Big value generation is possible from big data. More than ever, evolving data analytics technologies now make it possible to competitive advantage by mining data repositories to generate market intelligence insights.
Our times, the competitive enterprise management environments require the chief information officers and other IT and data management executives to make more insightful decisions promptly and accurately to react to the quickly changing business situations. Analytical models are the ideal means of translating big data stores into insightful decision-making info.
Here we will explore some of the most impactful data analytics models as suggested by the most impactful enterprise analytical experts, which one could try implementing to get the most out of your ERP data.
Context
- Introduction
- Practical Data Analytics Models
- Predictive Churn Prevention
- Predictive customer lifetime
- Customer segmentation
- Predictive next best course of action model
- Predictive product propensity
- Predictive maintenance
- Quality assurance
- Sentiment analysis
- Predictive risk
- Predictive upsell
- Conclusion
Introduction
Analytical modeling is the key to exploring and understanding the underlying data patterns and the story it has to tell. Analytical insights are now used to make predictions and facilitate business decisions.
Without these models, it may be nearly impossible to gain any actionable insight from the historical data. Analytical modeling has now grown to the degree that it can be applied to live streams of data, making it much quicker and effective in responding to real-time situations.
While dealing with analytical modeling, it is important to choose the right sets of data, fix algorithms properly, use apt technologies, and ensure appropriate data formats to help resolve complex business problems.
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Practical Data Analytics Models
1. Predictive Churn Prevention
This model is increasingly used to retain the customer base. One can effectively leverage the ERP data to build customer behavior models, which can identify the factors like who is likely to switch and why. These valuable data models can be used to prevent customer churn rates, implement retention campaigns, and save substantial revenue for your enterprises.
2. Predictive customer lifetime
This model will help to identify your top-value customers and devise plans to engage them effectively. You can put your big data to assess the customer lifetime value. For this, you may pinpoint the individuals with the potential to invest more in your products and services and cultivate relationships to be nurtured to build a continuous revenue stream.
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3. Customer segmentation
This model can be implemented to refine your customers better and develop a need-based or personalized messaging strategy. In the customer segmentation data analytics model, you are ideally grouping the customers based on similar characteristics, demographics, or buying behaviors to develop targeted marketing strategies and better outreach programs for each customer group. Big data mining can also uncover better insights, which may help alter your marketing tactics positively. For effective remote data management to facilitate segmentation, you can consult RemoteDBA.com experts.
4. Predictive next best course of action model
This model is also aimed at getting to know your customers better. Predictive analytics can foresee and advise the users on the next best course of action by closely observing the patterns and responding to life events, purchasing preferences, social media communications, and various other such aspects. This approach will help your organization to identify which customers to be approached in a predictive model and the best channel to reach them.
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5. Predictive product propensity
Know what the customers may purchase before they initiate an action. This model is working based on users' online behavior over social media networks and other websites. This info is combined with their previous purchase history to understand the decisive factors that will influence the customers' future purchasing behavior. Such a model can be used to identify which types of products customers are more likely to purchase next to provide them with customized recommendations and increase the possibility of sales and revenue growth.
6. Predictive maintenance
This model of data analytics will save you from falling prey to any unforeseen expenses. Predictive maintenance is a widely used data analytics model that forecasts any chances of an unpredictable machine breakdown based on the previous events and thereby helps the organizations calculate and implement maintenance planning, which will decrease any costly downtime or critical errors the equipment, etc.
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7. Quality assurance
This model will help to evoke consumer confidence in your products. The quality assurance model will help prevent any defects in the products and avoid headaches while delivering solutions to the customers. This model also uses historical data to identify problems in the past and ensure that needed processes, equipment, and machinery are there to deliver an output of optimum quality. Such a model will offer you peace of mind and can keep the total quality management on track.
8. Sentiment analysis
It will help to protect the company's reputation. The sentiment analysis model is also called the opinion mining model, which will help extract and categorize the information from various publicly available data sources like a blog, reviews, social media posts, etc.
This model aims to analyze and determine the sentiments of the mass public towards various products, services, and brands. By effectively analyzing the polarity of discussions and reviews on the web, a business can quickly adapt to the strategies designed to counter the negative sentiments and leverage the positives.
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9. Predictive risk
The predictive risk model focuses on identifying the risks and mitigating the same. In the case of industries like insurance, banking, telecommunications, healthcare, etc., big data models require the ability to mine huge data stores and deliver quick insights to identify the possibility of errors or fraud. Predictive risk analysis models are aimed to help such organizations spot and abate any risk exposure.
10. Predictive upsell
To help sell more. A predictive upsell model will help alleviate any depletion of the resources and increase businesses' selling power to support growth. Along with predictive upsell, cross-selling models can offer insights into potential products and services that customers have the propensity to buy.
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Conclusion
The evolving landscape of ERP systems will bring forth more possibilities of analytical data models. As of now, by incorporating these ten top models, you can surely gain a competitive advantage in the market to crush your 2021 goals.
Above mentioned practical data analytics models are
- Predictive Churn Prevention
- Predictive customer lifetime
- Customer segmentation
- Predictive next best course of action model
- Predictive product propensity
- Predictive maintenance
- Quality assurance
- Sentiment analysis
- Predictive risk
- Predictive upsell