This document discusses how marketers can use online listening and digital analytics to better understand consumers' online decision paths. It outlines how traditional listening through quantifiable data has expanded to include digital analytics and social media conversations. Marketers now have more data sources than ever to listen to consumers, but must take a strategic approach to make sense of it and answer the key question of "why". The document provides examples of how different industries like CPG, automotive and hospitality can apply online listening to gain insights into target audiences and inform cross-channel strategies.
Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure ...bo begole
This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the fieldwork, user interface, system components and functionality, and an evaluation of the Magitti prototype.
Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure ...bo begole
This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the fieldwork, user interface, system components and functionality, and an evaluation of the Magitti prototype.
SAS Big Data Forum - Transforming Big Data into Corporate GoldLouis Fernandes
Synopsis: How SAS believes organisations can turn Big Data in to competitive advantage through the use of High Performance Analytics.
In this presentations, we look at how SAS is seeing organisations take the outputs from big data analysis and turn them into tangible business outcomes through real-time decision-making.
In it, we explore:
- Why we believe organisations need to exploit their data assets to create the insights that build competitive advantage
- How to develop infrastructures required to support multi-dimentsional insight
- What SAS is doing to make this a reality
Key topics include:
- Data governance
- Big data infrastructure
- High performance analytics
- Data visualisation
About SAS:
- World’s largest, privately held software company,
- 35 years old
- Focus on advanced and predictive analytics right from the word go
- Big data has been in our DNA before it became mainstream
Using social media for market research and new product development: the case ...Merlien Institute
Using social media for market research and new product development: the case of Hallmark
Thomas Brailsford - Consumer Understanding & Insight Manager - Hallmark Cards
Evaluating the use of social media data as a research tool. Sharing what we learned and observed from a specific project: what worked and what didn’t. Highlighting paradigms of traditional research that are seriously challenged by the use of social media data.
Making the Leap from Market Research to Insight Part Three: Quantitative DataThom Pulliam
Article 3 of 3 about transforming research into insight. Published May 2012 in the Market Research Association’s Alert! magazine.
Part 1: The Four Places Insights Hide http://www.slideshare.net/tpulliam/alertmarketingresearchorg012012
Part 2: Qualitative Data
http://www.slideshare.net/tpulliam/marc-halertmarketingresearchinsightsqual
Social media Asia Pacific - Social Influence System is more than Social media marketing. Communicators know that social influence is changing how they do business and reach customers.
Zombie Social Media:
Social Media Marketing can be successful but often campaigns aren’t optimally executed because they’re:
Treated as one-off campaigns
Not aligned to comms goals and ROI is questionable
Not integrated into ‘regular’ workflows
Decision hierarchy murky
Infrastructure not built to support or amplify campaigns
- Audience media habits aren’t understood
Social media Asia Pacific
The New Normal: Predictive Power on the Front LinesInside Analysis
The Briefing Room with Mike Ferguson and Alteryx
Live Webcast on Feb. 12, 2013
Today's savvy organizations know that a streamlined approach to data and applications can put the power of predictive analytics right where it needs to be: in the hands of the user. Sure, training is still required, but a real revolution is underway for the graphic design of such user interfaces. Central to this overhaul of design is the concept of intelligent, simple workflow, which enables users to get things done in an orderly fashion.
Check out the slides for this episode of The Briefing Room to hear analyst Mike Ferguson of Intelligent Business Strategies as he explains why interface design and workflow must go hand-in-hand. He will be briefed by Matt Madden of Alteryx, who will tout his company’s predictive platform, a solution that leverages an array of traditional and Big Data analytics applications, designed for problem solvers and decision makers. Madden will also provide several customer use cases that demonstrate the new normal in predictive analytics.
The Customer Engagement Roadmap - The Key to Increasing the Value of Your Membership Base
Want to increase your subscription site’s profitability? The Customer Engagement Roadmap will show you how!
