Hotel Group Case Study

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Hotel Group Case Study: Challenge

• A hotel group consisting of over 7,000 hotels worldwide sought to increase their online bookings via online display media.

• The company’s online marketing team had little insight into the effectiveness of their current display media campaigns while also lacking scale.

• To add volume, client added CPA line items to the media plan resulteing in CPA providers bombarding the remarketing pool, gaining credit for last view. The ratio of reservations to actualize also decreased. Ultimately increasing CPA, flattening sales and decreasing profitability.

• Seeking to improve the efficiency and effectiveness of its digital strategy, the company turned to [x+1], the leader in data driven marketing.

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Hotel Group Case Study: Solution

• [x+1]’s proprietary Predictive Optimization Engine, POE, identified audience attributes associated with a positive response to the client’s online advertising.

• These models then generated target audience profiles, forecasted audience size and value and optimized budget allocations to create a media buy targeting the online users who were most likely to book a hotel room.

• For each provider on the media plan [x+1] was able to measure unique contribution to reach overall and to remarketing reach. Allowing the client to reward actualized reservations and reach providers and eliminated remarketing for all but one partner.

• [x+1]’s sophisticated analytics approach produced the maximum ROI for each campaign, while integrating seamlessly into the client’s existing media workflow.

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Unique Reach & Frequency Analysis

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Reach & Frequency reporting includes:• Reach and frequency by publisher / section• Reach and frequency unique and overlap rates between publishers / sections• Reach and frequency unique and overlap rates between publishers / sections for remarketing

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Remarketing analysis

SiteReach

(Unique Users)

1 2-3 4-5 6-8 9-12 13+ SiteReach

(Unique Users)

1 2-3 4-5 6-8 9-12 13+

Yahoo! Finance 32,465,678 32% 13% 5% 2% 9% 39% Yahoo! Finance 6,493,136 16% 10% 10% 9% 18% 37%MSN Homepage 27,595,826 43% 26% 15% 9% 4% 2% MSN Homepage 5,519,165 19% 14% 14% 13% 15% 25%AOL Finance 23,456,452 27% 18% 11% 7% 17% 20% AOL Finance 4,691,290 12% 11% 11% 10% 22% 33%Exchange 21,037,089 52% 22% 9% 4% 7% 7% Exchange 4,207,418 26% 26% 16% 9% 9% 14%Weather.com 19,937,985 32% 21% 14% 9% 14% 11% Weather.com 3,987,597 11% 10% 10% 9% 24% 35%Casale Media 16,947,287 48% 21% 9% 4% 9% 10% Casale Media 3,389,457 6% 8% 10% 13% 24% 39%Audience Science 11,149,657 24% 21% 18% 15% 11% 11% Audience Science 2,229,931 4% 6% 9% 14% 20% 47%Yahoo! BT Segment 5,909,318 31% 20% 12% 8% 16% 13% Yahoo! BT Segment 1,181,864 12% 11% 11% 10% 16% 39%CNN 3,131,939 36% 11% 10% 17% 13% 13% CNN 626,388 13% 12% 12% 11% 13% 39%Forbes 1,659,928 42% 13% 11% 14% 8% 12% Forbes 331,986 15% 14% 14% 13% 22% 22%TOTAL CAMPAIGN 37% 19% 11% 7% 10% 16% TOTAL CAMPAIGN 15% 13% 11% 11% 18% 32%

OVERALL (EXCLUDING REMARKETING) FREQUENCY REPORT REMARKETING FREQUENCY REPORT% OF SITE IMPRESSIONS IN USER FREQUENCY

CATEGORY USER FREQUENCY CATEGORY

Report Uses• Assess the relative attribution credit of conversions by site.

• Assess the effectiveness of the site to generate conversions on a weighted basis.

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Converter Overlap

SiteAttributed

Conversions% Conversions

Exclusive to site% Imps for

Converters to site% Conversions

Attributed% Converter Impressions

% Campaign Impressions

IndexWeighted

ConversionsAttribution Weighting

Yahoo! Finance 154 40% 81% 19.1% 17.5% 25.5% 69% 141 92%MSN Homepage 138 35% 75% 17.1% 18.8% 16.3% 115% 152 110%AOL Finance 117 56% 68% 14.5% 9.5% 9.2% 103% 77 65%Exchange 105 32% 56% 13.0% 19.3% 11.2% 172% 156 148%Weather.com 100 80% 74% 12.3% 13.4% 10.5% 128% 108 109%Casale Media 85 45% 91% 10.5% 8.8% 9.3% 94% 71 84%Audience Science 56 12% 52% 6.9% 4.8% 11.9% 40% 39 70%Yahoo! BT Segment 30 45% 65% 3.7% 4.8% 4.8% 100% 39 131%CNN 16 12% 56% 1.9% 1.7% 0.8% 213% 14 88%Forbes 8 50% 72% 1.0% 1.4% 0.6% 233% 11 136%

Overall Post-Impression Converter Report

Report Uses• Assess the relative attribution credit of conversions by site.

• Assess the effectiveness of the site to generate conversions on a weighted basis.

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Precedent Analysis

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Cross-channel attribution report includes:• % of unique converters seen in each channel by conversion type• # of click and display events by channel leading to a conversion event• Drill downs by different timelags (1 day, 1 week, etc.) • Optional data transfer for more in-depth sequencing analysis• Predictive Analysis will be available in forthcoming weeks

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Hotel Group Case Study: Results

• In the first three months of full implementation, CPA decreased 69%.• Actualized reservations made online increased 42%.• In addition, the company gained valuable insights into the visitor

attributes driving conversion.