For many loyalty marketers, making the right choices based on their respective budgets goes a long way toward achieving brand loyalty.
According to a new study from Visual IQ titled, “Bring Algorithmic Attribution Up to Speed: Accelerate Your Attribution Insights for More Effective Digital Channel Optimization,” today’s marketers are using algorithmic attribution to accurately track the increasingly complicated customer journey and optimize cross channel spend, yet currently cannot do so at the speed they would like.
The study, which was conducted by Forrester Consulting, explores the state of the marketing attribution market place.
A multi-dimensional algorithmic modeling approach to attribution, the study notes, “provides granular cross channel insights and data-driven optimization recommendations, leading to better performance and higher return on marketing spend.”
Based on in-depth surveys with more than 50 U.S. and U.K. marketing professionals responsible for digital channel optimization at their organization and utilizing algorithmic attribution models, the study finds that algorithmic attribution is essential for marketers to optimize media investments.
In fact, 75% of respondents indicated that they use algorithmic attribution informed insights to develop a more precise reach strategy, and 71% use those insights to target the right audiences more effectively. What’s more, 67% of marketers use these insights to adjust media spend across channels, while 63% use them to adjust tactics within individual channels.
While the benefits of attribution are vast, marketers must be agile and able to react quickly to changes in market conditions and keep pace with competitors’ ever-changing strategies. The study reveals that being able to quickly optimize marketing channels on a daily basis is important or very important for many marketers (58%), yet only one-third indicates they can do so today.
Marketers indicate, the study shows, that inflexible marketing and media budgets (41%), data consolidation challenges (37%), and partner and/or agency contracts that limit budget changes across channels and campaigns (29%) are key inhibitors. What’s more, 29% of marketers that use algorithmic attribution don’t receive fresh data fast enough to optimize on the most current results.
Here are some other key study findings:
Algorithmic attribution has quantifiable benefits. Three-quarters of marketers experience improvements in customer targeting and brand engagement from utilizing algorithmic attribution, with 80% reporting that they reach the right customers and 74% saying they serve more effective creative to customers at the right time.
Speedy algorithmic attribution is critical to achieving top marketing goals. The top goals defined by marketing organizations are improving profitability (43%), targeting more effectively (43%) and increasing brand engagement (39%). Yet marketers that cling to traditional media buying and measurement practices, or use attribution technologies that are unable to update models and insights on a daily basis risk falling behind the competition.
Daily optimization is critical during key time periods. The rebuilding of attribution models on a daily basis – based on the most recent marketing performance data available and enabling real-time optimization informed by this data – is vital during critical time periods, including when competition is live in the market (55%), during the start of a campaign (53%) and during key sale seasons (51%).
“Marketers are challenged to make the most of media budgets, and legacy measurement solutions that don’t accurately allocate credit between touch points and channels or identify the right audiences to target are no longer an option,” said Manu Mathew, co-founder and CEO of Visual IQ. “The results of this study confirm the major role that algorithmic attribution plays in helping marketers make the right media investment decisions, yet there is clearly an appetite for near real-time updates and daily optimization. Getting there means choosing technologies that support a daily optimization cadence.”