TheoryLane Case Studies
Cloud Vision AI and Dynamic Digital Campaign Performance Analytics
A Durable Goods Manufacturing Client’s In-house dynamic digital marketing created large amount of data. The difficultly in managing the data volume limited their ability to effectively use media and consumer behavior to better understand buyer personas.
In a traditional digital marketing campaign, only a few asset variations are used. (An “Asset” being the combination of verbiage, imagery, coloring, products, and other attributes of a digital display advertisement.) Alternatively, a Dynamic Digital Marketing Campaign involves multiple variations of verbiage, imagery, colors, products, etc. The combinations quickly become difficult to manage; for example, five text variations, with five different images, in five colors create 5*5*5 or 125 unique creative assets.
In the case of this client, they had over 2,000 unique assets for a single campaign; therefore, the more traditional data management techniques of manually assigning attributes to each asset was too work intensive. (“Attributes” such as product, size, general color schema, mood, number of people, text, etc.)
An automated image tagging routine was developed in Google Cloud Platform consisting of uploading thousands of assets to a GCP Cloud Storage Bucket, using GCP Cloud Vision API to assign the image attributes, then loading the Cloud Vision API output into a BigQuery table.
The performance of each asset variation was also loaded into a BigQuery table.
The performance and cloud vision output were matched, and reporting tables constructed.
Analytics tools such as Looker and PowerBI were connected to the matched reporting tables.
Google Cloud Vision API benefited the client on several levels. Primarily, they were able to understand which imagery and language resulted in better performance.
Additionally, the assets were assigned attributes in seconds and in a consistent manner. If the attribute assignment had been performed manually, we may expect a week of effort and with inconsistent attributes due to human error.
Third Party Audit Management App
“I want to grow my business, but all I ever seem to do is clean up these reports!”
It’s a common problem for any business, but in this case of this client, their business is managing highly skilled auditors who provide 3rd party non-financial statement assurance services – often required by banks and insurance agencies.
Each auditor wants to submit their audit report in a different format – leading to significant report formatting effort for the client.
TheoryLane developed a high-level information flow diagram to help align technical terminology with business processes.
During TheoryLane facilitated Round table discussions on how digital information moved between the agents in an audit, an opportunity was identified.
The application would then produce a pre-formatted report based on the audit information to develop an application which auditors could use to input audit notes, assessment values, and relevant evidence.
Additionally, financial models were constructed. A cost-benefit analysis was created to determine value of expanding application versus additional human resources. A value-proposition and valuation model revealed potential external market viability.
TheoryLane produced a delivery plan for Proof of Concept application and provided development resources to successfully delivery the proof-of-concept application.
Proof of Concept was sufficient to create standardization in smaller audits, freeing up client time to pursue additional business, and even rolling Proof-of-Concept app into potential start-up.
Compliance NLP Innovation Roadmap
“We’ve automated a manual process; can we do something with all this data moving around?”
When a business moves its data environment from spreadsheet and emails to web apps and cloud storage, a remarkable thing happens – the data is now accessible by more than one person!
In this case study, the process of insurance contract, claim, and coverage reconciliation was moved to an online application. In the application, file upload and document reconciliation were still performed manually.
*Specific use cases not shown due to proprietary nature
In uncertainty there is opportunity, TheoryLane performed innovation roadmapping and associated research recommendations on enhancements to the client’s data environment and data processing.
Using our established method of communicating complex business processes in simple language, TheoryLane identified several opportunities to apply advanced ML / AI solutions without disruption of existing processes.
Following the steps outlined in their roadmap, using the recommended technology, they were able to implement business specific applications of NLP functions such as term similarity and ID similarity matching within their next development iteration.
To provide a more informed perspective for financial decision making, TheoryLane developed make-vs-buy and cost benefit analysis on Cloud PaaS and SaaS offerings to determine which analytics services to purchase ‘off the shelf’ and what functionality should be developed ‘in-house’ to protect their competitive advantage. (i.e. differentiate the firm from all the other competitor using the same SaaS product.)
Combination of Cloud PaaS components with proprietary operations was determined to be the best balance between strategic capability and 3rd party managed affordability.
For example, the diagram below is a simplified example of how Data Scientists may perform data transformations and model development within a managed environment, while still retaining the flexibility (and ownership) of code developed within said environment.