THE ALGORITHM AGENT

TitleTHE ALGORITHM AGENT
BrandSTANDARD CHARTERED BANK
Product / ServiceSIMPLY CASH VISA CARD
CategoryC02. Use of Real-time Data
EntrantTBWA\HONG KONG, HONG KONG SAR
Idea Creation TBWA\HONG KONG, HONG KONG SAR
Media Placement CARAT Hong Kong, HONG KONG SAR
Media Placement 2 IPROSPECT Hong Kong, HONG KONG SAR
Production YAMANYAMO Hong Kong, HONG KONG SAR

Credits

Name Company Position
Jan Cho TBWA Hong Kong Managing Director
Jerome Ooi TBWA Hong Kong Executive Creative Director
Terence Ling TBWA Hong Kong Head of Strategy
Pauline Wong TBWA Hong Kong Director of Client Services
William Chow TBWA Hong Kong Group Creative Director
Mikey Batt TBWA Hong Kong Associate Creative Director
So So TBWA Hong Kong Associate Creative Director
Ronald Cheng TBWA Hong Kong Senior Copywriter
Ann Lo TBWA Hong Kong Art Director
Kenny Wang TBWA Hong Kong Art Director
Jan Lee TBWA Hong Kong Group Account Director
Elaine Kwan TBWA Hong Kong Senior Account Manager
Nicole Wong TBWA Hong Kong Strategist
Juno Kam TBWA Hong Kong Screen Producer
Karen Cheng TBWA Hong Kong Director of Social
Gerald Tam TBWA Hong Kong Social Strategist
Emily Ng TBWA Hong Kong Social Strategist
LaiWai Tse TBWA Hong Kong Social Content Creator
Harry Yiu TBWA Hong Kong Video Editor
Samantha Leung TBWA Hong Kong Account Manager
Jody Yu TBWA Hong Kong Account Executive
Hang Wong TBWA Hong Kong Production Manager
Joey Yeung TBWA Hong Kong Studio Manager
Kirsty Kwok iProspect Hong Kong Associate Digital Performance Director
Sasa Wai iProspect Hong Kong Associate Digital Performance Manager
Iris Lau iProspect Hong Kong Digital Performance Specialist
Ego Lau Carat Hong Kong General Manager
Jerry Cheung Carat Hong Kong Associate Business Director
Kay Tse Carat Hong Kong Planning Manager
Hana Wong Carat Hong Kong Senior Digital Manager

Why is this work relevant for Media?

This work shows how today’s media landscape presents a lucrative opportunity when the power of data, mass personalisation and full-funnel marketing are well-harnessed. The only saving grace of the Simply Cash Visa Card was that its cashback came without caveats. The ’Algorithm Agent’ made use of long-tail data to customise branded content according to personal interests in real-time. Hundreds of customised videos were generated to demonstrate the card’s versatility. Its data-informed full-funnel approach became a systematic engine for the business, achieving a 62% increase in applications at a 23% lower Cost Per Acquisition.

Background

WALLETS WERE GETTING FULL Over 20 million credit cards are circulating in Hong Kong - that’s an average of four cards for every adult. Such severe market saturation has stemmed market growth. By 2018, net acquisition of new cards slowed to 1.1%, a sixth of what it was in 2011. FIGHT AMONGST BANKS WAS GETTING UGLY As an outcome, banks use desperate tactics to win the next cardholder, such as using aggressive sales agents to intercept unsuspecting pedestrians at random. OUR PRODUCT WAS OUTCOMPETED FROM THE GET-GO Every bank was jumping on the cashback bandwagon. Competitors were offering four times more cashback compared to the 1.5% that Simply Cash Visa Card offered. We needed a radically different approach towards acquisition in spite of our product’s inherent disadvantages. BUSINESS OBJECTIVES: - Increase credit card sign-ups by 30%. - Reduce Cost Per Acquisition by 10%. (KPIs set by previous campaign benchmarks)

Describe the creative idea/insights (30% of vote)

