We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
    7 Min Read
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
    chatgpt image jul 13, 2026, 03 59 46 pm
    How Data Analytics Improves Multi-Location Search Strategies
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Some Thoughts on Measuring Consumer Marketing’s Affect on the Path to Purchase
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Software > Marketing Automation > Some Thoughts on Measuring Consumer Marketing’s Affect on the Path to Purchase
AnalyticsMarketingMarketing Automation

Some Thoughts on Measuring Consumer Marketing’s Affect on the Path to Purchase

TeradataExperts
TeradataExperts
4 Min Read
Some Thoughts on Measuring Consumer Marketing’s Affect on the Path to Purchase
Photo by AlphaTradeZone on Pexels (https://www.pexels.com/photo/a-person-holding-digital-tablet-with-graphs-5833269/)
SHARE

For Consumer Goods companies it’s getting harder to argue with the value of knowing consumers and shoppers at an individual level. It’s simply best practice for any industry, but a relatively new idea in Consumer Goods where the historical emphasis has been brand marketing and trade promotions. Both of these very significant investments have been relied upon to drive sales absent direct relationships or detailed knowledge of individual consumers. That has to change, as noted in posts like this one.

The challenging part of the discussion begins with how difficult this visibility is to achieve given the lack of an existing consumer or shopper database that has a real person’s name at the center. Few Consumer Goods companies have complete visibility into the data generated across every agency, promotion, campaign, channel or brand. It’s a data integration and agency-process issue Teradata has worked hard to solve for some of the largest Consumer Goods manufacturers. The lens then focuses quickly on what consumer actions can be captured, measured, and improved upon to increase sales.

One idea to explore is mapping all consumer interactions to a “state-based” path to purchase which can be tested, validated then optimized. Consider all the ways both offline and digital marketing efforts have targeted consumers over the past several years and how these relate to different markers along the path.

  •  
    • Offline to online interactions out of store (television advertising, newspapers or magazines, presumably at or near home, featuring a call to action to register, scan or text).
    • Desktop web experience – register or log-in (presumed to be at or near home).
    • Email read on a desktop client (presumed to be at or near home).
    • Email read on a mobile device (“on the go”).
    • SMS text message interaction (“on the go”).
    • In-store interactions (QR code scan, unique SMS shortcode, unique email or mobile web, mobile application interaction data).

If you start with the assumption that certain interactions are inherently performed at-home, “on the go” or in the store environment you can begin to paint a picture of how digital marketing efforts affect the path to purchase. You start by asking relatively basic questions, such as:

More Read

Your Phone, Big Data and the Cloud
Your Phone, Big Data and the Cloud
Do Social Media Monitoring Tools Provide True Intelligence?
Are Video Streams Overflowing, And Will You Pay For It?
4 Best Practices for Sharing Workforce Data: A Mobile Component
No Wait in Kuwait – But Some Weight (Part 2)
  •  
    • How many consumers have you interacted with in these ways?
    • What consumers have interacted with you across these ways? A segmentation or profile emerges.
    • How have consumers interacted with you across these ways – the data provided and captured, the sequence and timing. Does an observable path emerge?
    • What patterns exist among the interactions that are especially insightful – in terms of moving a consumer through a hypothetical “buy pipeline” or along the path? What campaigns, incentives or creative ideas were responsible?

And that’s just the beginning. All interactions can be related to geographies, markets and individual retail locations to begin gauging a more direct impact consumer marketing has on sales than ever before possible.

How else might Consumer Goods marketers leverage consumer data to understand and shape the path to purchase in their favor?

 

Gib Bassett 

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Flat editorial illustration: The article describes an AI safety incident where an agent bypassed sandbox controls by exploiting D
OpenAI Pauses Advanced AI Work After Agent Bypasses Sandbox Controls
Artificial Intelligence News Security
Flat editorial illustration: The article explains that training robots for physical interaction requires three distinct data cate
Physical AI: What Data Do You Need to Train a Robot?
Artificial Intelligence Exclusive Robotics
What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
Analytics Big Data Exclusive Software
Flat editorial illustration: The article examines AI agents that escalate from legitimate data retrieval to attempted intrusions
OpenAI’s Government Website Incidents Raise a Hard Question for AI Agents: When Should They Stop?
Artificial Intelligence News Security

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

10 Tips on Getting More Out of Google Analytics
Analytics

10 Tips on Getting More Out of Google Analytics

6 Min Read
The Data Analytics of Leap Year
Analytics

The Data Analytics of Leap Year

3 Min Read
5 Tools That Use Big Data For Social Media Optimization
AnalyticsBig DataExclusiveSocial Data

5 Tools That Use Big Data For Social Media Optimization

7 Min Read
data analytics and commerce media
AnalyticsBig DataExclusive

Leveraging Commerce Media & Data Analytics in Ecommerce

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.

From Bolts to Bots: How AI Is Fortifying the Automotive Industry
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?