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SAS Big Data Forum - Transforming Big Data into Corporate GoldLouis Fernandes
Synopsis: How SAS believes organisations can turn Big Data in to competitive advantage through the use of High Performance Analytics.
In this presentations, we look at how SAS is seeing organisations take the outputs from big data analysis and turn them into tangible business outcomes through real-time decision-making.
In it, we explore:
- Why we believe organisations need to exploit their data assets to create the insights that build competitive advantage
- How to develop infrastructures required to support multi-dimentsional insight
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- World’s largest, privately held software company,
- 35 years old
- Focus on advanced and predictive analytics right from the word go
- Big data has been in our DNA before it became mainstream
Using social media for market research and new product development: the case ...Merlien Institute
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Article 3 of 3 about transforming research into insight. Published May 2012 in the Market Research Association’s Alert! magazine.
Part 1: The Four Places Insights Hide http://www.slideshare.net/tpulliam/alertmarketingresearchorg012012
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Zombie Social Media:
Social Media Marketing can be successful but often campaigns aren’t optimally executed because they’re:
Treated as one-off campaigns
Not aligned to comms goals and ROI is questionable
Not integrated into ‘regular’ workflows
Decision hierarchy murky
Infrastructure not built to support or amplify campaigns
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The Briefing Room with Mike Ferguson and Alteryx
Live Webcast on Feb. 12, 2013
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Check out the slides for this episode of The Briefing Room to hear analyst Mike Ferguson of Intelligent Business Strategies as he explains why interface design and workflow must go hand-in-hand. He will be briefed by Matt Madden of Alteryx, who will tout his company’s predictive platform, a solution that leverages an array of traditional and Big Data analytics applications, designed for problem solvers and decision makers. Madden will also provide several customer use cases that demonstrate the new normal in predictive analytics.
The Customer Engagement Roadmap - The Key to Increasing the Value of Your Membership Base
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Understanding the Consumer: Social Media Listening and Online Decision Paths
1. Understanding the Consumer:
Social Media Listening and
Online Decision Paths
CPG – Healthcare – Technology – Automotive
Travel - Financial Services – Call Center
2. When a man walks into a
room, he brings his whole
life with him. He has a
million reasons for being
anywhere, just ask him.
If you listen,
he'll tell you
how he got there.
3. “To listen well
is as powerful a
means of influence
as to talk well.”
– John Marshall,
Chief Justice of the United States
4. Direct Marketers
have always been
listening…
- To response rates - With creative testing
- To call center conversations – With research
- With audience segmentation
5. Traditional listening through quantifiable
data was done through traditional
analytics…
DATABASE
ANALYTICS
BEHAVIORS
PRIMARY
RESEARCH
ATTITUDES
7. Which required that we add digital
analytics to the mix…
DIGITAL
ANALYTICS
TRADITIONAL
DATABASE
ANALYTICS
BEHAVIORS
PRIMARY
RESEARCH
ATTITUDES
8. And then people began talking…
- 1 Billion Users on
- 460,000 new accounts per day on
- The average site visit on is 80 minutes
- 400,000,000 users on
Sources: Facebook, Twitter, RICG, Mashable
8
9. And listening became crucial to marketers….
“Listening is perhaps the most
essential neglected skill in
business. Part of the reason is
that it’s always been so hard.
But in the [new] era, listening is
easy. Not listening, on the other
hand, is criminal.”
