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With the adoption of mobile, over the past decades, e-commerce has grown at a notable level, by bringing shopping literally into consumers’ hands. Technologies impact every stage of the customer’s online shopping journey —from personalized marketing to price managing, and behavioral analytics.

One of the most influential technologies in the industry – is retail predictive analytics. Business owners are aware of the fact that providing value through a targeted campaign is no longer enough. The ability to be the first who predict trends is a new distinguishing factor.

Wonder how predictive analytics actually work? Read the article to answer this question!

How is eCommerce Predictive Analytics Works?

Predictive analytics is statistical modeling that analyzes historical data, and with the help of different techniques, methods, and tool (data mining, data modeling, deep learning, machine learning, and AI algorithms) make predictions on the future. Predictive analytics in the retail industry understand risks and opportunities, analyze buyers’ behavior, and assist with inventory management with the help of the data patterns that are possible to predict. In simple words, predictive analytics turn past and current data into valuable future actions.

Also, eCommerce predictive analytics covers what if?’ scenarios: what if I raise the price in April by four percent? What if I add another sales promotion in May?

Reasons to Use Predictive Analytics in eCommerce and Retail | Inoxoft

This year the market is expected to reach up to $11 billion in revenue, compared to $7 billion in 2020. Because more and more businesses make use of predictive analytics in retail and other industries.

Read more: data analytics in manufacturing

The types of Predictive Analytics

There are three types of predictive analytics businesses can employ:

  • decision-making modeling
  • predictive modeling
  • descriptive modeling

Decision-making modeling

Shows relations between elements in a decision. These can be the data, the decision, and the forecasted results. This relationship can forecast potential results, increase the chance of the needed outcomes and decrease the others.

Predictive modeling

Use statistical data to predict the outcomes and make sure that similar units in different samples have similar performance or vice versa. With help of predictive modeling, you can predict customers’ behavior and detect risk.

Descriptive modeling

Classify clients into groups to describe relationships within a dataset. So, you get product preferences accounting for age, status, gender, etc.

Big data vs Predictive analytics

Reasons to Use Predictive Analytics in eCommerce and Retail | Inoxoft

Benefits of Using Predictive Analytics for eCommerce

Here are a few benefits of predictive analytics usage in eCommerce that any company can obtain.

Product recommendations

With the help of predictive customer analytics in retail, a store won’t recommend a “vegetarian frying pan for steaks”.

Before recommending any products, online stores, companies, and services check different criteria: user’s previous behavior, purchase history, the current season, browsing history, in real-time. For instance, a smart system of recommendation is a common approach within the industry. Tech giants like Netflix fully use the power of data analytics. Their well-known recommendation AI-powered algorithm discovers customer behavior and buying patterns and then suggests movies and TV shows based on received insights.

The same strategy we can observe with Spotify, Amazon, eBay, every streaming platform. Predictive analytics, with advanced Machine Learning capabilities, correlate data from different sources to create a personalized experience for each client.

Price formation

With predictive analytics tools, lots of companies are able to implement dynamic pricing. Advanced ML algorithms form prices based on the analysis of many factors: the season, day and time of the week, weather, holidays, current demand, etc. Based on the information a system suggests an appropriate price range for a particular service. This approach is well known to users of Uber, Airbnb, different airlines, etc.

Supply chain management

Predictive analytics helps you effectively manage the supply chain process, including planning and forecasting, sourcing, fulfillment, delivery, and returns. Some retailers have experienced up to 60% reductions in operating margins by applying analytics to supply chain management. What benefits may it bring?

  • improved stock management
  • greater order fulfillment
  • optimized use of available warehouse space
  • better use of cash flow
  • no “out-of-stock ” items

Fraud management

The probability of fraud in online payment is pretty big. eCommerce predictive analytics studies customer buying patterns, payment methods, etc. The implementation of predictive models identifies potential fraud, reduces credit card payment failure, secures online retail, increases conversions and sales.

Enhanced Business Intelligence

One of the most valuable things predictive analytics can bring to your business is the ability to understand market trends and customer expectations in real-time. BI tools capture customer data, review trends, and develop models that determine what a client might like. The BI leads to better decision-making and better service overall by offering the products clients want at the price they want.

Also, predictive analytics in the eCommerce industry allows companies to:

  • Increase business capabilities
  • Become competitive on the market
  • Make the most money on your sales
  • Run targeted campaigns
  • Obtain new service (product) opportunities
  • Gain insights according to clients preferences
  • Reduce cost waste and risk incident
  • Determine the price a customer is ready to pay for your product
  • Satisfy user demand 100%
  • Enhance business intelligence
  • Make fast and accurate business decisions

What are Predictive Analytics Use Cases in Retail?

Let’s discover a few predictive analytics examples in retail:

Demand forecasting

Instead of human judgment, Nestlé used analytics for demand forecasting. As a result, the enterprise managed to make multimillion-dollar reductions in inventory and reduce its inventory safety stock by 20%. The technology they’ve implemented senses demand signals, associated with, price, advertising, sales, promotions, and economic factors, automatically notifying a company what things are actually influencing customers to buy their products.

New locations

Companies used to manage their strategies for growth or finding the best places for new locations manually. It required examining local demand, neighborhood demographics, land costs, etc. It was time-consuming and challenging.

Starbucks uses geospatial data analytics to find effective locations. Algorithms allow them to consider all the details: local population, average income, nearby competitors, and so on. As we can see, this approach seems to be convincing.

Personalized experience

An international beauty-products retailer, Sephora uses predictive analytics to offer personalized experiences to its customers through its mobile app. Moreover, today people are willing to share their data to get a personalized experience.

Based on clients’ interests and preferences, the company generates customized beauty recommendations, access to recently launched products, and invitations to in-store events.

Learn how to build a big data business strategy!
           

Inoxoft’s Experience

Reasons to Use Predictive Analytics in eCommerce and Retail | Inoxoft

Inoxoft leverages intelligent technologies to power the industry. We provide Big data analytics services and ML development services to help you obtain valuable insights and apply effective solutions to identify trends, markets and risks before they appear. Our team offers expertise in

  • Natural Language Processing
  • Predictive Analytics
  • Sales prediction
  • Pricing analytics
  • Marketing optimization

Our story of creating Predictive Customer Segmentation for Retail Company!       

Final Thoughts

Predictive analytics in retail is becoming a crucial part of the industry. As the data analytics market grows, more and more affordable solutions and models are being developed. So, lots of startups, enterprises, and local businesses have been adopting technology to lead the market.

If you are ready to implement an idea, contact our expert to get all the details and answers to help you achieve your business goals as soon as possible.