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Adswerve was recently honored to contribute to this Google webinar about Digital Maturity and share how we help our clients assess their existing capabilities. You can also access the webinar slides here. Before we dive into what we discussed, let’s shed some light on
Google’s Digital Maturity Framework
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An Intro to Google’s Digital Maturity Framework
Developed in partnership with Boston Consulting Group (BCG), Google’s benchmark is a diagnostic tool that informs where your business sits on the path to full data-driven marketing and attribution. Google and BCG identified four stages on the Digital Maturity scale, including:
- Nascent: Marketing campaigns mainly use external data and direct buys, with limited links to sales.
- Emerging: There is some use of owned data in automated buying with single-channel optimization and testing.
- Connected: Data integrated and activated across channels with demonstrated link to ROI or sales proxies.
- Multi-Moment: Dynamic execution across multiple channels, optimized toward individual customer business outcomes and transactions.
- Stakeholder Interviews: Gathering feedback throughout the entire organization; defining requirements and documenting gaps and successes
- Cross-Team Coordination: Defining what collaboration across business units looks like and understanding what data outputs are shared
- Audit Existing Collection: Reviewing existing collection implementation and analyzing historical data.
- Audit Existing Capabilities: Surfacing and reviewing descriptive statistics on data and labeling feature weights with Machine Learning (ML) processing.
- To uncover the quality of our client’s data, we apply unsupervised learning techniques to analyze highly cardinal/dimensional data sets.
- To determine if Google Analytics data is actionable and clean, we perform health checks and audits. You can do this, too, through our free Google Data Studio Report.
- To inspect Google Analytics activity and monitor dataLayer in real-time, we’ve built a free Data Layer Inspector+ Chrome Extension.
- To test capabilities, we clean and prepare data for use in prediction pipelines, assessing feature importance and weights, and then sharing the source code in a Python Notebook.
- BQ allows us to store unlimited amounts of data from different data sources we need for attribution purposes
- We have the ability to effortlessly process an unlimited amount of data with the power of SQL to create datasets for the attribution model
- We can use BQ ML right inside BQ to build ML model without even leaving the tool
- BQ represents a “one shop stop” where we can efficiently work with large amounts of data which saves a huge amount of time
Registration for this webinar is now closed. Check back soon for on-demand access.