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Consumer awareness has been successfully measured for many years with traditional methods, such as surveys, panel studies or focus groups. Pulte Group, a leading business-to-consumer marketer and home builder, was looking for a means to gauge how often consumers would consider their brand relative to their competitors online, but with less expense than traditional survey methods and without self-reported bias. As a marketer that employs a truly cross-media strategy in various markets, Pulte turned to Organic for the measurement solution. Using website traffic tools, social media monitoring, search activity metrics, specific housing industry metrics, and SAS, Organic was able to reduce all of these online and social media vehicles into a single, trackable monthly index. On July 13, 2011, the IAB and leaders from Organic and SAS reviewed insights from this project and partnership. The speakers answered questions about challenges in understanding consumer behavior, analytics, and the wealth of data available from social media sources. The companies also shared their best practices in digital analytics in the following discussion: SAS: Digital Analytics - Best Practices 1. How can an organization bring together different forms of online data? 2. How to manage/scrub/modify the data to begin making sense of it? 3. Approaches to analyzing online data – including sentiment analysis, data mining, and forecasting Presenters: Vinicius Vivaldi Sr. Solutions Architect, SAS Institute Suneel Grover Solutions Architect, SAS Institute Jason Harper VP – Marketing Intelligence, Organic, Inc. John Bejnarowicz Senior Statistician, Organic
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IAB Webinar hosted by Organic and SAS
Navigating the Cross-Section of Consumer Insights,
Sentiment, & Online Behavior
Jason Harper – OrganicJohn Bejnarowicz – OrganicSuneel Grover – SASVinicius Vivaldi- SAS
Who is Pulte Group?
The Competition
© & ™or ® , Organic, Inc. Confidential. All rights reserved
Is their Marketing Working?
© & ™or ® , Organic, Inc. Confidential. All rights reserved
New Marketing Technique: COBI
Agenda• The Customer Journey• The Digital Landscape• Synthesizing Data Streams• Predicting Success• Lessons Learned• SAS®: Digital Analytics - Best Practices
The Customer JourneyLocation, Location, Location?
Location, Location, and a Lot More• Location
• Price Point
• School District
• Builder
Capturing Builder Consideration
Social Media MonitoringGoogle Insights for Search3rd party web activity
Purchase
Potential New Home Buyers
Upper Funnel (Location)
Middle(Builder
Consideration)
Lower(Prospect)
The Digital Landscape
Searches
Searches
SearchesGoogle Insights For Search
• Visits• Page Views
Website Traffic
• Compete• Alexa• comScore• Quantcast• Hitwise
Website Traffic
• Facebook• Twitter• Blogs
Social Media
• Meltwater• Sysomos• Blog Pulse
Social Media
Synthesizing Data StreamsOrganic’s Consideration of Brand Index (COBI)
COBIAre consumers talking about your brand/company online or virtually?
Are consumers searching for your brand/company online?
COBI is a single measure that captures a brand’s “share of consumer mind” online.
Are consumers visiting your website?
Measures from a range of “virtual” touchpoints give a better measure of overall brand share-of-mind than any single measure.• Direct consumer brand interaction (Website visits, page views)
• Indirect consumer brand interaction (natural Google searches)
• Active consumer participation in brand reputation (social media mentions)
• New home orders were included to give the index a real-world industry metric to combine with the virtual metrics.
Website Visits
Website Page Views
Google Search Index
Social Mentions
New Home Orders*
Positive Social Media Mentions
Multiple Virtual Metrics are Combined to Create an Index
COBI
Data Reduction: the object is to take multiple variables and condense their information into a lesser number of variables while still capturing the monthly swings in the data.
• Principal Components
• Multi Dimensional Scaling
• Factor Analysis
Statistical methodology behind Organic’s COBI is Principal Factor Analysis.
