To Attribute or Not to Attribute?
To Attribute or Not to Attribute?

To Attribute or Not to Attribute?

How to measure success in digital advertising is often a tricky question for companies looking to expand their digital activity, or even those taking the first step in away from “traditional” marketing to a more online focused strategy. If every piece of activity on digital channels can be measured and quantified for the return it provides, there should be no more guesswork for advertising teams anymore, right?

Well the short answer is yes and no.

Mark Sullivan recently addressed the issue of determining success, or more specifically, return on investment (ROI) in a blog post for the excellent Smart Insights website.

Mark offered some helpful tips on how advertisers who were inexperienced with digital channels can cut through some of the noise to find a key metric which will show them exactly which of their activities are working and which are not. This obviously leads on to making more informed choices about how to allocate finite budgets between the plethora of digital channels now available.

The article outlines some of the most common metrics in pay-per-click (PPC) and social media platform reports, and Mark’s comments about how the wealth of information can lead to more confusion than inspiration are very relevant.

Return on Investment is the gold standard that today’s marketers are seeking. But with each new media channel, there is new complexity in the metrics used to calculate ROI. A couple of examples:


PPC Metrics


Pay-Per-Click (PPC) advertising has 8 typical Key Performance Indicators (KPIs) to track the effectiveness of campaigns, including:


  • Impressions: How many times an ad is shown
  • Quality Score: A search engine’s calculation of ad copy relevance to keyword or query
  • Clicks: How many times someone clicks on an ad
  • CPC: Cost per click
  • CTR: Click through rate
  • CPA: Cost per acquisition
  • Conversion Rate
  • Total Spend

Social Media metrics


Social media metrics include reach and engagement, in addition to platform specific metrics, including:


  • Likes
  • Followers
  • Shares
  • Retweets

All of this data can cause a marketer’s head to spin. Many SMBs and their marketers are asking:

‘In my social media, should I focus more on reach or on how many likes a promoted post has gotten?’
‘Are shares more important?’
‘Is Click-through-rate what I’m worried about, or conversions?’

The solution the Mark offers is measuring one key metric called Lead Acquisition Cost (LAC), which he explains below.

There is one clear metric that, regardless of the media channel, provides a simple way to begin calculating the elusive advertising ROI.


This metric can be measured the same way regardless of campaign, whether social media, search marketing, even a simple print ad. Think of this metric above all else: Lead Acquisition Cost (LAC).
Why is LAC so effective at connecting results to efforts? Because it focuses not on a click or a ‘Like’, but an actual prospect engaging with a business. Similar to Cost Per Lead (CPL) or Price Per Lead (PPL), it’s basically a business’s calculation and qualification of the amount paid for each lead, of any form, that a business receives.

It’s calculated by taking the cost of all investments in lead-generating campaigns and dividing that cost by the new leads acquired during a fixed amount of time.
For the purposes of this calculation, you should only count what some marketers call qualified leads. Qualified leads reflect the number of leads that were actually in the market for services or products your company provides. If you’re having a hard time figuring out how to qualify leads coming in, then focus on total leads for now. Once you begin to calculate LAC, your appetite for refining the data will grow along with your understanding of how to do it.

I would suggest that Mark is only partially right with this approach. Calculating LAC is a great place to start for advertisers to determine if their overall activity (which could involve multiple digital channels) is profitable compared to the number of sales being generated online or via telephone calls.

The difficulty (or opportunity for the optimists among us!) comes when revenue increases at around the same time as one or more digital marketing activities begin, but it is difficult to directly show that those activities are actually responsible for the increase in revenue.

A common scenario many businesses face when tracking online sales is that they are attributed to a branded search (whether this was served by an organic listing or a paid search result) or direct entry. So how does this reflect on all of the ads or social media campaigns which currently show a big fat 0 in the conversion column?

What the LAC model is great for is determining what is called Last Click Revenue Attribution. In the case of our example above, unless the business is a household name like Adidas or Coca-Cola, we know that the customer must have heard of us somewhere in order to make the branded search or type our website address into their browser for a direct entry, so the question is how?

The multi-channel attribution funnel in Google Analytics (shown below) is a great place to start putting the pieces of the puzzle together. You can use it to see how what percentage of people who converted via a direct entry interacted with your website via another source beforehand, e.g. PPC or social referral. These interactions are known assisted conversions, because they assisted the user towards the channel that is attributed as the last click before conversion.

multi-channel funnel attribution


Because of the complex, often overlapping, way that people interact with brands on multiple channels today, using multiple devices, making calls and even seeing offline media like billboards and flyers, not to mention visiting physical stores, getting a clear picture of customer journeys from start to finish is often very difficult, if not impossible. However, using tools like the multi-channel attribution funnel does at least show which channels are playing an assist role somewhere in the journey, from research to buying intent to brand awareness to conversion, that all customers typically travel along.

At the very least, if no assist conversions are visible for a business, testing one digital channel activity at a time and looking for correlated trends in uplift on direct entry or branded search conversions can begin to give advertisers an idea of the hidden role of their digital activities.

In short then, the LAC model represents an opportunity to measure what is working, but also a potential problem in attributing the real value of all activities. For the intermediate advertiser, thinking beyond the last click conversion is a must. A recent entry on the reef blog from Hadrien Brassens has more great tips for how digital marketers can use the wealth of knowledge available to them to drive amazing results for their clients.

Do you have any experience of attributing customer journeys or any thoughts on last click attribution vs assist conversions? Feel free to share them with me in the comments below!

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