Digital marketing is hard work. After you determine the best starting point for your marketing roadmap (i.e. finding out what your customers are looking to learn) you start to invest your time and money into reaching them and letting them know you can solve their problem. Generating relevant leads and eventually converting them into buyers are the stretch goals that follow.
A great return on investment is when you start delivering on those stretch goals; when your marketing strategies turn into positive outcomes. Moving forward, the next seemingly logical step is to continue doing what you are doing and expect the same results. In theory, that should work. But would it in a real world scenario?
Every purchase made on a website is the result of a buyer’s journey during which there are various touch points. Every step of the way, businesses are employing a number of marketing tactics through different marketing channels, as part of the often-quoted Awareness>consideration>purchase cycle.
So what is it that worked with the last campaign? How to know which touch point should the credit go to for the recent successful conversion? Which tactic or channel of marketing should the future resources be concentrated on? These questions give us a vantage point, through which emerge three kinds of businesses:
1) Businesses that are grappling with the questions above and struggling to find the answers;
2) Businesses that have found their answers by attributing the conversion to either the first or last customer-brand interaction or simply giving equal credit to every interaction; and
3) Businesses that follow a rather more complex—albeit necessary—attribution modelling
Now, is it a good thing to have a straightforward attribution approach (2nd on our list) wherein you either attribute 100 percent of the credit to the first or last touch points or simply decide to divide the credit equally between all touch points?
The answer lies somewhere between “not sure” and “not quite.”
The problem with the straightforward approach is that you are not factoring in the journey that led customer to the purchase stage. In essence, you are not concerning yourself with asking which of the specific marketing activities carried out were most responsible for nudging the customer towards conversion.
How Would Attribution Modelling Help
Attribution modelling is a great place to start (but by no means is it the place to stop). This approach aims to cast the spotlight on metrics that have made the most difference, by determining the value of each consumer touch point that led to conversion. But there is no guarantee that a specific attribution model will work for a given business.
Let’s have a quick glance at the most commonly used attribution models:
- Single-source: The single-source model attributes the conversion success to either the first customer-brand interaction or the last customer-brand interaction (that happened right before the purchase). As pointed out earlier, this model is flawed simply for the disregard for buyer’s journey.
- Last non-direct click: This one is also a single-source model except for a difference – if purchase was made on a direct visit to your website, the non-direct click model deems that the decision to buy was made before the direct visit, and the credit should go to the last channel that preceded the direct visit.
- Linear: The Linear model works on the equal-recognition principle. It states that because there are multiple touch points in the mix, attaching more value to one over others may lead to future resources being inaccurately assigned.
- Position-based: The positional model takes a middle ground. The first and last touch point are given equal credit for conversion and the remaining credit is divided evenly among the middle interactions.
- Time decay: The closer a touch point is to the purchase activity, the higher up it is in chain of most important customer-brand interactions.
It’s not early days for attribution modelling, though majority of businesses are yet to adopt it. As the business environment is turning more and more data-oriented, we may see an increasing number of businesses taking interest in this method.
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