Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    composable analytics
    How Composable Analytics Unlocks Modular Agility for Data Teams
    9 Min Read
    data mining to find the right poly bag makers
    Using Data Analytics to Choose the Best Poly Mailer Bags
    12 Min Read
    data analytics for pharmacy trends
    How Data Analytics Is Tracking Trends in the Pharmacy Industry
    5 Min Read
    car expense data analytics
    Data Analytics for Smarter Vehicle Expense Management
    10 Min Read
    image fx (60)
    Data Analytics Driving the Modern E-commerce Warehouse
    13 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Understanding Value
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Uncategorized > Understanding Value
Uncategorized

Understanding Value

TeradataAusNZ
TeradataAusNZ
4 Min Read
SHARE

It is interesting when we talk about the value of a customer (sometimes referred to as profitability). There are a number of different rules of thumb presented as the best way to derive value; the approach tends to be determined by the use of the outcome. Marketing wants to understand value to be able to differentiate services and offers, while finance wants to reconcile to the GL, and so on. Some approaches have flexibility to be a little wrong and others strive for perfection. So there is no wonder that the conversation of customer value causes so much excitement in organisations.

The challenge that becomes evident very quickly is that there is no such thing as the perfect model or a right way to build a model, there is only a wrong way in the eyes of the end users. Users will never use a model once it has been deemed incorrect for purpose, hence the proliferation of profitability models in organisations. In my experience organisations have at a minimum three different profitability based models, there is no wonder that a profitability conversation is so passionate! How do you move towards accepting a single model?

The adoption of any model takes time, people need to …

More Read

Mobile, Mobile, Mobile!
Examples of animations in R
Using Geographic Data
Reading – Viral Data in SOA: An Enterprise Pandemic
Pentagon Implements New Cyber Security Guidelines for Cloud Vendors



It is interesting when we talk about the value of a customer (sometimes referred to as profitability). There are a number of different rules of thumb presented as the best way to derive value; the approach tends to be determined by the use of the outcome. Marketing wants to understand value to be able to differentiate services and offers, while finance wants to reconcile to the GL, and so on. Some approaches have flexibility to be a little wrong and others strive for perfection. So there is no wonder that the conversation of customer value causes so much excitement in organisations.

The challenge that becomes evident very quickly is that there is no such thing as the perfect model or a right way to build a model, there is only a wrong way in the eyes of the end users. Users will never use a model once it has been deemed incorrect for purpose, hence the proliferation of profitability models in organisations. In my experience organisations have at a minimum three different profitability based models, there is no wonder that a profitability conversation is so passionate! How do you move towards accepting a single model?

The adoption of any model takes time, people need to understand the rules and assumptions that have been used when building the model, for all users to agree to its make up. Education needs to evolve with the capabilities of the organisation and model to ensure that people stay onboard. The complexity of the model needs to provide the end users of the model with the information required to perform their jobs.

The only gotcha is that the model needs to have the ability to become more complex over time, using more detailed information and more detailed rules as the organisation matures. On day one, no user is going to be interested in drilling down five levels to understand the why’s. But in time they will, consequently the model needs to be able to support detailed investigation not just a score without any context.

 

Daniel Tehan

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

student learning AI
Advanced Degrees Still Matter in an AI-Driven Job Market
Artificial Intelligence Exclusive
mobile device farm
How Mobile Device Farms Strengthen Big Data Workflows
Big Data Exclusive
composable analytics
How Composable Analytics Unlocks Modular Agility for Data Teams
Analytics Big Data Exclusive
fintech startups
Why Fintech Start-Ups Struggle To Secure The Funding They Need
Infographic News

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Make it Conversational

3 Min Read

DQ is 1/3 Process Knowledge + 1/3 Business Knowledge + 1/3 Intuition

5 Min Read

Yes, Virginia, Google Does Devalue Everything It Touches

6 Min Read

Social Media ROI is About the People

4 Min Read

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

giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?