Marketers struggle with conversion attribution
Marketers struggle with conversion attribution
23 JULY 2014 6:29 AM
Conversion attribution continues to be tough for marketers due to the lack of standard technology and increasingly complicated booking behavior.
GLOBAL REPORT—Conversion attribution has become more complex with the booking funnel passing through multiple sessions, sites and devices. As marketing’s ambit of responsibility alters to handle a wider list of intertwined touch points, marketing analytics needs to evolve as well, sources said.
“At the moment we are tracking various marketing channels much more independently, by using the most appropriate technology for different channels,” said Angie Ho, VP of online marketing at Pan Pacific Hotels Group, via email. For example, Pan Pacific uses Marin and Google Analytics for pay per click and MediaMind for display ads.
Ho said of utmost importance is to enable a consistent standard across all channels to properly attribute the value of each source in a customer’s journey. 
“The value of digital marketing will be further amplified if the same standard technology can track the touch points of the user across different devices. It is easier said than done, and so far Google seems to come closest to this with big data,” said Ho, who believes with all the progress being made in the digital realm this is not going to be too far away.
“Honestly, what you are asking is the impact of organizations utilizing services from a fragmented, and diverse set of offering in the digital marketing ecosystem,” said Suneel Grover, senior solutions architect for SAS Institute.
He said entities are using services from large and small technology vendors to address this. This has culminated in analysis applications that look at digital marketing challenges in silos, and not in any form of an integrated framework. 
Grover said if hotel companies cannot join data from these various channels, process through the large volumes and varying structures and ultimately apply algorithmic analysis, then they will continue down the path of inaccurate attribution approaches and misinterpret the customer journey.  
He said there is a need to create a process of data access to uproot the siloed digital data streams feeding into these basic channel-specific reporting applications. “Analytic models are limited to what we feed into them. … The less of the customer experience they can see, the less valuable these models will be.”
Sources identified a couple hurdles in the race: 
Casey Ueberroth, ‎senior VP of marketing at Preferred Hotel Group, acknowledged via email that social media and public relations can be challenging. 
The group has approximately 50,000 followers on Facebook and nearly 5,000 followers on Twitter. 
“We have ongoing conversations and dialogue occurring across both channels and are finding a loyal audience. Even so, it is still complicated to tie back those impressions to bookings,” Ueberroth said. 
“Recently, we have had more success with FBX (Facebook targeting) and are seeing strong results and attribution,” he said. “With regards to public relations, again, we can look at spikes in booking same store, year over year for a property or campaign and try and match to key editorial content in the market. However, it is particularly difficult to gauge this and is a rough hypothesis, at best.” 
Multichannel analytic limitations
The challenge facing marketers is how to progress beyond the multichannel analytic limitations of aggregated data collection methods used by traditional Web analytics. 
Grover said there’s an opportunity to have a digital data collection framework that enables both business intelligence and advanced analytics. This methodology, he said, requires organizations to: 
  • collect data from Web, social and mobile app sessions (i.e. multi-domain visits);
  • accurately stitch together digital visits attributed to one visitor profile (whether anonymous or known); and
  • after authentication, accurately connect digital visits across devices, browsers and multiple domains attributed to one customer profile. 
Grover said marketers must rethink how they collect digital behavioral data. There is a need to source data that originates from the Web or from mobile apps out of traditional silos.
Algorithmic attribution
There are two forms of attribution models: business-rule defined models (first interaction, last interaction, etc.)  and algorithmic models. 
While the likes of last interaction or first interaction models aren’t often considered apt enough, Grover said algorithmic attribution improves credit allocation. 
He said attribution effectively recalculates existing key-performance-indicator metrics focused on efficiency—such as cost per action, which measures an advertiser’s per-conversion cost from start to finish in association with conversions—across interaction points. Thus, marketing leadership and analysts can gain insight into the value and cost of steps through a customer journey. 
“Attribution can dramatically reduce ineffective campaign tactics and improve the migration of high-value customers (and prospects) through the purchase funnel,” Grover said. 
Other than credit allocation, he said a couple other highlights of algorithmic attribution include:
  • Customer interactions by channel: By applying an analytical attribution model, marketers can understand the correlated influence of channels, such as how email affects online search. This allows organizations to build more integrated marketing efforts that complement and coordinate with one another. It also forces channel and/or departmental leadership to work together, rather than compete for the final form of attributed success or credit. 
  • Enables marketers to allocate spend more intelligently: Executives are instructing their teams to work toward optimizing advertising, communications and media budgets. Attribution provides data-driven evidence to focus on which activities are working. It helps marketers bid on the most effective search terms and place advertisements (including which creative type) on the right publisher sites that ultimately drive more valuable and relevant traffic back to their digital properties.
Steady progress The hotel industry features a variety of business models and organizational structures. For instance, Ueberroth said that because PHG partners with more than 650 hotels, the group’s business model is complex. 
“We are continuously running campaigns to drive business through our branded sites, which allows consumers to see a vast, curated collection of our independent hotels. At the same time, our member hotels are conducting their own direct-to-property efforts,” he said. 
“Generally, Preferred Hotel Group goes quite heavy in retargeting and, most recently, Facebook targeting. Fortunately, our hotels have really begun to embrace having a full spectrum of data details and attribution, so many allow us to pixel our site as well as theirs, so we have full view of consumer pathway,” he added.
The industry has still a long way to go, Grover said. 
He said marketers are asked to do what appears to be impossible: to analyze and value every interaction across the complex journey of beginning a campaign through actual purchases using sophisticated mathematics. The proliferation of data makes it difficult to integrate different types, detect patterns and correlations, and craft a holistic understanding of the customer. Organizations still store data in silos, making it challenging to map out the customer purchase path. As a result, it’s difficult to determine which data is valuable and insightful. 
“There is a reason why algorithmic attribution adoption is still low today. If it was easy, everyone would be doing it,” Grover said.

No Comments

  • Martin Soler August 7, 2014 10:30 PM

    I don't think the attribution model has become more complex, users always went through multiple sites. The difference now is that companies are paying on those multiple sites (meta-ads, AdWords, Facebook ads, Retargeting) so you have a single user costing you at every touch point hence costing $20 to acquire. First or last click models are on the way out, both are obsolete since they can't take into account the real cost of acquisition. Probably the best attribution model is an inverted bell curve with the first and last click having the highest value and all in between having less value, the middle one having the lowest. Then one can analyse the return per channel and cut off channels that don't serve a profitable purpose. Or at least the bid will dramatically reduce. But I'm not sure Google or any other platform is super keen on that since they may see a drop in revenue.

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