How Predictive Analytics in Retail can Rebuild Customer Satisfaction

At a time when markets are coming crashing down on retailers, will technology adoption present a breakthrough in customer retention? According to McKinsey, advanced analytics powered by machine learning is essential for retailers to become more agile in the new normal. Artificial Intelligence (AI) is poised to improve retail functions from merchandising to customer engagement with data-driven predictive analytics techniques. We, at Oodles, as an Artificial Intelligence Development Company, explore how predictive analytics in retail will propel ROIs with in-depth insights.

Why Predictive Analytics in Retails is Essential for Business Viability

While consumers are increasingly shunning human contact, retail stores are still struggling to stay relevant in times of shifting consumerism. Forrester predicts that the global retail industry is likely to bear $2.1 trillion of losses arising due to COVID. The pandemic has brought long-term implications for retail functioning that includes challenges like-

a) Fluctuating consumer demands

b) Pressing precautionary measures during storage and supply

c) Inefficient delivery resources

d) Poor inventory management, among others.

Here’s how a Nielsen study visualizes the major COVID-led challenges that are likely to haunt retailers for the next four years. A step into technology can easily turn these challenges into opportunities for retailers.

How are emerging technologies making retailers future-ready?

Emerging technologies like the Internet of Things (IoT), AI, machine learning, and 5G connectivity can streamline legacy retail functions. They empower retailers with speed, intelligence, and deeper insights about consumer needs and preferences to understand and align their services accordingly. Solutions like contactless thermal scanning, demand forecasting, consumer behavior analysis, smart inventory, etc. can optimize retailer operations and services significantly.

Under AI, predictive analytics is a machine learning technique that analyzes large volumes of consumer data from disparate sources to generate insights. For instance, consumer demographic data can fuel AI-driven predictions about buying behavior, marketing opportunities, pricing strategies, and more.

The next section elaborates on some most promising applications of predictive analytics in retail to overcome COVID-led challenges.

Applications of Predictive Analytics in Retail to Combat COVID Challenges

1) Predictive Analytics for Retail Merchandising

Studies have found that a majority of retailers struggle to collect, analyze, and draw actionable insights from consumer data. Whether planning for seasonal demand changes, managing inventory budget, improving product positioning, or increasing operational efficiency, manual efforts don’t suffice.

AI augments retail excellence with data-driven insights about which product are consumers likely to buy the most, when, and how often?

Predictive analytics services in retail solve 3 critical challenges-

a) Efficient Assortment Planning

Machine learning algorithms surpass human intelligence in increasing the ROI on inventory investment by making precise recommendations of assortment volume, options, sizes, and more.

b) Smart Inventory Management

Predictive analytics provides timely insights on inventory exceptions, mobilization, anomalies, and other factors to make accurate safety stock decisions.

c) Price Optimization

Data-driven analytics factor in critical parameters like business scope, consumer behavior, feedback, and competitor rates to derive optimal product prices.

McKinsey Global visualizes different retail merchandising elements where automation and advanced analytics will play a significant role.

AI and machine learning are arguably the most essential technologies forming the foundation of future-ready retail operations.

Also read | How Machine Learning in Retail is Driving Sales Post COVID

2) Targeted Marketing Campaigns

Often, retailers struggle to capture customer interests due to poorly timed marketing campaigns. Machine learning algorithms sift through data silos of customer demographics, living conditions, needs, and preferences to launch targeted marketing efforts.

ML techniques like segmentation, customer churn prediction, and loyalty analytics enable marketers to formulate customer-oriented and omnichannel engagement strategies.

The Oodles AI team recently built a custom advert system for a leading media company based in the UK. The analytics-driven solution displays personalized TV commercials in place of existing adverts by tracking real-time advert data across customer TV sets.

Watching these targeted adverts enable customers to earn loyalty points that can be redeemed at a retail store. A self-rewarding marketing campaign like this enables retailers to engage customers effectively while increasing loyalty and retention rates.

Also read | 4 Futuristic IoT Solutions for eCommerce Businesses in the New Normal

3) Personalized Retail Chatbots

Considering the tectonic shift in consumer demands from offline to online channels, retailers must explore virtual customer engagement opportunities. AI-powered chatbots present a user-friendly and interactive window to augment retail marketing through personalized messages, offers, updates, and more.

Studies suggest that more than 50% of the millennial generation is more likely to buy goods from a chatbot than human executives.

Powered by Natural Language Processing and analytics, retail chatbots connect with a wider audience to recommend personalized products across social channels.

Also read | Why WhatsApp Chatbots for eCommerce are Must-have Marketing Tools

Oodles AI: Your Retail Automation Partner

Retail stores are a powerhouse of data that if utilized correctly, will add unprecedented value to all the layers of retail operations. We, at Oodles, use our experiential knowledge in data analytics to collect, analyze, and build next-gen solutions upon retail data silos. Our data analysts deploy case-specific AI algorithms, tools, and technologies to build resilient retail solutions including-

a) Price optimization

b) Demand forecasting

c) Customer segmentation

d) Personalized loyalty programs

e) Conversational commerce, and more.

Connect with our AI Development team to know more about our AI capabilities and solutions.