Believe it or not, so many people within the marketing industry today still focus on that infamous last click. Yet, there’s much more to consider within a customer’s purchasing journey than the final tap of a mouse.
There’s no denying that every brand wants more from their marketing spend, in fact as an agency it’s something we strive to achieve for those we work with. However, the techniques used by the digital industry over the past 20 years to measure the impact of marketing channels are dated, and have been pulling the wool over many peoples’ eyes for quite some time.
Let’s escape the rigidity of the attribution model (and ignore the arguments in its favour, just for a second) and focus on our own online behaviour as a consumer.
Think back to the last time you bought something online. How many steps did you take? In the interests of sharing, I’ll provide an example of my own.
I’ve been needing a wooden shelving unit for my house for a while, and as we’ve just redecorated I had a specific style of wood in mind to match the rest of the room. To see what’s available, I went online to begin my research. Here’s the path I decided to take:
- I searched for “rustic oak shelving unit” on Google.
- I visited the top five sites, but didn’t find what I was looking for.
- Back to Google. Instead, I tried searching for well-known furniture brands like “Habitat” and “Made.com” - still no luck.
- Turning to social media, I asked for recommendations from friends. Nothing suitable came up.
- Next, I tried eBay directly and searched for “rustic oak shelving unit”. Some progress - I’d stumbled across something interesting.
- After discovering the term “mango wood” from eBay, I typed in “mango wood shelving unit” on Google.
- More browsing ensued, and I found a website that offered the perfect unit, with the correct style and dimensions.
- The website advertised a showroom, which was only 12 miles from our home. We paid a visit, loved the unit, but found it was over our budget.
- Once home, we tried searching for the unit again on Google using the exact product name.
- We found a cheaper supplier, but ended up procrastinating for four days.
- Back online, we searched for the supplier via Google and click on their website.
- We purchase the product from the new supplier, at a cheaper price.
That’s the process I went through to find a perfect wooden shelving unit, at a price we could afford. Going back to attribution, let’s see if my actions match up with some of the most popular models used.
First Click - the first step taken receives 100% of the credit for the purchase made
After searching for “rustic oak shelving unit”, the results Google served were very poor. While it did instigate my purchase, it certainly can’t take all the credit for being responsible for the sale; in fact, even if I’d started at a later step, the outcome could have been the same.
Last Click - the last step taken receives 100% of the credit for the purchase made
My last step involved Googling the name of the company I decided to make my purchase from. As you can see above, a lot of work went into discovering the company’s website - the last click can’t take all the glory here.
Linear - every step of the process is equally responsible for the sale
While fairer than both first click and last click models, I don’t believe every step taken to find the shelving unit achieved the same level of progress. Some steps definitely helped more than others.
Positional - the first and last click are worth the same, while every other step in the process is divided evenly
The even nature of spreading the credit doesn’t sit well with me as accurate attribution.
Time Decay - the closer the step to the conversion, the more credit it receives for the sale
While later steps, such as paying a visit to the store and searching for the product name, definitely had a significant impact on my purchase, the time decay model removes the importance of the earlier steps - such as searching “rustic oak shelving unit” on eBay.
None of the traditional models can accurately represent my purchase journey. Based on this, the graph below was created to represent the importance of each step fairly.
The graph reveals how the importance of each step in my journey towards purchasing a wooden shelving unit should be represented. You’ll notice eBay receives a large chunk of the credit, but this is justified as without the site I wouldn’t know mango wood existed and, by extension, the purchase would never have been made.
The offline store visit also prompted further research, explaining the 15% credit, and both the start and end of the journey also deserve more recognition than social, for example - they did instigate and close my search after all.
What’s next for the digital industry?
My example isn’t an exception, thousands of people follow a very similar process every single day, for everything from furniture and clothing to laptops, TVs and even cars. This being said, it seems strange that so many of us in the digital industry are measuring the success of marketing channels on a last click basis - giving all the credit to that final channel at the very end of the journey.
Why are people still relying so heavily on a last click attribution model? One word. Fear. Fear of their budget decisions being called into question, of what the data might say and of the effort required to measure things accurately and put new methods into place.
Change isn’t coming; it’s already here. The data and ability to make informed decisions about a client’s marketing spends exists right now, offering something you won’t find with last-click attribution. Hard work will be needed, and the initial setup may take a few months to put into place, but the insight this change will provide will make decisions concerning budgeting more educated.
The next challenge? Identifying which job you will allocate to each channel in your marketing efforts. Just as it isn’t PPC’s job to generate interest in your products, it isn’t display’s job to convert them. But that’s for another day…
My advice would be to initially look into improving your tracking; start to use an attribution model; and work towards a data-driven approach which will embrace and account for changing user behaviour.