Turning personalization from a series of labor-intensive, one-off projects into business as usual - at scale and across all customer touch points, including offline touch points. And yes, it is as complicated as it sounds.
The media sales division of this entertainment juggernaut wanted to go beyond its one-size-fits-all event sponsorship packages and offer advertisers the ability to send highly personalized marketing messages to event audiences.
This global sport apparel brand relaunched their web site and wanted to offer visitors a more personalized experience, starting with content and commerce.
This global sport apparel brand wanted to recognize and reward some of its most engaged customers and community members.
This global CPG company had traditionally relied heavily on mass media advertising and was making its initial foray into data-driven marketing. Ghostwrote an action guide to educate international marketing teams and to provide a common reference framework.
This startup soft launched their customer acquisition activities, and we identified relevant programmatic audiences to include in an initial "test and learn" phase.
Precisely quantified audience size and cost efficiency of site-based vs. audience-based media plan, leading to early adoption of programmatic media approach.
Merged and normalized several disjointed data sources in order to evaluate market potential and develop business cases (media product reach, partnership with a financial institution).
Sourced relevant data and conducted analysis to prioritize target audiences and to frame pricing strategy.
Organized large, disparate research datasets to provide a series of simple and visual cheat sheets for each of the key international markets.
Helped support the development of an anti-corruption social strategy for an independent candidate to the Presidency of the Republic of South Africa.
Using mobile app geolocation usage information, modeled conversion likelihood based on distance to physical store and helped prioritize reseller network expansion efforts.
Audited existing marketing capabilities and identified key gaps in the marketing technology stack to enable the implementation of a personalized messaging vehicle. Missing components included a Customer Data Platform (CDP), an Advertiser Data Store, a Channel Orchestration Tool, an Offer and Messaging Decisioning Tool, and more specifically a Mobile Wallet Marketing tool.
Conducted in-depth vendor evaluation to select a platform to securely and confidentially manage advertiser data (2nd party data) shared anonymously for targeting purposes.
Identified strengths, weaknesses and gaps in the current marketing technology stack in order to enable personalization at scale. Assessed challenges and trade-offs for using best of breed components in lieu of standard components of integrated marketing suite.
The challenge of implementing and operating a marketing technology stack in an emerging market was first and foremost economic. With much lower absolute revenue per customer, priority had to be given to tools that were free or whose cost was not skyrocketing based on monthly user volume.
(Pre-sales consulting)
This theme park company introduced wristbands to simplify access and payment and improve guest experience. The challenge was to turn the large incremental stream of raw time- and location-stamped user event data into smaller, meaningful user profile information.
This leading data and information provider wanted to move beyond glorified brochureware and implement a more effective, more personalized online lead generation system.
This mobile payment startup was struggling to get adoption and repeat usage past the initial installation. Through a combination of user analytics, user session recordings, and A/B testing, it became clear that the app onboarding experience needed to be improved.
This wine e-commerce startup was trying to optimize its customer acquisition costs. An audit revealed conversion funnel improvement opportunities by streamlining and forking the checkout flow.
Operationalized the personalization strategy: identified data and technology dependencies, created templates for detailed specification and prioritization, produced specification documentation, and sequenced deployment based on expected impact.
Implemented several personalization campaign experiments at medium scale to stress-test technical and organizational readiness.
As part of the initial "test and learn" phase of the launch, several marketing experiments were deployed and iterated to assess the relative impact of various components of the product offering and messaging.
Deployed several data-driven marketing campaigns to leverage newly collected customer data points.
Deployed marketing experiments to establish best practices for media spend, media placement and formats, offer type, visual treatment, and copy variations.
Developed forecast model and operationalized data preparation flow to provide daily estimates of future audience size for a given advertiser and a given set of audience targeting criteria.
Defined and implemented customer data architecture and data collection requirements, with quarterly revisions and additions.
Architected and implemented customer data infrastructure, on top of which we later built a more holistic business intelligence system.
Prioritized data collection requirements in order to expand first party user profiles and improve targeting options.
Defined and implemented customer data architecture and data collection requirements, with quarterly revisions and additions.
Automated data acquisition and maintenance for large portions of a very extensive cross-media repository of inventory & pricing data.
To justify a larger overhaul of the marketing technology stack, developed custom code and data pipelines to run a small scale proof of concept of a personalized marketing platform.
Diagnosed several issues related to customer understanding and usage of product features. They were addressed with a more user-friendly, guided product onboarding screen flow.
Developed framework for evaluating success of test campaigns (common metrics vs. advertiser-specific metrics)
Developed distributed dashboards to provide daily estimates of future audience size for a given advertiser and a given set of audience targeting criteria.
Conducted feasibility and cost analysis for a predictive model that would collect new first-party research data to complement limited and skewed existing second- and third-party data.
Analyzed identity resolution performance across various vendors to prioritize sourcing of 3rd party data.
Implemented automation scripts and flows to augment user profile using 3rd party data sources. The goal was to enable audience targeting by wireless carrier.
Implemented full-fledged Business Intelligence (BI) system and reports on top of Marketing Intelligence backbone.
Conducted custom demographic and geographic analysis using Google Analytics data exports.
