SCMP Signal, the First Publisher-Built Brand Suitability Tool
Overview of this campaign
Objective 1: Keep brands safe whilst on SCMP across all platforms from the first impression.
Current technology only starts to analyse content after a critical mass is reached, leaving brands exposed to unsafe content and most only work on Desktop and mobile content.
These tools only use negative keyword blocking (exclusion lists). These become generic lists and are unable to determine the difference between a ‘photoshoot’ and a shooting.
Signal was designed to work the moment the article is published and works across all platforms by using NLP to read Keywords, context and sentiment to determine the true nature of the article.
Objective 2: Drive client’s performance & reach during a campaign.
Exclusion lists limit a client’s access to key audiences by up to 40% as reported by Trinity mirror using IBM watson - this affects campaign optimisation.
We aim to extend the awareness of the average campaign by at least 70% and increase the average CTR by 40%
Objective 3: Understand more about our audiences.
To collect data and add it into our central customer data platform (CDP) 1PlusX and append it onto their unique ID.
This data could include:
Behavioral content consumption and intent
Observed sentiment preference
Reading preference by article reading complexity
Objective 4: Make the data easily available for usage.
The tool is built into our CMS so editors can use it to understand if the sentiment, reading level and more are what they expected.
So that the article is scored for brand safety/suitability as soon as it is published and that value is immediately passed to the Ad server.
Objective 5: Drive additional revenue for SCMP.
We want to see clients who are sensitive to brand safety issues start to spend with SCMP again and in a broader range of content verticals.
Results for this campaign
The build began in February 2020 and took four months to build. We launched to market in Signal in May 2020.
This build was a cross departmental effort including Data, Product, Technology and the Commercial team.
Three components were necessary 1) Sentiment analysis 2) Sensitive keywords tagging and 3) article readability analysis. Working together to provide a complex profile of the written articles. We employed all of these innovations, plus the use of a lexicon-analysis tool, called Valence Aware Dictionary for Sentiment Reasoning (VADER).
The chart below represents the reading level difficulty of the articles SCMP publishes every day:
Green - 5th -7th grade - Romance novels
Blue - 8th - 12th grade (Secondary School) - New York Times
Orange - College - Harvard Law Review
Red - College Graduate - Scientific Journals
As you can see below, over 50% of our content is written at a college level.
https://lh6.googleusercontent.com/h4Scan3piDw8nicIRS7I-S0wgXD88ToOyLxT9rhV-LCMLsifTM4sqdNP9ok4aruoJ75t0SW3kwJMPrFZChk3jxWXucfyTnP-tf-vOQQ2uhFb92WARrN5j5LL-efHlB2KXsw8f0QF" alt="" width="600" height="248" />
The y-axis represents the % of our articles each day published at various readability levels.
SCMP Signal is now used to ensure the safety of 100% of all direct booked deals.
Since launching we have successfully run SCMP Signal on more than 450 campaigns and been able to build the trust of several new sensitive brands including the likes of Cartier, HSBC etc. to spend with us with data led campaigns.
Providing best in class brand safety tools to our clients:
https://lh3.googleusercontent.com/6Bdu3rhQ0tYVQewG1Z5OA-cwktKJT_3_0qIEL1bLFwf6rOSfc66LDewV0XO1r3PIkAhzA7v-n-1FpGnDhVQ8vQul44mQEzF5WEhW5J2UEEuyVm6GoDfe6THMQREfoX-7M4BNzw_O" alt="" width="249" height="172" />
Campaign: Content Banners_ 2020
Client: SCMP Group Marketing & Events
Dates: 10th July 2020 - December 2020
55% increase in CTR & a 54% overall campaign uplift.