Bringing Multiple Metrics Down to One
1. Determine competitor set
2. Collect data for all competitors from four sources• Web traffic• Social media• Google searches• Builder New Orders (SEC filings)
4. Using shares figures, perform Principal Factor Analysis to generate scoring coefficients for a single factor.
5. Scale the resulting factor scores to range from 0-100
3. Create monthly shares for the data
The COBI Process
Month Builder Site Visits Page Views Google
Index
Total Social
Mentions
Positive Social
Mentions
Net New Sign-ups
(quarterly)
March-10 Beazer 106,893 576,766 9 1,167 196 728 March-10 Centex 61,873 554,456 18 2,491 228 1,823 March-10 DRHorton 145,173 1,580,796 19 931 121 4,037 March-10 Hovnanian 83,707 752,977 8 430 37 961 March-10 KB 110,482 1,194,538 14 6,925 458 1,446 March-10 Lennar 166,992 1,298,694 18 6,540 835 2,652 March-10 Meritage 34,406 353,341 7 232 212 621 March-10 NVR 9,678 38,113 20 348 38 2,000 March-10 Pulte 109,500 525,906 17 2,960 344 1,925 March-10 Richmond 67,327 434,888 6 327 287 637 March-10 Ryland 78,951 395,765 16 802 209 969 March-10 Shea 69,925 755,982 5 427 82 600 March-10 Std_Pacific 45,328 310,553 6 327 32 554 March-10 Taylor 343,610 1,594,489 5 382 68 1,034 March-10 Tollbros 80,750 1,055,578 14 1,551 176 526
Collect Data
How is the Score calculated? – In practice
• Website Visits• Website Page Views• Social Mentions• Positive Social Mentions• New Orders
Month Builder
Monthly Share of
Visits
Monthly Share of
Views Google
Index
Monthly Share of
Social Mentions
Monthly Share of Positive
Social Mentions
Quarterly Share of
Net New Sign-ups
March-10 Beazer 7% 5% 9 5% 6% 4%March-10 Centex 4% 5% 18 10% 7% 9%March-10 DRHorton 10% 14% 19 4% 4% 20%March-10 Hovnanian 6% 7% 8 2% 1% 5%March-10 KB 7% 10% 14 27% 14% 7%March-10 Lennar 11% 11% 18 25% 25% 13%March-10 Meritage 2% 3% 7 1% 6% 3%March-10 NVR 1% 0% 20 1% 1% 10%March-10 Pulte 7% 5% 17 11% 10% 9%March-10 Richmond 4% 4% 6 1% 9% 3%March-10 Ryland 5% 3% 16 3% 6% 5%March-10 Shea 5% 7% 5 2% 2% 3%March-10 Std_Pacific 3% 3% 6 1% 1% 3%March-10 Taylor 23% 14% 5 1% 2% 5%March-10 Tollbros 5% 9% 14 6% 5% 3%
Sums to 100% each month
Calculate “Monthly Shares” for:
How is the Score calculated? – In practice
Utilize Principal Factor Analysis to create scoring coefficients, then input the data values into the resulting equation:
COBI Score = 27.7 + 81.5 * (Share of Visits) + 69.5 * (Share of Page Views)+0.265 * (Google Search Index) + 44.8 * (Share of Total Social Mentions)+25.3 * (Share of Positive Social Mentions) + 47.2 * (Share of Net New Sign ups)
How is the Score calculated? – In practice
Pulte COBI
Predicting Success…For the Competition Too?
…and thus, we now have a way to track how our competitors are doing as well.
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
11,000
12,000
13,000
50
60
70
80Jan
-08
Feb-0
8
Mar
-08
Apr-0
8
May
-08
Jun-08
Jul-08
Aug-0
8
Sep-0
8
Oct -0
8
Nov-0
8
Dec-0
8
Jan-09
Feb-0
9
Mar
-09
Apr-0
9
May
-09
Jun-09
Jul-09
Aug-0
9
Sep-0
9
Oct-0
9
Nov-0
9
Dec-0
9
Jan-10
Feb-1
0
Mar
-10
Cons
idera
tion
Scor
e
Pulte
Prospects
Correlation = 88%
COBI was found to closely track with Pulte’s “Prospect” sign-ups
Lessons LearnedNot All Press is Good Press
Fairly well
Social media has been a strength for one top competitor in the past year, and other measures are showing similar signs.
One top competitor is increasing investments in social media presence… how is that strategy working for them?
