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Top 12 Predictive Analytics Use-cases that can revolutionize the e-commerce Industry

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As per Harvard Business Review: “Business leaders are scrambling to adjust to a world few imagined possible just a year ago. The myth of a borderless world has come crashing down. Traditional pillars of open markets — the United States and the UK — are wobbling, and China, India are positioning itself as globalization’s staunchest defender.”

Statistics by Harvard Business review

With more people growing each year, the challenges also get tougher year-by-year. We thought of getting an in-depth understanding of

What are some common challenges e-commerce businesses face?

  1. Understanding who is “Visitor” and “Potential Buyer”
  2. Nurturing the existing prospects
  3. Personalization
  4. Calculating the Life Time Value
  5. Understanding the buyers’ behaviour
  6. Cart Abandonment
  7. Customer Churn

So, how can e-commerce businesses tackle the above challenges?

One definitive solution can be – Predictive Analytics.

Predictive Analytics encloses a variety of techniques from data mining, predictive modelling and machine learning to analyze current and historical data and make predictions about future events.

With Predictive analytics e-commerce businesses can

  • Improve Lead scoring
  • Increase Customer retention rates
  • Provide personalized campaigns for each customer
  • Accurately predict and increase CLV
  • Utilize Behavioural Analytics to analyze buyers’ behaviour
  • Reduce cart abandonment rates
  • Use Pattern recognition to take actions that prevent Customer Churn

Below are some use-cases of Predictive Analytics, that can boost the growth of e-commerce businesses ~5X when inculcated:

1.     Real-time Product Pricing Management

Business Challenges:

  • Understanding the real-time, personalized pricing strategy
  • Competitive monitoring
  • Identifying profitable discounts and offer margins

  Solution:

Predictive Analytics offers insights on pricing, to maximize profits.

Product price comparison

Here in the above example, Competitor B offers a lesser price to customers compared to Competitor A, based on a real-time pricing strategy, that includes metrics such as CLV, Frequency of the visit, Lead scoring, and competitor prices.

Real-time Predictive Analytics can combine demographical data like gender,geo location etc, and real-time pricing metrics to predict personalized prices for customers, and enhance the sales%.  

Supporting Stats:

  • 57% of the customers who used the discount coupon said that they would not have purchased if they had not received a coupon code.
  • 59% of consumers say they consider shipping costs while making online purchase decisions.

Source: Jongjin Lee- Slideshare

2.     Increasing Life Time Value of a Customer

Predicting the CLV (Customer Lifetime Value) of your customer is a great insight to help retain high valued customers.

Business Challenges:

  • CLV Accuracy
  • Increasing CLV

Solution:

  • Calculates and predicts accurate CLV.
  • Predictive Analytics model includes a 3-step process to increase CLV.

Steps to increase Customer CLV:

Steps to increase CLV

Supporting Stats:

  • A 5% increase in the retention of customers in your business produces a 25% increase in your profit.
  • Customer LTV is an important cognition for around 70% of the companies.

Source: marketingmetrics.com

3.      Continuous, Real-time and Predictive Maintenance

Business Challenges:

  • Inventory Management
  • Updating Product Description
  • SEO
  • Bugs in the software/website

Solution:

  • Real-time inventory management, and predict products that customers might order.
  • Real-time SEO suggestions as needed.
  • Regular security checks performed in continuous cycles, to predict upcoming maintenance tasks.
  • Importing new products, categories, brands and suppliers, Updating related product descriptions.
  • Predict upcoming maintenance cost and streamline costs due to maintenance.

Supporting Stats:

15% of the retailers shipped the wrong product to the customers

Source: Clevertap

4.     Behaviour Analysis on Various Platforms

Business Challenges:

  • Understanding the purchase behaviour of the customer
  • Customer Segmentation
  • Personalized campaigning
  • Tracking User’s behaviour in real-time

Solution:

  • Monitoring the behaviour on various social platforms and predict right product recommendations for each user.
  • Real-time E-Mail Analytics.
  • Analyzing customer purchase behaviour and predict recommendations for right discounts at the right time.
  • Behavioural customer segmentation for intelligent automated campaigning.

Supporting Stats:

  • Men are more likely to abandon their carts.
  • 40% of customers do not need to receive emails and newsletters.
  • 16% of the customers are “window shoppers”.

Source: Jongjin Lee- Slideshare

5.     Customer Journey Analytics

Business Challenges:

  • Tracking the customer journey
  • Relating all touchpoints related to the customer journey
  • Retaining Customer-focused mentality

Solution:

Supporting Stats:

73% of consumers use more than one channel during their shopping journey.

Source: Harvard Business Review

6.     Quality Inspection

Business Challenges:

  • Regular quality checks and upgrades
  • Overall customer feedbacks segmentation
  • Quality assurance checklist

Solution:

  • Predict potential quality issues and mitigate before acquiring lower ratings to the products.
  • Promote stable buying behaviour.
  • Routine quality checking tests by user interaction.
  • Predict recommendations based on the quality, for improvements.