Product insight: Less is more if it’s unconditional. Scrutiny into hundreds of terms behind common cashback offers revealed a maze of complex rules. Our card’s saving grace was that its cashback came without caveats. The proposition was ‘limitless cashback for limitless joy’. Data insight: One segment, 1,267 lifestyle interests. Millennials are thought to be into more or less the same things. Analysis of search, social and platform data revealed a long-tail of 1,267 spending-related interests that were searched 3 billion times each month. Channel insight: One magazine for every lifestyle is YouTube. Amongst our target audience, 4 in 5 use YouTube every week. They habitually window-shop and learn via trending content on the platform. IDEA: LIMITLESS TRENDING CONTENT CAN BE TURNED INTO ‘AGENTS’ TO SELL A LIMITLESS CASHBACK CARD. The ‘Algorithm Agent’ was a data-fuelled automated acquisition engine that captured customers using real-time customised content.

Describe the strategy (20% of vote)

Our target audience were ‘young urbanites’ aged 22 to 35. They have an average credit card spending of US$730 each month across a diverging set of lifestyle interests. 83% of these millennial shoppers turn to YouTube for reviews and product information before making purchase decisions. This segment believes they are overlooked by most traditional banks. Mileage-based credit cards fail to impress as they feel it takes too long to earn enough miles for a free flight. In contrast, cashback is favoured for its simplicity and versatility. With our product only having one clear competitive advantage – that it had no spending caveats – our strategy was to highlight its endless cashback-earning opportunities using the endless pieces of spending-related content available on YouTube. And beyond performing a mere demonstration, we designed a full-funnel marketing system that captured millennials as they were being enticed with such trending content.

Describe the execution (20% of vote)

TARGET This stage is about spotting audiences. Google implemented data-targeting by finding users as they were looking for content related to our interests. HOOK This stage is about ‘seducing’ audiences. When Director Mix spots a right audience, it ‘mixes’ a pre-roll ad based on the content sought after. Over 16Mil+ customised flock videos were generated. E.g. if a “10-minute make-up tutorial” was desired, it served this 6-second ad: “In a rush for a blush? Earn 1.5% cashback on that.” CLASSIFY Those who were served customised ads were retargeted with a 15-second tactical ad. As this was a skippable TrueView video, the pool of those who did not skip was classified as having product intent. CONVERT This pool was then converted through a programmatic approach. It drove traffic to the online sign-up form using offer-based display, search and re-marketing tactics.

List the results (30% of vote)

REACH & EFFICIENCY (KPIs set by Hong Kong finance category benchmarks) YouTube Pre-Roll Flock Ads (6sec) •KPI Goal: Reach 2M @ US$5.49 CPM •Result: Reached 2.6M @ US$4.85 CPM •Reached 86% of target universe on YouTube YouTube TrueView Ads (15sec) •KPI Goal: CPV No higher than US$0.038 •Result: CPV US$0.023 (40% lower than KPI) •Viewability Rate: 94% CONSIDERATION (KPIs set by previous campaign benchmarks) •KPI Goal: Improve product consideration by 25%. •Result: 40% lift during campaign period. •KPI Goal: Increase brand and product search by 20%. •Result: 156% lift during campaign period, more than 7 times higher than KPI. BUSINESS OBJECTIVES - CONVERSION (KPIs set by previous campaign benchmarks) •KPI Goal: Increase credit card sign-ups by 30%. •Result: 62% lift within 4 weeks, more than double the KPI. •KPI Goal: Reduce Cost Per Acquisition by 10%. •Result: CPA dropped 23% vs previous campaigns, more than double the KPI.

Describe the use of data, or how the data enhanced the campaign output

The ‘Algorithm Agent’ was dependent on a set of interest data deduced from multiple sources. #1: Identify the main interest categories. We used cluster analysis via Nielsen Clear Decisions to identify eight main categories of millennial interests that were related to spending. #2: Partner with Google for first-level data. Google was partnered for the scale of our data and technology needs. They provided first-level data on platform behaviour on YouTube based on main categories. #3: Combine analysis with social and keyword research data. We used keyword research and social monitoring tools to mine second-level data, including data on trending interests that had smaller volumes but higher growth. The final dataset was used for long-tail targeting. 1,267 spending-related interests that had: - 3 billion monthly YouTube searches - 7 million monthly social mentions - 78% reach into active 25-54 year old YouTube users within Hong Kong.