Not much
The sweepstakes created a short-term bump in web traffic that disappeared when the sweepstakes did. No other digital metrics improved.
A competitor is running a “free home” sweepstakes… how has that tactic increased their consideration with consumers?
Yes.
This competitors scores are lower than their market share might dictate, even though there is no direct way in the COBI score that measures “bad press”.
A competitor has been receiving “bad press” in some markets… does this show up in lower consideration?
SAS®: Digital Analytics –Best Practices
Unstructured Data Sources
Data Management
Capturing & Storing Unstructured Data• How to collect the data?
• Leveraging Application Programming Interfaces (APIs) and RSS feeds• Ability to crawl the Internet
• Storing the data• Capability of storing the data for
both short and long term views• Accessing databases
• Native access engines vs. ODBC connections
Data Cleansing• Unstructured data, in the form of text, when captured, presents its
own level of data management challenges– Being able to correctly structure the data and clean it is a priority
– Technology needs to have the ability to: » Eliminate irrelevant information
» Quantity ≠ Quality
• Miss-spelings
• Treat acronyms and abbreviations (e.g. “LOL”)
• Pr☺f@nity
• *Punctuation*
Sentiment Analysis• The action of identifying the expressed sentiments by customers, partners,
suppliers and employees• Typically categorized into three levels
– Polarity indicator: Positive, negative, neutral
• Why is it important to measure sentiment?• Public perception of brand, product, and/or service
• Traditional Methodologies• Statistical• Rules-based• Traditional methodologies typically use one or
the other– Common issues with measuring polarity accurately– Hybrid approach advantages
• Overall vs. granular/feature-level sentiment
Good, but a little outdated. I bought the Nikon Coolpix L10 as my first digital compact P&S camera. I had it for a couple of weeks, until mine had a 'lens error' that basically made the camera inoperable (it was stuck open). It might've been due to batteries running low, but I tried another set (which I now think was also low).
The picture quality from the L10 was very good, a bit of barrel distortion was noticed in the wide angle and shooting tall skyscrapers (noticed by the curve along the side of the frame where the buildings are supposed to be straight).Another gripe I had with the camera was how slow the auto-focus was. It would basically go through the whole range of focus every time I pressed the shutter half-way and then some. This became more annoying the more I used it.
Eventually a lot of my pictures came out blurry, including outdoor overcast days with 3x optical zoom. Basically anytime there's zoom & less than ideal lighting, I would have to have rock steady hands to get non-blurry pictures. Overall it's a good camera if you can overlook the issues I mentioned.
Product: Nikon Coolpix L10, Polarity: mixedFeature: Picture Quality, Polarity: positive
Feature: Autofocus, Polarity: negative
Overall vs. Granular/Feature-level Sentiment
Unstructured Data Mining & Forecasting• How does an organization proactively identify new topics, new terms, and
new information being generated by the consumer?• Unstructured data mining
– Let the data speak for itself– Develop an early warning or indication system– Raise awareness of forthcoming topic trends
• Forecasting can help• Predict a topic reaching a significant threshold and proactively act on this
information– Marketing applications
» Defend and manage against brand-inhibiting events
» Understand and act with intelligence during a new product/service launch
» Augment Net Promoter Score strategies
For More Information…
• Jason Harper – Organic• [email protected]
• John Bejnarowicz – Organic• [email protected]
• Vinicius Vivaldi (SAS®)• [email protected]
• Suneel Grover (SAS®)• [email protected]
Thank You.
Questions
• Please type your questions into the chat feature on the upper-right corner of your screen.
Upcoming Member Events• Educational Webinars
– Compliance with IAB’s New Member Code of Conduct, July 27th @12 Noon EST
• Professional Development Classes– Essentials of the Digital Marketing Ecosystem, August 4th, NYC – Professional Presentations: Turn Information into a Story That
Sells, August 9th, NYC – On-demand training classes also available @ iab.net
• Conferences– Mobile: IAB Marketplace, July 18, NYC– MIXX Conference, Expo, & Awards, October 3-4, NYC– Ad Operations Summit, November 7, NYC