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4 reasons why more businesses should use media attribution

Author's avatar By Hugh Gage 05 Nov, 2014
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Why is attribution modelling important?

Ever since its birth in the mid 1990s, online advertising has been lauded as the most accountable advertising medium ever, but it has taken a while to live up to the promise... The early days of measuring online display campaign effectiveness based on click-through rates alone seem excruciatingly painful to recall for example!

Fast forward to 2014 and the latest online media spend figures show that digital media spend has increased dramatically, with predictions that Digital media will account for 75% of all marketing spend in 5 years. With this kind of money comes a responsibility to ensure media spend isn’t being wasted, or at the very least to find ways of explaining how just much value is being derived from it.

More advanced digital marketeers have started looking towards attribution modelling to help better understand what their budget is doing for them and how it can be made to work harder.

Yet, research by Smart Insights has shown that it's rare for marketers to use sophisticated attribution models. While not first to market, Google Analytics has put attribution within reach of all businesses, so in a new guide for Smart Insights, I have explained how to apply GA to review and adjust your digital media investments. In this post I will look more broadly at some of the reasons why attribution should get more attention.

4 reasons why attribution should get budget-holder attention

There is no doubt that attribution modelling is not simple or for the faint hearted and while there are still questions that rumble on about the genuine level of its value, in principle at least it holds the promise of something better. In dealing with the question about why it’s important, one can boil the answer down into these key elements (there are probably others too)

  1. Wastage
  2. Margin
  3. Conversion
  4. Allocation of budget to media mix

The first two seem pretty obvious!

  •  1. Wastage

Wastage in digital media not only means inefficiency and money that was badly spent but at a broader level it also points the finger at missed opportunity, sort of like an opportunity cost.

If budget in an online media campaign was more efficiently allocated, then it permits one of two knock-on possibilities:

  • The opportunity to re-task funds to other acquisition channels in order to drive growth
  • The opportunity to completely reallocate funds to another area of digital marketing such as a split testing program, perhaps focussed on landing pages (if one was looking to retain some synergies with the acquisition strategy).
  •  2.  Margin

This is really is fairly simple, less money spent on acquiring the same revenue will amount to better trading margins and the potential for more profit.

The third is more complicated...

  • 3.  Conversion

For better or worse conversion rates are heavily ingrained in digital marketing. Either by themselves or in concert with other metrics they have come to represent most aspects of the digital marketing pallet.

An improving conversion rate is often considered the harbinger of greater efficiencies and better profits for e-commerce businesses, so it’s unsurprising then that conversion rates are also used to evaluate acquisition. But of course visitors don’t use one channel to arrive at a site and depending on the product or service they may well take more than one visit to make up their minds about a purchase.

On the face of it attribution modelling is all about delivering a greater volume of good outcomes and revenue for less budget however, conversion rate is not a metric that is commonly included in attribution modelling.

Yet if a marketeer is spending £$€ xx,xxx each month on an integrated marketing acquisition program with an overall conversion rate to sale of 1.5%, then following the ethos mentioned earlier the process of increasing that overall conversion rate is evident. By reconfiguring the digital media mix could potentially yield more revenue for less spend depending on the individual media costs associated with the channels involved.

A further consideration might be as follows: if one channel in the mix is shown to act comparatively independently i.e. it neither assists nor receives much assistance from other channels and therefore the sales it delivers are unlikely to be harvested from any of the other channels. As a result, an argument could be made to increase investment in this source on the basis that it will deliver a corresponding increase in sales and revenue which could not have been harvested from anywhere else. However this would be to miss two fundamental points:

  1. If the cost of using this channel is greater than the others then a boost in investment here might end up delivering an increased cost of acquisition.
  2. Following the incumbent dogma, if the conversion rate for this channel is lower than the others then this too could drive up the overall cost of acquisition and potentially bring down the site average conversion rate.

There are some obvious problems/questions at this point:

  • How do you calculate conversion when you are no longer using a single touch-point attribution model and where the degree of credit you assign to each touch-point may in the multi-channel funnel may vary depending on its position in the journey?
  • When thinking about acquisition source, should you even be thinking about conversion rate or should the primary marketing metric now become cost of sale / cost per newly acquired customer / cost per retained customer? The issue here is that not all costs are so easily quantifiable e.g. organic search costs can be very variable and do not easily lend themselves to analysis in Google (Universal) Analytics.
  • Should conversion ratio now only be viewed on context of site design and usability?
  • What about lifetime customer value in the context of ROI?

 The point here is that while it’s no bad thing to focus on conversion rate in order to assess the impact of developments to your site design (although even this is questionable in relation to e-commerce sites). When it comes to optimising your digital media spend, conversion rate is increasingly becoming yesterday’s metric and possibly even a danger to the wider effort.

Optimising your acquisition strategy using attribution modeling might yield greater cost efficiencies but at the same time it may also suppress your conversion rate. If that does happen, then accept it. It’s important to get away from the dogmatic idea that a declining conversion rate is unanimously a bad thing.

  • 4.  Allocation of budget to media mix. Once attribution models have been reviewed, it may the case that too much credit is given to certain traffic drivers with the last-click model. These drivers could be channels, media placements or keywords. Some typical examples of reallocation of budget that may be indicated by attribution reviews are:Increase display budget. Banner ads typically have more of an awareness-driving role, so their value be underestimated by last-click.Increase generic search term spend. Consumers are more likely to search on general keywords which searchers may type “trainers” or “running shoes” when exploring a category such as in our sport shoes examples.

    Increase social media investment. Paid ads or organic clicks from social media updates, like display ads are less likely to directly result in a lead or sale on a first visit, but may be the first point of contact of a visitor with a brand.

    So, a study of attribution can show that investment in a particular media channel or tactic is actually more worthwhile than the last click method would suggest, so budget can be reassigned to it, particularly if wastage can be identified in other channels...

All this being said, it is important to understand that attribution modelling is not a panacea but it does have the potential upend even the most basic assumptions on how we’ve been measuring acquisition performance by channel until now.

Author's avatar

By Hugh Gage

Hugh Gage of Engage Digital is the Smart Insights Expert commentator on Digital Analytics. He is author of our Expert member guide to Tag management. Hugh has worked in digital communications since 1994, switching from roles in media where he was Head of Online Planning and Buying at Manning Gottlieb OMD, to senior analyst at Logan Tod now part of PwC. He was one of the first to hold the Google Analytics Individual Qualification and is also a DAA Certified Analyst. You can follow him on Twitter or connect on LinkedIn.

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