Case studies

optimizing digital marketing spend

Challenge: A leading digital media company was investing millions of dollars into acquiring users for its website and apps but was struggling to generate a positive ROI.  They were using many of the standard digital analytics tools to help make decisions on how to allocate marketing dollars but challenged with both the accuracy and timeliness of the data.

Solution: We conducted an audit of the digital marketing and analytics processes and capabilities to determine where and how to improve marketing ROI.  We developed and implemented a new process to determine the lifetime value of a newly acquired user based on a combination of predictive and heuristic factors.  We created monthly playbooks that the digital marketing team used to inform decisions about the channel (e.g., Facebook, YouTube, Google Search) and CPM/CPC allowables.

Impact:  We reduced overall spend by eliminating unprofitable digital marketing while increasing ROI by 20-40% with remaining marketing tactics.

personalizing digital experiences with first party data

Challenge:  A global consumer brand had access to over 100 terabytes of first part data from its consumers but wasn’t leveraging it to personalize their experience.  The lack of personalization drove measurably higher acquisition costs, lower retention, and reduced lifetime value.

Solution: We worked with the ecommerce, marketing, and technology teams to assess their current capabilities, define a personalization roadmap, determine the appropriate build vs. buy tradeoffs, and created a business case to advance their personalization agenda.  In addition to building a new cloud-based data platform to host a consumer 360, we helped lead the implementation of a new marketing cloud, A/B testing and personalization solution, and developed new marketing workflows to support the content needs for a highly personalized digital experience across website, mobile app, and email.

Impact:  We generated $5M in cost savings through improved operational efficiencies, contract terms, and reduced resource costs for personalizing communications at scale. 

Modernizing AI Tech Stack and Algorithms

Challenge: The company was an active user of data science to improve marketing performance and consumer experience on their websites, mobile apps, and digital marketing channels (e.g., email, social media).  However, over time the company’s capabilities had not kept up with the advancements in technology and analytics methods and were underperforming relative to expectations.

Solution: After assessing the maturity level of both the analytics group and the functions they supported (e.g., marketing, pricing, inventory planning), a prioritized set of initiatives were recommended and agreed upon by the senior executive team.  The first phase involved migrating their data platform to one of the leading cloud providers and deploying a portfolio of open-source analytic tools.  Once in place, a wholesale rebuild of all existing algorithms was undertaken leveraging the newly available machine learning solutions.  The initial set of new models were quickly evaluated and proved to both reduce costs while materially improving model performance.  Eventually, all existing models were upgraded and an ongoing, self-funded, model refresh cycle was put in place.

Impact:  We were able to reduce compute, data storage, and software licensing costs, producing over $0.5 M in annual savings net of the new cloud infrastructure costs.  The new machine learning based models increased profitability by 5% through higher response rates and cross-sell.