We’re pleased to announce the launch of Senaryo, a media spend analysis tool designed to help you optimize your marketing return on investment.
In this article we’ll give a quick introduction to how it works. But let’s start with a quick explanation of why you should care ?
We live in a fast-paced world where, according to many, data is the “new oil” that powers practically every industry.
Marketing is no exception. To adapt to this change, marketers are seeking new ways to analyze newly available data and media spend to better understand how marketing investments affect their companies’ sales and revenue.
To accurately measure sales performance in this digital world, it isn’t enough to rely only on Google Analytics. You need to know where your sales come from, e.g. which campaigns, affiliates, or media prompted your visitors to become buyers.
That’s where attribution modelling comes in. An attribution model helps you assign conversion values to their sources and, ultimately, to optimize your marketing return on investment (MROI).
There are several types of attribution models, but they are not all equally effective.
For example, the last-click model is a good entry-level option — and is Google Adwords’ default setting – but cannot sufficiently handle most real-life online buying journeys. These journeys are complex and rarely follow a simple “log in – search – buy” pattern; people tend to click on multiple ads before they finally convert. First-click and linear models also come up short for the same reason. And time-decay and position-based models do not track particular channels and make too many assumptions about conversion values.
The fact of the matter is that some clicks matter more than others. It’s important to know the difference and identify how your various marketing channels are cross-influencing each other.
With data-driven attribution modelling, you can!
Data-driven attribution modelling in a nutshell.
Rapidly gaining a reputation as the most accurate modelling approach, data-driven attribution models assign fractional conversion credits to your marketing touchpoints. Such models involve two main steps:
analyzing the available conversion path data to develop custom probabilistic models and
applying sophisticated algorithms to those probabilistic models.
Markov chains are one example of the sophisticated algorithms Senaryo uses. Markov chains are mathematical systems that help you understand the probability of hopping from one situation / set of values to another one, a.k.a. “transitioning between states” in statistical terms. Such models assume that what happens next in the chain of events depends exclusively on the current state of the system.
Markov chains are particularly useful for modelling customer journeys, which can be viewed as a chain of touchpoints with each node representing a marketing initiative or marketing channel.
All very interesting, but how will it help me optimize my MROI?
By using all available conversion journey data on both converting and non-converting visitors, Senaryo’s conversion probability models help demonstrate:
how particular marketing touchpoints influence the probability of conversion and
how likely a user is to convert at any particular point on the buying journey, given a particular sequence of paid search clicks.
When using on Markov chains, for example, Senaryo provides an assessment of how likely a visitor will transition into the state of being a customer, i.e. convert.
For example, consider the following conversion journey with a Markov chain that has six unique states: start, channel 1, channel 2, channel 3, conversion, and no conversion.
The links represent the probability of a transition between the states:
In this example, channel 1 appears in 50% of conversions, while channels 2 and 3 appear in 100% of them. Therefore, channels 2 and 3 have double the weight of channel 1, and the Markov chain attributes 20%, 40%, and 40% of the conversions to channels 1, 2, and 3, respectively.
With that information, you can optimize your MROI by allocating resources to your marketing touchpoints and channels with the greatest probability of converting.
Here’s an example of what you’ll see in Senaryo:
In summary, Senaryo helps you optimize your MROI by:
accessing all your data from both external platforms and internal systems via APIs and direct database connections,
running sophisticated algorithm-based analyses on it, and
automatically recommending budget allocations based on the results of these analyses.
With Senaryo’s cutting-edge machine learning capabilities, you can see your e-commerce marketing from all angles and understand how to optimize it to grow your business in the fast-evolving digital world.
Get professional advice on the Media Spend Analysis from Lingaro.