10 Data Analytics Models
to Crush Your Goals In 2017

Big data holds big value, and now more than ever, effective data analytics enables large enterprises to gain the competitive advantage by mining their ERP’s data repository for market intelligence. Today’s competitive enterprise environments require CIOs and other executives to make informed decisions accurately and promptly. Data analytics models are the most effective means of translating big data into decision-making information.

With this in mind, we met with our practice heads and data analytics team in search of the most impactful data analytics models that enterprises could implement to make the most of their ERP data. Without further ado –

Here are 10 Data Analytics Models
to Crush Your Goals in 2017

1.  Predictive Churn Prevention Models

Retain Your Customer Base

Leverage your ERP data to build models of customer behavior that can identify who is likely to switch to a competitor and why. These valuable models can be used to prevent customer churn and help implement highly effective retention campaigns to save your enterprise substantial revenue.

2.  Predictive Customer Lifetime Value Models

Identify Your High-Value Customers

Put your big data to work and calculate customer lifetime value. Pinpoint individuals with a propensity to invest more in your products and services so that relationships can be cultivated and nurtured to ensure a continuous revenue stream.

3.  Customer Segmentation Models

Refine Your Messaging

Group customers based on similar characteristics and buying behaviors in order to align your company’s marketing strategy and develop targeted outreach programs to these groups. Your big data mining could also uncover new insights that alter your marketing tactics!

4.  Adaptive & Predictive Next-Best-Action Models

Get to Know Your Customers

These predictive analytics models foresee the next best action by observing, learning and responding to life-event patterns, purchasing behaviors, social media interactions, and additional aspects. This allows your company to determine which customers need to be approached and the best channel to contact them.

5.  Predictive Maintenance Models

Don’t Fall Prey to Unforeseen Expenses

Predictive maintenance models can help forecast previously unpredictable machine breakdown, thereby helping companies to calculate and improve maintenance planning, leading to decreases in costly downtime of critical equipment.

6.  Predictive Product Propensity Models

Know What Customers Will Purchase Before They Do

Integrate your customer’s online behavior from social networks like Facebook, Twitter and Instagram with their historical purchasing data to identify and understand factors that will influence future purchasing decisions. Models can be used to identify which products a customer is likely to buy and automatically provide recommendations, thereby increasing sales and driving revenue growth.

7.  Quality Assurance Models

Evoke Confidence in Your Products

Quality assurance models prevent defects in your products and avoid headaches when delivering solutions to your customers. Use historical data to detect and solve problems in production and ensure that equipment, machinery and processes are delivering proper output and quality. These models will provide you with the peace of mind and keep your quality management on point.

8.  Predictive Risk Models

Identify and Mitigate Your Risks

Banking, insurance and telecommunications organizations are capable of mining big data with models designed to deliver faster insights into fraud and score liabilities. These models are designed to help organizations spot and abate risk exposure. Auritas uses and recommends an industry standard process called CRISP-DM, which is an acronym for Cross Industry Practices for Data Mining.

9.  Sentiment Analysis Models

Protect Your Reputation

Sentiment analysis, or “opinion mining” models identify, extract and categorize information from publicly available data sources, such as online reviews, blogs and social media posts. Their purpose is to analyze and determine sentiments towards an organization and its products and services. Assess the polarity of product reviews and discussions around the web and quickly adopt strategies designed to counter negative opinions and enhance positive sentiment.

10.  Predictive Upsell & Cross-Sell Models

Sell More, Sell Smarter

Alleviate the depletion of resources and increase selling power to support year-over-year growth. Predictive upsell and cross-sell models combine buying behaviors and market basket analyses to reveal insights into which products and services customers have the propensity to purchase and actively cross-sell and upsell them.

While we chose not to rank in order of importance, (due to the varying nature of businesses and the industries they participate in) predictive models overwhelmingly dominate our list. This is a direct result of the evolving landscape’s increased demand for ERP solutions that allow for informed, accurate and prompt decision-making, and the best-of-breed companies, such as SAP, delivering ERP predictive data analytics solutions, including a SAP S/4HANA. A company would benefit by incorporating any of these 10 models, and we certainly pity the competitor of the company that incorporates them all. Here’s to crushing your goals in 2017!

For more information, contactus@auritas.com

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