Predictive modeling is a way to price, promote and predict product sales based on lots of unstructured data. The analytics process includes a variety of techniques such as using machine learning, monitoring the web for relevant data and analyzing historical facts to make predictions about sales trends, optimal prices and other critical marketing efforts. The approach uses data to generate a predictive score for people, employees, SKUs, components, machines and other processes involved in the sale and marketing of products.
How Does the Process Work?
The first step to conduct valuable insights is to define the project. That includes the goals of the effort, the variables, scope of the analysis and identification of the relevant datasets to be included. Other steps in the process include:
- Collecting Data
Accurate data mining is essential to the process. That requires gathering data from multiple sources to provide a more complete view of customer actions and browsing behavior. Business apps offer inexpensive ways to collect unstructured data from many sources including websites, social media pages, review sites, marketplace platforms and competitor websites. This information can prove invaluable by providing a broader view of the product, market for it, social media mentions, reviews, keyword choices and price tracking.
- Analyzing the Data
The process of making sense of unstructured data involves inspecting, cleaning and modeling the data that companies gather. This can be done manually, but the prospect is nearly Herculean. Most companies use some kind of business software to make sense of the data. The process usually involves machine learning capabilities or rules-based analyses to identify key information. The rules-based approach requires using logic and linguistic skills to define a set of categories and create rules for each category. This requires hiring a specialist or using the experience of skilled in-house IT staff and natural language processing functions. Manual categorization is also an option for managing small volumes of data.
- Verification Using Statistical Analyses
Analyzing related statistics can validate predictions, assumptions and hypotheses.
- Deployment of Predictive Models
Using modeling to create accurate models of real-world behavior can generate automated responses in everyday business operations or trigger staff alerts for more critical decisions. It’s important to monitor predictive analytics to ensure that each model’s performance falls within predictive parameters.
Why Should E-commerce Companies Use Predictive Modeling?
Predictive analytics has been around for quite some time, but few companies used it during the early years of computing because of the difficulties and prohibitive costs. Big data has changed the game as more companies must find ways to gain insights from unstructured data. E-commerce companies especially stand to benefit by predictive customer behavior and designing products and prices to get better sales results.
Technology and monitoring apps grow cheaper every year, so it’s more affordable than ever for companies to deal with unstructured data to model customer behavior. The benefits of predictive analytics include: 
- Predicting in advance what customers will buy
- Scheduling large and small production runs accurately to keep inventory at just-in-time levels
- Managing personalized recommendations and incentive offers
- Improving supply chain management
- Pricing products in the sweet spot between too expensive and too cheap
- Predicting when to use alternative pricing strategies such as those based on competitor prices, market penetration, milking the market and others
- Increasing business intelligence for developing new products and business opportunities
- Reducing fraud
- Earning higher revenues from recommendations, suggestions and cross-selling
What Are Some of the Predictive Analytics Technologies?
Information-monitoring and data-gathering apps are popular choices for low-cost solutions for collecting unstructured data from many sources. Business software grows more intelligent each day as many kinds of software incorporate machine learning into their algorithms. The market for predictive analytics software grows cheaper each year, and the market is expected to reach $9.2 billion by the end of 2020.  Some of the top predictive and modeling technologies include:
- Predictive Marketing Dashboards
This technology can bring together all aspects of predictive insights into a single dashboard that staff can use to monitor, manipulate and fine-tune predictions and corresponding actions. Staff needs only to identify what they’re looking for, and the software matches the request with data insights.Many business intelligence tools can work in tandem or run parallel to make business decisions more accurate based on key criteria. These resources can generate fraud and risk predictions, predict customer behavior and apply regulatory guidelines to business initiatives.
- Predictive Libraries
Some technologies and software include predictive libraries to fit most security and needs. Self-aware software can build predictive models based on criteria that companies select.
- Artificial Intelligence
Drawing from machine learning, artificial intelligence and statistics, AUI systems can apply machine learning in data mining to increase accuracy and relevance.
- Industry-Specific Analytics
Key industries have industry-specific technology to make predictions about business, production and supply chain management. For example, supply chain management predictive software can use comparative analyses to choose smarter ways to optimize the supply chain and monitor conditions in real-time to prevent costly delays.
Why Should Companies Invest in Predictive Technologies?
Most companies use only 0.5 percent of the total data that they possess. E-commerce companies have a wealth of data from their websites, social pages and marketing channels, and the sky’s the limit for third-party intelligence and internet monitoring.  Machine learning is becoming more capable as they retrieve and transform analytics into actionable insights.
Identifying, capturing and using big data can improve the bottom line for any e-commerce company. For example, Amazon earns 35 percent more income based on its automated product recommendations. Enhancing a company’s pricing strategy is one of the top benefits of predictive modeling. Most companies using the approach earn between 2 percent and 7 increases in profit margins and between 200 percent and 350 percent in return on their investments in predictive technology.
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References: Business2community.com: Making Sense of Unstructured Data with Text Analytics
https://www.business2community.com/business-intelligence/making-sense-unstructured-data-text-analytics-01930919  Blog.blackcurve.com: Why eCommerce Companies Should Use Predictive Analytics
https://blog.blackcurve.com/why-ecommerce-companies-should-use-predictive-analytics  Technologyadvice.com: Top 10 Predictive Analytics Tools, By Category
https://technologyadvice.com/blog/information-technology/top-predictive-analytics-tools/  Forbes.com Here’s How Data And Analytics Can Benefit E-Commerce Business Owners