Visibility was the name of the game for this optical retail chain. In a cookieless world, it needed detailed and real-time insight into its appointment advertising and in-store eye test availability to mitigate inefficiencies or, worse still, prevent lost custom. We worked with the client over an 18-month period to evolve its marketing analytics and bidding capabilities to deliver ROI for media investments while leaving a legacy of future-proofed data foundations across the company.
The Model
Our AI-driven Marketing Maturity Model is deployed to assess client's capabilities, provide a bespoke roadmap and implement proven solutions to elevate their data and technology maturity. Our proprietary model is based on 100+ of Acceleration’s completed Cloud projects across six vectors and 30 proven use cases, enabled by AI. Each use case delivers incremental gains for rapid short-term ROI. Together, all the use-cases form a scalable, holistic maturity model that creates sort-term results and long-term value. This case study showcases the results generated for an optical retail chain across key use cases, demonstrated and framed against our new AI-based maturity framework.
Background & Goals
Our client is a multinational optical retail chain. For a company synonymous with sight, it was lacking the visibility and knowledge required to achieve optimization in real-time and at a local level.
This was critical because of the way in which consumers book eye tests and the need to avoid offering appointments at stores when there is no availability. It was also facing a cookieless future with restrictions on third party data making it difficult to track how media performs and harder to budget and optimize media buying as a result.
It was a situation that required hitting four objectives if it was to be successfully resolved. The brand needed:
- A new analytic concept for digital attribution that eliminates the need for cookies
- To measure and analyze digital marketing activities in real-time
- The ability to evaluate investments and allocate and optimal spend to media buying platforms
- To increase appointment bookings & minimize customer drop-off
Discover & Assess
We knew from our extensive MMM and consumer profiling work with this particular client that it was critical to obtain granular detail across all advertising platforms to effectively evaluate buying strategy. This needed to be done on a day-to-day basis because that’s how it needed to optimize its campaigns related to booking eye tests.
Our discovery process informed us that platforms were over attributing themselves. Addressing this meant custom attribution modeling was key in terms of learning to trust the data that was being generated. We also needed to create a model that made sense from a human behavior perspective and combine real-world daily insights to provide one single source of truth.
Future Mapping & Priorities
What followed was an iterative process with the brand over 18 months to build its marketing analytics capabilities in a way it was not previously able to do because it lacked the visibility at the required frequency and volume to make the correct decisions. Initial modeling based on daily-insight data showcased good results but needed improvements in accuracy.
We spent time testing and validating against platform data and other attribution models and previous MMM results, while experimenting with Google Cloud and setting up schedulers that allowed flows to run automatically. It was also important to evolve the brand’s capabilities in AI and ML to provide realistic expectations, delivery capabilities and potential results across the company’s entire estate.
Implementing Change
We designed and built a number a tailor-made solutions including:
- Paradigm: A digital attribution and optimization tool running in Google Cloud which works completely independently from cookies. Insights on media performance are implemented into a model which receives new knowledge through data for media channels, which is compared to the number of booked eye tests. It creates automated and logical results as well as pacing forecasting recommendations for a media mix.
- Capacity algorithm: Automated algorithmic media buying was hosted in Google Cloud and used to optimize campaign budgets at a daily and local level. It shifts budget around between the stores’ geo zones on Facebook, Search and Programmatic buying to drive footfall to locations with available appointment slots.
- Simulation dashboard: We developed a tailor-made interactive simulation dashboard giving the brand a platform from which to model and simulate how it wants to spend its media budgets on a daily level irrespective of platform.
Short-term success and Long-term legacy
Our modeling work has proved a clear uplift in performance and delivered real business value.
- 16% lower cost per acquisition - This is equivalent to an annual saving in media investment of 3,100,00 DKK
- 15% more eye tests across the store portfolio achieved with the same budget compared to existing strategies
The brand can now optimize digital media investments based on millions of live dynamic data signals and impressions completely without relying on cookies and IDs. It ensures a future-proof data foundation and holistic budget allocation and pacing across the company.
The brand was also so impressed with the work that it has since developed an internal source of truth track to address silos elsewhere in the business. Today, real-time and dynamic is the only way for this company, which has changed its regular MMM to being a quarterly process - up from annually - and it also intends to move all its BI solutions into a single MMM source of truth dashboard.
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