Sectors that gained revenue during pandemic and forecasted to be stable post pandemic

Series 2 – Retail & Consumer Packaged Goods (CPG)

– Advanced Analytics Practice

Observations:

1. CPG firms have witnessed a significant rise in demand due to increased consumption, especially through e-commerce.
2. Brick and mortar grocers are also witnessing a growth in demand. However, certain categories like apparel, luxury items etc. have observed set back in demand.

Challenges:

1. Pileup of inventory in the non-essential product categories. Categories such as apparel, home goods, furniture etc. have gotten a cold shoulder from customers leading to massive inventory pile-ups.
2. Fundamental shift in the buying patterns of consumers with a distinct trend towards online shopping. Retailers facing challenges in offer management, personalized experience and digital engagement.
3. Anticipation of a shopper’s requirements to customize offers and recommendations that meet the personalized needs of the shopper.
4. With increasing demand on digital channels, companies will not have the opportunity to resolve customer concerns in-person.

Artificial Intelligence Powered Solutions:

1. Markdown Analytics – Markdowns will be an effective strategy for retailers to move out non-essential product categories. Unfortunately, while markdowns are inevitable, they can also be costly. Markdown optimization ensures retailers can recoup lost sales at the best possible margin and clear inventory to make room for new items as customers will get back to brick-and-mortar shopping. Sophisticated machine learning models for price elasticity determination and markdown optimization algorithms can be used to achieve the optimal strategies.
2. AI-powerd Shopping Chatbot – As people are staying at home during the current COVID-19 pandemic, they are turning to online sellers. The influx in requests rises each day, which is why, in an effort to streamline customer service and meet the increased demand, retailers should think about turning to chatbots. These virtual AI-powered assistants are able to engage customers 24/7; helps customers to browse for products, load the products into a cart and finally pay for the purchase; offer instant responses and improve customer satisfaction. Deep learning models are used to create an intelligent natural language interface for the customers to have a seamless experience.
3. Customer Sentiment & Complaint Management – Monitoring online customer sentiments and ensuring quick redressal of complaints will be imperative. It can be a huge challenge to discover what is being said about your site, your brand or the products you sell. A combination of Sentiment Analysis and Natural Language Processing can be used to mine such data and present actionable insights. Sentiment analysis is the process of determining the emotional tone behind a series of words to gain an understanding of the attitudes, opinions and emotions expressed. A complaint management engine monitors social media and online platforms for customer reviews, automatically triage the feedback to the correct category and tracks responses.
4. Personalized Shopping – In these uncertain times, for retailers, personalization is the key to connecting with shoppers and thriving in a post-COVID world. Online shopping offers a seemingly infinite array of choices and leaves shoppers overwhelmed and more hesitant to make buying decisions. AI-driven machine learning analyses shoppers’ behavior online, gauging customer interests, learning and tailoring offers for each individual customer, thus helping retailers to offer a more engaging experience to consumers. Machine learning algorithms take stock of every customer action to immediately start personalizing the experience. Each action builds a profile around the shopper and align products and categories to their preferences.

You can read the next series here: Healthcare Providers.