By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data science anayst
    Growing Demand for Data Science & Data Analyst Roles
    6 Min Read
    predictive analytics in dropshipping
    Predictive Analytics Helps New Dropshipping Businesses Thrive
    12 Min Read
    data-driven approach in healthcare
    The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
    6 Min Read
    analytics for tax compliance
    Analytics Changes the Calculus of Business Tax Compliance
    8 Min Read
    big data analytics in gaming
    The Role of Big Data Analytics in Gaming
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Predictive Analytics: 8 Things to Keep in Mind (Part 7)
Share
Notification Show More
Latest News
ai in automotive industry
AI Is Changing the Automotive Industry Forever
Artificial Intelligence
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > Predictive Analytics: 8 Things to Keep in Mind (Part 7)
Business IntelligenceData MiningPredictive Analytics

Predictive Analytics: 8 Things to Keep in Mind (Part 7)

Editor SDC
Last updated: 2010/06/03 at 10:55 AM
Editor SDC
6 Min Read
SHARE

Theme 7: Prototype, Pilot, Scale

Edison did not invent the light bulb. He took a working concept and developed hundreds of prototypes rapidly, tested them and along the way figured out improvements that were required to scale his invention for commercial use. Julian Trubin writes about the prototyping process:

In 1879 Edison obtained an improved Sprengel vacuum pump, and it proved to be the catalyst for a breakthrough. Edison discovered that a carbon filament in an oxygen-free bulb glowed for 40 hours. Soon, by changing the shape of the filament to a horseshoe it burned for over 100 hours and later, by additional improvements, it lasted for 1500 hours.

Edison’s primary contribution to the development of light bulb was that he carried the idea from laboratory to commercialization, taking into consideration not only technical problems, but also issues like economics and the manufacturing of bulbs.

More Read

embedded bi tools

Embedded BI Tools Bring Huge Benefits to Business Applications

The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
Data Mining Technology Helps Online Brands Optimize Their Branding
What Role Does Big Data Have on the Deep Web?
How IoT Can Be Connected to Business Intelligence

We took a leaf from Edison’s book when we developed, our prototype, pilot and scale approach to deploy analytics solutions for clients.

In our experience rapid prototyping is essential to show the value of the initiative to senior executives.  One of our health care clients …

Theme 7: Prototype, Pilot, Scale

Edison did not invent the light bulb. He took a working concept and developed hundreds of prototypes rapidly, tested them and along the way figured out improvements that were required to scale his invention for commercial use. Julian Trubin writes about the prototyping process:

In 1879 Edison obtained an improved Sprengel vacuum pump, and it proved to be the catalyst for a breakthrough. Edison discovered that a carbon filament in an oxygen-free bulb glowed for 40 hours. Soon, by changing the shape of the filament to a horseshoe it burned for over 100 hours and later, by additional improvements, it lasted for 1500 hours.

Edison’s primary contribution to the development of light bulb was that he carried the idea from laboratory to commercialization, taking into consideration not only technical problems, but also issues like economics and the manufacturing of bulbs.

We took a leaf from Edison’s book when we developed, our prototype, pilot and scale approach to deploy analytics solutions for clients.

In our experience rapid prototyping is essential to show the value of the initiative to senior executives.  One of our health care clients wanted help in institutionalizing data driven culture within its sales organization especially in identifying and focusing sales effort on high potential customers. At first, we developed a prototype predictive scoring model to identify the high potential customers. Mapping the results of the model to existing effort demonstrated that greater than 50% sales force time was used ineffectively and the client was leaving a lot of dollars on the table.

However, for organizations to see bottom line benefit, adoption of predictive analytics based solution is key. Piloting helps refine the prototype and plan for potential adoption pitfalls amongst the end users.  At our healthcare client, we knew that there were skeptics amongst the sales people who do not trust the model and there were change management blind spots which we wanted to discover prior to the national roll out. We designed a pilot with the following objectives:

  1. Prove the validity of the predictive model
  2. Create evangelists from the sales team of the pilot regions
  3. Identify the big data gaps and establish a process of continually refining CRM data
  4. Establish and refine the key performance metrics to report to senior management
  5. Understand the key questions and concerns of the sales team in adopting the system

We collected a lot of rich quantitative and qualitative data during the pilot phase, which conclusively proved the value of the predictive model but also provided us with insights to incorporate into the roll out process.  For instance we learned that in a few instances customer address data was not getting updated  in the data warehouse and that sales managers wanted to understand the factors that went into calculating the predictive score of customer before they felt comfortable using it.

Scaling the pilot requires cross organization coordination and strong program management to ensure that the pilot learnings are incorporated in the roll out, there is a positive word of mouth buzz for the solution and there is minimal impact to day-to-day business. The inputs from pilot helped us better design the compensation rules and reporting metrics, which helped us roll out the system which head the trust of the sales force.

Our client saw significant uplift in revenue in the first 3 months of rollout. The sales organization started realizing the value of data driven approach and hired a team to support other sales analytics initiatives.

Are there other tips and tricks which you have successfully used to deploy predictive analytics solutions?

Link to original post

TAGGED: analytics, business intelligence, data mining
Editor SDC June 3, 2010
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

ai in automotive industry
AI Is Changing the Automotive Industry Forever
Artificial Intelligence
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

embedded bi tools
Business Intelligence

Embedded BI Tools Bring Huge Benefits to Business Applications

5 Min Read
data-driven approach in healthcare
Analytics

The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas

6 Min Read
data mining
Data Mining

Data Mining Technology Helps Online Brands Optimize Their Branding

7 Min Read
big data technology has helped improve the state of both the deep web and dark web
Big Data

What Role Does Big Data Have on the Deep Web?

8 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?