As reported earlier this week, Google recently launched an update to its DFA reporting suite with the addition of attribution modelling capabilities natively into the DFA interface. Beyond the basic models (namely: linear, first position, last position, time decay, etc.), DFA also offers the ability for users to build their own custom model to manipulate the rules of the model itself.

By offering it as a value-add within an existing ad server, it may force existing attribution software clients (who currently deploy the likes of c3metrics, Clearsaleing) to review their overall investment in attribution modelling.

Many agencies have been investing in developing their own proprietary solutions, often requiring the need of some form of 'big data' solution to execute this. Will Google's recent roll-out be seen as a positive for the industry? Will it help to increase more scaled adoption of attribution modelling as a practice by marketers? Will it devalue the internal tools being developed by agencies? We asked some industry leaders what they thought of this. The general consensus seems to be that, whilst added analysis is agreed to be a good thing, in the hands of non-analysts, the benefits could range from slim to adverse:

Samuel Watts, Associate Director, Starcom MediaVest Group, “This has certainly led to plenty of discussion our end. Anything that helps to move the industry away from judging on the last click is a good thing, although it is important that we do not move from one arbitrary model to another without thinking about the role that each channel and site is playing in the media mix. Adding an ability to use a rules-based approach will obviously help the adoption of attribution in DFA, however in our experience, generating the attributed results is the easy bit. The more difficult, and unsurprisingly more valuable, bit is having the expertise to analyse the results to make solid recommendations to increase ROI. This is the difference between a reporting and an actionable insight. At Starcom MediaVest Group we see the need for an automated attribution model as one aspect of best practice digital evaluation. Hidden within the web logs are a treasure trove of insight, the challenge for our analysts is getting to gems to inform better planning.”

Kate Tickner, Business Solutions Executive for Big Data Solutions at IBM, "There aren't many companies that can match Google for investment in data analysis tools, so in my opinion many will welcome enhanced analytical capabilities within their existing DFA solution. Others are likely to be less enthusiastic as they will see this as yet another way to extend Google's hold on the market place. Amongst the fans however, it will probably be a subset of people will be able to use analytics effectively or accurately. There is a balance to be struck between making analytics accessible and ‘dumbing down’ sophisticated analytical techniques too far. Users without the right skill set could recommend actions based on inaccurate data or flawed models. Organisations who are basing campaigns on the results of any complex analyses need to have checks in place to ensure they understand the quality of the base data and the transformations it has been through during the modelling process."

Peter Wallace, Head of Performance, Total Media, “There is still an air of mystery amongst clients as to how these attribution models are actually created and therefore how they benefit them. Essentially, Google is providing too much autonomy to agencies by allowing them to develop custom models. To increase the pace of development for attribution modelling, you actually need a much greater level of education and an industry-accepted standard. This product will produce greater client uncertainty and allow parties without the appropriate levels of expertise to create models which could in fact be inaccurate. Will this product devalue the agencies’ proprietary technology? No, not at all. In fact, it will accelerate the development of tools across the industry. Agencies will strive to add value through their experience and expertise until attribution modelling is fully accepted by clients and represented across every plan.”

Andy Mihalop, Head of Digital, Moneysupermarket, “The launch of Google’s attribution modeling tool is a positive step for marketers and builds on the existing path to conversion functionality in terms of custom modeling. This is undoubtedly aimed at SME marketers, who don’t have substantial media budgets and therefore the opportunity to leverage existing media agency or pure play attribution management solutions. I can’t see this gaining much traction with more sophisticated advertisers who require a more customised solution, such as integrating offline data. The other challenge is data analysis. Regardless of the solution, advertisers will still require skilled data analysts in order to leverage and action the insight. That may be tough for SME’s.”