Supporting Stats:

Reason for Return: 25% cited ‘Product was not what I was expecting’, It’s important to have accurate quality products and its images on the online store.

  Source: Jongjin Lee- Slideshare

Quality inspection benefits

7.     Real-Time Sentiment Analysis

Business Challenge:

With the growing trend in the online presence of the consumers, its indeed a more difficult task to maintain a great reputation at all times and all places.

Solution:

  • Predictive Analytics can combine CRM, and implement schematic learning to be pro-active on customer followups for a negative or a delayed experience.
  • Predictive Analytics can combine Web search and some crawling tools and techniques to monitor customer feedback and suggestions at various platforms and give an overall rating score or performance from the customers.
  • Pro-active in providing few recommendations for change related to customer service, or to act way before to avoid any negative reputation.

Supporting Stats:

85% of people read online reviews before making a purchase.

        Source: Jongjin Lee- Slideshare

8.     Delivery and Supply Chain Management

Setting goals and target for your e-commerce business with Predictive Analytics at different stages of the supply chain becomes simple.

Business Challenges:

  • Demand and Stock Analysis (How is the forecast matching with the actual sales?)
  • Detailed Inventory Management (What stock should be held?, What will be the optimized budget?)
  • Delivery Planning (What, where and when should I ship?)
  • Procurement Analytics (How to achieve lowest landed cost?, How to retain high-quality suppliers and customers?)

Solution:

  • Points out any deviation – to fine-tune sales.
  • Inventory segmentation based on customer type. Segmenting customers allows to see how profitable are different types of customers to the business, predicts stock counts based on an optimized budget.
  • Optimization of shipment schedules and routes. Tracks shipment vehicles, and optimizes faster routes to impress customers with same-day deliveries.
  • Predictive models based on cost and stability.

Supporting Stats:

  • 24% of customers expect same-day deliveries important.
  • 44% of the abandoned carts are due to high shipping costs.                                          Source: Jongjin Lee- Slideshare

IQLECT’s ShopIQ has an inventory prediction model, that helps e-commerce business to predict what stock should be held, to prevent understocking or overstocking of any product. Click on the video below:

9.   Efficient Communication

Business Challenges:

  • When is the best time to mail your customers regarding offers and promotions?
  • What is the peak sales time?
  • Which is the best channel for each customer to promote the product and increase sales?

Solution:

  • Predicts the best time to interact with the customers for email campaigns.
  • Predicts the peak sales time to help us carry out efficient campaigns and marketing strategies to promote product sales.
  • Enhances sales by predicting personalized success sales channels.

Supporting Stats:

70% of the customer’s journey is based on how the customer feels they are being treated.

Source: (McKinsey)

10.     Customer Churn Prevention

Business Challenges:

  • When the business is about to lose some customers, it needs to bring in a few new customers to compensate for the loss.
  • The Cost of Acquiring a new customer = 5X (The Cost of Retaining a customer)

Solution:

  • Predict that few customers are at the risk of leaving the e-commerce site before losing them.
  • Predictive Analytics lets the business understand the signs of dissatisfaction among customers and react before the signs of a goodbye.
  • Employs pattern recognition with ML, and churned history of customers, to predict “vulnerable to churn” customers.

Churn prediction modelSupporting Stats:

32% of all customers would stop doing business with a brand they loved after one bad experience.

Source: PWC

11.     Cross and Up-Selling

Business Challenges:

  • Insufficient client awareness
  • Poor internal selling
  • The wrong selling approach with clients

Solution:

Predictive Analysis can use Machine Learning to understand the customer’s behaviour and can recommend products and suggest discounts on various products to enhance the buying percentage of products on the online store.

Supporting Stats:

  • The probability of selling to a new prospect is 5-20%, whereas to an existing customer, it is 60-70%.
  • Amazon attributes up to 35% of its revenue to cross-selling from both “Frequently bought together” and “Customers who bought this item also bought” sections.

Source: Forbes.com

12.     Risk Mitigation

Business Challenges:

  • Online Security issues
  • Customer disputes and chargebacks
  • Credit card fraud
  • e-commerce taxation

Solution:

  • Predicts online security issues like malware, phishing and provide a secured layer to the e-commerce platform.
  • Predicts the chargeback reasons even before the customer reports, and provide necessary alternative solutions to act upon.
  • Watch out for suspicious transactions, and cancels the order.
  • Includes appropriate sales tax depending upon the appropriate shipping destination and predicts the exact delivery cost.

Supporting Stats:

  • For 3% of the customers the order was billed twice or there were clerical errors
  • 30% of the purchases were made with stolen credit cards.                                              Source: Clevertap

In the growing environment of marketing, business strategy and innovative technologies are the keys to flourish on every move ahead.

Predictive Analytics helps develop e-commerce business to accumulate revenue and amplify the voice of the brand.

If you need to inculcate Predictive real-time data analytics into your e-commerce platform, then get Started with IQLECT’s ShopIQ.

SignUp for a 14-days free trial.

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