The Difference Between Data Analysis and Attribution

October 8, 2019
• Author: Adrien


Tracking user activity with an analytics solution is a crucial part of every marketers responsibility. But that's just one side of the coin. You may know the sources that are giving you the most traffic, but do you know which drum up the most business?


What is the difference between data analysis and data attribution?

The short answer is that data analysis is finding meaning in a set of information. Typically it involves using a particular applications or set of applications; maybe, it has to do with some mathematical functions; and probably, the process includes a fair amount of time spent formatting and transforming before the real work begins.


Whereas data attribution - at least in a marketing sense - is crediting channels with value based on their ability to influence potential customers to convert.


Two quick sub definitions: a channel is typically an advertising platform, like Google AdWords; though it's really anything in your marketing mix that spreads the word about what you're trying to market. And, in most cases, conversion means making a purchase - the potential customer converts into a customer - or preforming a desired action, such as signing up for a demo or an email list.


However, that just the basics. The reality of this ever changing world is that the science of understanding your data is always becoming more and more complex. Companies with multiple digital and offline marketing channels have been struggling to define their marketing strategies.


Well, every company has lots of data these days so what gives? It's as they're collecting more than they know what to do with. To tell you the truth, they probably are. They're drowning in it. After all, the collection part is easy, knowing how to interpret it, is what's tough.


And, it's a fact: data is useless unless you can turn it into a source of actionable insight. Careers are build off of understanding the meaning of all of the numbers and how they can inspire change for the better. But what's the first step?


At ROIVENUE, we think that adequate marketing measurement is the best place to start when trying to understand the real needs of your customers and how they engage with your brand. Next, to obtain a full picture of your data, you need to blend analytics with solid data-driven attribution.


Understanding the actual needs of your customers by being able to put your thumb on what's working and what isn't makes all the difference.


Why is data analysis not enough?

Data analytics is the foundation. A company cannot build a successful digital strategy without using analytics to collect base insights. Analytics is key in understanding a company’s digital marketing performance and measuring clients' interactions with the brand’s online properties.


Analytics can collect data feedback that concerns your client behavior. You need it to discover what keywords return the highest earnings, what sections of your site have the highest engagement, or what pages are losing visitors.


However, something is missing. Although this data can significantly help companies make strategic decisions around their digital marketing strategy, it may not be enough.


How does Attribution create real added value?

Let’s be honest. Companies implementing multi-channel campaigns to reach their target markets have the risk of missing key details. The more complex a plan or endeavor becomes, the easier it becomes to overlook various aspects.

Simple data analysis as a marketing measurement tool doesn’t tell the full story of the many touch-points across a customers journey, or simply what led them to convert in the first place.


When data attribution enters marketing strategy

Defining your marketing approach based on data-driven attribution consistently improves tracking and increases the concrete value assignment of all touch points that lead to the outcomes that you desire. Data attribution digs deeper into customer behavior and ad impressions data, helping companies understand the complete customers journey.

What's more is that, by using data attribution, a company can determine customer lifetime value. Some marketers consider CLV to be the whole grail of obtaining and maintaining profitability. This measurement defines the maximum revenue potential of a particular customer or customer type, allowing companies to focus their efforts on individuals and groups that will provide the best value.


Another crucial component is that the price of attracting a new customer will always greatly exceed the cost of having a past customer place another order. Thus, cohorts of many different properties can be created.


How multi-touch data driven attribution impacts your marketing strategy?

Data-driven attribution uses sophisticated algorithms that lead you towards realistic and complete insights that will help you drive your marketing efforts.


When comparing each interaction and conversion path you need to be aware of all effective touch points over a customers’ journey. This is necessary because they are all relevant components used to determine where to focus your efforts.

An algorithmic attribution model allows you to effectively measure the most relevant KPIs for highly integrated multi-channel campaigns including profit, ROI, CLV, and sales. It is important to have a clear understanding of attribution data in order to improve how the companies are making their investments for running ads or creating content to optimize their marketing strategy.


Need an analytics or attribution consultation? Schedule a live one-on-one demo with one of our product experts!