• Skip to primary navigation
  • Skip to main content
  • Skip to footer
  • 020 3330 7010
  • marketing@allresponsemedia.com
  • E-mail
  • Facebook
  • Instagram
  • LinkedIn
  • Twitter
  • YouTube
ARM logo

All Response Media

  • Home
  • About ARM
    • About ARM
    • Meet the Team
  • Our Services
    • TV
    • Digital
      • PPC
      • SEO
      • CRO
      • Social Media
      • Programmatic
    • Offline Media
      • TV
      • TV Execution
      • Press
      • Radio
      • Inserts
      • Door to Door
      • Outdoor
      • DRTV
    • Analytics
    • ARMalytics®
  • Success Stories
    • Client Success Stories
    • TV Star Competition UK
    • TV Star Competition NL
    • Our Work With Startups
  • Content Hub
  • Careers
  • Contact Us
You are here: Home / Data / Getting the most out of my data!

Getting the most out of my data!

11th May 2016 by Dan Mowbray

There is a clear challenge occurring within the current media mix, which typically comes down to the large number of touch points a typical customer will interact with before making their final purchase.

Current standard attribution models are able to show the key points within the customer journey; for example, the first interaction which started the customer on the path to conversion or the last interaction which secured the purchase.

This information is of course important, but by no means enough. Advertisers should have a mind-set that no single channel is entirely responsible for the purchase, and that every channel contributes to the customer’s final purchase decision in some way.

Applying this mind-set ensures the data available can be fully utilised, meaning data naturally becomes more relevant, comprehensive and easily actionable.

Introducing data driven attribution

The data driven attribution model is an additional model now available. There is a key difference with the data driven attribution model and standard models. Rather than using static proportions to assign value to a touch point, an algorithm is used to determine the true value of each touch point. The model is constantly learning and is updated on a weekly basis, to ensure it is making the best representation of the data available.

Example

The current setup of a static attribution model could be assigning a high value to social campaigns, as it is assumed that they impact in lots of conversions. If in reality social campaigns are only playing a very small part in the overall journey, and it is actually PPC which is the largest influence, the data driven attribution algorithm will determine this and lower the value accordingly.

All Response Media Viewpoint

Attribution is all about being able to analyse past performance data in order to then influence key campaign decisions going forward.

Pros

✓ Justification of media spend: Not only will the model allow the identification of the best touch points, it will also identify poor performing areas to ensure budgets can be aligned correctly.

✓ Enhanced media mix: Since the model shows the true value of each touch point, it allows the overall media mix to be enhanced. For example, if a gap is identified within the customer journey, it then opens the opportunity to introduce a new form of media to plug this gap.

✓ Understand the multi-channel funnel: This understanding is crucial and ultimately allows campaigns to be planned better and more robustly.

✓ SMART DECISION MAKING: The underlying thing which data driven attribution opens up, is the use of actual live campaign data to them make smarter decisions moving forward.

Cons

χ Flexibility: The main issue with the use of data driven attribution models is flexibility, and mainly the lack of it.

  • If a data driven attribution model experiences a change in tags or groupings, then the model can no longer be applied and needs to re-learn before being utilised again.
  • Look back windows should be at the default of 30 days; this could be fine for some industries but not for others; e.g. long haul travel can have a long click to purchase time.
  • Although the model being updated can be seen as a positive, it could also be looked at negatively as this could simply be not often enough or too often for others.
  • Data Driven attribution data is only available from the date the model is configured, not retroactively.

χ Inaccuracies – A data-driven model will be less accurate if there is a time gap between training and attribution. The larger the gap, the larger the impact on accuracy.

Data-driven attribution is a progression for digital but still is not perfect. At ARM, we have trialled a number of different data driven attribution technologies to get to this view point.  It is not just digital channels that we need to take in a silo, but the full media mix. With ARMalytics, our advanced tech and measurement suite, we see the clear impact of TV/radio on web visits and the data driven models are not taking this into account yet.

Subscribe For More

Newsletter Signup

Footer

ARM logo

The Leading Performance Media Agency

Building businesses and brands by providing clients with an Unfair Competitive Advantage.
ARMalytics®

Get In Touch

London: Sutton Yard, 65 Goswell Road, EC1V 7EN
Phone: +44 (0) 20 3330 7000

Leeds: Marshalls Mill, Marshall Street, LS11 9YJ
Phone: +44 (0) 20 3330 8050

Amsterdam: Koivistokade 3, 1013 AC
Phone: +31 6 3761 9020

marketing@allresponsemedia.com

Privacy Policy | Cookie Policy | Modern Slavery Policy

  • E-mail
  • Facebook
  • Instagram
  • LinkedIn
  • Twitter
  • YouTube

Our Newsletter

Subscribe to receive exclusive media insights straight to your inbox. We respect your privacy.

Newsletter Signup

We are using cookies to give you the best experience on our website.

You can find out more about which cookies we are using or switch them off in settings.

ARM logo
Powered by  GDPR Cookie Compliance
Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

Strictly Necessary Cookies

These cookies are essential to provide you with services available through our website and to enable you to use certain features of our website.

If you disable this cookie, we cannot provide you certain services on our website and we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.

Analytical and Performance Cookies

These cookies are used to collect information to analyse the traffic to our website and how visitors are using our website.

For example, these cookies may track things such as how long you spend on the website or the pages you visit which helps us to understand how we can improve our website for you.

The information collected through these tracking and performance cookies do not identify any individual visitor.

Please enable Strictly Necessary Cookies first so that we can save your preferences!

Advertising and Targeting Cookies

These cookies are used to show advertising that is likely to be of interest to you based on your browsing habits.

These cookies, as served by our content and/or advertising providers, may combine information they collected from our website with other information they have independently collected relating to your web browser's activities across their network of websites.

If you choose to remove or disable these targeting or advertising cookies, you will still see adverts but they may not be relevant to you.

Please enable Strictly Necessary Cookies first so that we can save your preferences!

Cookie Policy

More information about our Privacy Policy and Cookie Policy