By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    AI analytics
    AI-Based Analytics Are Changing the Future of Credit Cards
    6 Min Read
    data overload showing data analytics
    How Does Next-Gen SIEM Prevent Data Overload For Security Analysts?
    8 Min Read
    hire a marketing agency with a background in data analytics
    5 Reasons to Hire a Marketing Agency that Knows Data Analytics
    7 Min Read
    predictive analytics for amazon pricing
    Using Predictive Analytics to Get the Best Deals on Amazon
    8 Min Read
    data science anayst
    Growing Demand for Data Science & Data Analyst Roles
    6 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: PAW: Predictive Modeling for E-Mail Marketing
Share
Notification Show More
Aa
SmartData CollectiveSmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Business Intelligence > CRM > PAW: Predictive Modeling for E-Mail Marketing
CRMData MiningPredictive Analytics

PAW: Predictive Modeling for E-Mail Marketing

JamesTaylor
Last updated: 2009/02/19 at 11:13 PM
JamesTaylor
7 Min Read
SHARE

Live from Predictive Analytics World

Arthur Hughes (author of Strategic Database Marketing) and Anna Lu of e-Dialog.com presented on predictive modeling for e-mail marketing. Arthur has been developing databases for database marketing for 30 years or so. Initially he focused on databases but found that people could not use them to make money and that led him to his focus on database marketing.

E-mail marketing is often considered as only a way to drive online sales but it also drives offline sales. E-mail is not recognized as the potential marketing powerhouse it is. E-mail produces far better returns than other direct mail and 4x the offline sales than online but remains siloed as part of online sales. In addition, data shows that multi-channel customers are more profitable than single channel customers and e-mail helps with all channels. Yet too many e-mail marketing budgets are departments are just lost among the online marketing department.

Predictive modeling does not get used in emails because of the cost difference – where direct mail is $600 or more CPM, email is $8 CPM and so the value of analytics seems lower. However, email open rates are down to 16% and a recession will s…

More Read

predictive analytics for amazon pricing

Using Predictive Analytics to Get the Best Deals on Amazon

Predictive Analytics Helps New Dropshipping Businesses Thrive
Data Mining Technology Helps Online Brands Optimize Their Branding
Promising Benefits of Predictive Analytics in Asset Management
What Role Does Big Data Have on the Deep Web?


Live from Predictive Analytics World

Arthur Hughes (author of Strategic Database Marketing) and Anna Lu of e-Dialog.com presented on predictive modeling for e-mail marketing. Arthur has been developing databases for database marketing for 30 years or so. Initially he focused on databases but found that people could not use them to make money and that led him to his focus on database marketing.

E-mail marketing is often considered as only a way to drive online sales but it also drives offline sales. E-mail is not recognized as the potential marketing powerhouse it is. E-mail produces far better returns than other direct mail and 4x the offline sales than online but remains siloed as part of online sales. In addition, data shows that multi-channel customers are more profitable than single channel customers and e-mail helps with all channels. Yet too many e-mail marketing budgets are departments are just lost among the online marketing department.

Predictive modeling does not get used in emails because of the cost difference – where direct mail is $600 or more CPM, email is $8 CPM and so the value of analytics seems lower. However, email open rates are down to 16% and a recession will simply cause still more email to be used (because its cheap) so open rates will continue to fall and subscription rates will do likewise – and unsubcribing costs money. E-mail marketing must get smarter to avoid this fate.

Anna then introduced a case study – a frequent flyer program sending out semi-monthly emails. Opt-out members were about 18% but accounting for nearly 30% of revenue – they were better customers. Furthermore these were mostly recent unsubscriptions.

To solve the problem they built a model using CHAID. They had all the member data plus all the email behavior information like opens, clicks (and what kind of link they clicked). They found some good predictors among the attributes. Next they looked at the revenue from the members and found that the top 10% generated 67% of revenue with the top 20% generating 84%. Clearly retaining the top 20% was going to be key while the bottom 50% contributed almost nothing.

They developed a risk-revenue matrix, mapping the lifetime value and the likelihood to defect. Clearly the ones who are valuable and likely to leave are the highest priority. Now these folks can be targeted. When they did this with Cingular in the past they were able to reduce churn by 26% and retaining millions of dollars.

The second example was an e-tailer, specifically an off-price name-brand retailer. Email is their single largest channel and their most important retention tool – linked to 40% of their revenue. Have lots of data like attrition, sales, seasonal purchases, departments shopped, number and value of items purchased etc. 50% of their revenue comes from their loyalty club (that has a fee) even though it only had 1.8% of the email list as members. So how to find potential loyalty club members in the other 98.2%?

Used logistic regression to build a model based on about 10 predictor variables such as lifetime purchases, email source (internal or third party), opens, clicks etc. For instance, total purchases and months since first purchase contributed positively but more recent email acquisitions were better. With this information could afford to spend money getting these high-potential folks to join the loyalty program. Of course you also do some testing with a random sample to see how much better the results are than random.

Besides the improved positive results, you need to value the lower unsubscribe rates. For many companies a subscriber has a value of $5-$15 – they must be replaced and this can cost say $14! If a mailing not only has less positive effects but also drives people away (because the customer feels they are not understood by the company) then the cost of replacing the subscribers could exceed the value gained making the overall email campaign a net value destroyer! As I like to say, people respond to decision (emails in this case) as though they are personal and deliberate so make sure they are!

Arthur is a great presenter and this presentation was terrific.

More posts and a white paper on predictive analytics and decision management at decisionmanagementsolutions.com/paw

TAGGED: data mining, email marketing, marketing, paw, predictive analytics, predictive analytics world, predictive model, response model
JamesTaylor February 19, 2009
Share This Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Data Ethics: Safeguarding Privacy and Ensuring Responsible Data Practices
Data Ethics: Safeguarding Privacy and Ensuring Responsible Data Practices
Best Practices Big Data Data Collection Data Management Privacy
data protection for SMEs
8 Crucial Tips to Help SMEs Guard Against Data Breaches
Data Management
How AI is Boosting the Customer Support Game
How AI is Boosting the Customer Support Game
Artificial Intelligence
AI analytics
AI-Based Analytics Are Changing the Future of Credit Cards
Analytics Artificial Intelligence Exclusive

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

predictive analytics for amazon pricing
Predictive Analytics

Using Predictive Analytics to Get the Best Deals on Amazon

8 Min Read
predictive analytics in dropshipping
Predictive Analytics

Predictive Analytics Helps New Dropshipping Businesses Thrive

12 Min Read
data mining
Data Mining

Data Mining Technology Helps Online Brands Optimize Their Branding

7 Min Read
analyst,women,looking,at,kpi,data,on,computer,screen
Predictive Analytics

Promising Benefits of Predictive Analytics in Asset Management

11 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
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?