting and sales analysis
Big data analytics tools can analyze large quantities of retail data quickly. Also, most such tools have a simple UI Dashboard which helps retailers know detailed descriptions of their queries in a single click. Thus a lot of time is saved, which was previously used for creating reports or sales summary. Reports generated from a data analytics tool are quick, fast, and easy to understand.
17. Marketing mix spend optimization
Forecasting sales and proving ROI of marketing activities are two pain points faced by most retailers. Marketing Mix Modelling is a big data statistical analysis, which uses historical data to show the impact of marketing activities on sales and then forecasts the impact of future marketing tactics. Insights derived from such tools can be used to enhance marketing strategies and optimize the costs.[/toggle]
Adopting the strategies as mentioned above, retailers can maximize their gains this holiday season starting with Black Friday which begins as the clock chimes 12 today. Machine Powered Big Data analytics is there to help retailers attract new shoppers, retain them, enhance product line, define new categories, and formulate and align business strategies. Gear up for a Big Data Black Friday this 2017!
