Marketing attribution decomplexifies this landscape as it allows marketers to disentangle and measure the impact of their marketing efforts. At its core, marketing attribution consists in crediting each income/outcome stream to the right source of impact while taking into consideration synergy, saturation and halo effects.
The size of the marketing attribution market is expected to grow to US$ 3.6 billion by 2023 (Markets and Markets). This growth opens doors to further adoption from internal marketing teams.
As a matter of fact, investing in an internal marketing analytics team to perform the attribution work is a trend followed by a number of brands as it allows them a better control and reactiveness to market changes which in turn leads to a significant increase in the overall competitiveness and efficiency of the business.
However, in order to succeed the journey from outsourcing to insourcing, it is crucial that decision makers think about two crucial elements:
- The human resources and the training needed to ensure the delivery of internal marketing analytics projects.
- The technology to be adopted to enable internal teams to streamline the delivery of the projects and ensure the needed scalability and repeatability.
Internalising analytics will enhance the demand for ready and easy to use technologies that allow internal teams to run marketing attribution projects smoothly and efficiently. The availability and maturity of DIY software solutions is therefore crucial for a larger adoption of insourced analytics and the latter is one of the strategic decisions CMOs need to take.
So what are the advantages for self-serve marketing attribution?
Companies that are new to the field of Marketing Attribution generally opt for an external partner to run their attribution work, enjoying their white-glove service and benefiting from their cross-industry expertise and the efficiency of their processes.
On the other hand, companies that have a long history in running attribution modelling through external partners, start to feel the need to be more hands on to ensure a quicker responsiveness to market changes and better deployment and adoption of these methods within the business.
There is a number of benefits to this approach from which we cite:
- Timely Responsiveness: internal teams are close to the business and are prompted on a daily business about the latest development, hence making them well placed to gear the attribution work towards answering the most pressing business questions and diving deeper in the most needed direction.
- Limit external share of sensitive data: With the spread of legislation controlling the use of data, internalising attribution limits the exposure to this growing concern.
- Improved Transparency: Having analytics teams on site improves the transparency since the different stakeholders can be directly involved in improving and checking the accuracy of data, results and recommendations
- Increased Flexibility: this is particularly important in the context of project updates frequency that become indexed to the changing requirements of the business rather than to what has been agreed in the initial contract as things tend to change quite rapidly.
- Enlarged Scope: Having the support of internal teams means that more products/channels could be modelled regardless of the size of the market and the size of the media budget deployed, that in some cases tend to restrict the scope of the attribution work.
The growth of data sources, media channels and the increased demand for attribution pushes some business to adopt a DIY approach especially when they are used to run this type of analytics through external partners. While this approach reveals a certain number of advantages, it is very important that brands that decide to go down this route make sure they pile all the success factors prior to the implementation. Having the right resources, the right technology and the right level of training are important ingredients to make the transition smooth and ensure the success of the DIY method in the long run.