- Li and Bernoff, Groundswell, 2008
10. And we had so much more to listen
to…
DIGITAL
ANALYTICS
TRADITIONAL
DATABASE
ANALYTICS
BEHAVIORS
“SPOKEN”
INTERACTIONS
SM
CONVERSATIONS
SEARCH
INTENTIONS PRIMARY
RESEARCH
ATTITUDES
11. and so much more to gain…
DIGITAL
ANALYTICS
DO
WHY
TRADITIONAL
DATABASE SAY
ANALYTICS
BEHAVIORS
“SPOKEN”
INTERACTIONS
THINK
SM
CONVERSATIONS
SEARCH
INTENTIONS PRIMARY
RESEARCH
ATTITUDES
21. Listening Outputs to Drive to the “Why”
Topic Group Trends Topic Trends Top Domains and Domain Focus
Topic Map Positive and Negative Language Twitter Drill-down
Momentum Drivers Consumer Language Share of Voice Polarity
21
22. By analyzing thousands of chat
sessions between a client sales team
and their resellers….
We were able to quantify the frustration
the partners felt in obtaining the info their
customers needed
And could justify the cost of online resources and
partner training to address those needs
24. While looking at 87,000 social media
conversations around dieting and
portion control for a CPG brand
We found the right emotional language to
connect with women
Which then guided our creative brief and
execution, differentiating the brand from a
crowded competitive landscape
25. Sentiment and Language Correlation
Low High
Volume, Positive Volume, Positive
Mean Sentiment
Sentiment Sentiment
Low High
Volume, Negativ Volume, Negativ
e Sentiment e Sentiment
Volume Index
26. And while creating win-probability data
models based on analysis of 25,000
inbound health insurance sales calls
We learned that the client was losing a high
percentage of sales to one specific
competitor
Resulting in customized scripting for calls in which
that competitor was mentioned
31. So if…
what people SAY
and what people DO
can be very different things,
we need to look at both.
32. What Customers Do
SITE-SIDE
ANALYTICS DIGITAL DO
PATHWAYS
WHY
BEHAVIOR BASED
DATABASE
ANALYTICS SAY
SPOKEN
INTERACTIONS
THINK
SOCIAL MEDIA
CONVERSATIONS
SEARCH
ATTITUDES
INTENTIONS
NEEDS
SURVEY BASED
33. Research Limits
Marketers have struggled to answer the questions around
decision making process, as sources have limits
Personal Primary
Focus Groups
Experience Research
NOT BASED ON ACTUAL OBSERVED BEHAVIOR
UNRELIABLE/HIGH LEVEL
SELF REPORTED
34. Rapid changes in technology, mobility
and connectivity have transformed the
decision making process
Latent Brand Awareness
Decide
Investigate
Buy
Moment Decision Predisposed
Set of Brands Absorb
of Truth to Act Consume
Integrate Advocate
35. “Getting information off the
Internet is like taking a drink from
a fire hydrant.”
- Mitchell Kapor, Founder of the Lotus Corporation
36. Decision Tracking
A SINGLE SOURCE CONSUMER VIEW COUPLED WITH PATHWAY AND
TARGETING ALGORITHMS RESULTING IN UNPRECEDENTED ABILITY TO
INFORM AND IMPLEMENT CROSS-CHANNEL STRATEGY
38. The Five “W’s” of Online Behavior
WHO: WHAT: WHERE: WHEN: WHY:
Customers and Clickstream EVERYWHERE! Actions Surrounding Insights and
Prospects behavior a Brand Interaction Implementation
39. “The Internet is really about highly
specialized information, highly
specialized targeting.”
- Eric Schmidt, Google
43. Chocolate Lovers, Holiday fast approaching
Who is a chocolate lover?
How many brands do they shop?
Is there anything else standing in the way of a
chocolate purchase?
How do I reach them online?
What makes this population unique?
44. Optimizing Reach with
Online Video
Sample Data for Model Build Custom Model & Campaign Target
Inputs Deploy Audience Audience
Identify chocolate lovers based Build model to find ideal Serve video ads to target
on their online behavior prospects based on input data audience
Brand
Loyalists Holiday
Buyers
Chocolate
Lovers
45. Defining Chocolate Lovers
Search Keywords Websites
Industry Brands
Chocolate Hershey hersheys.com
Milk Chocolate Dove Chocolate ghirardelli.com
Dark Chocolate Cadbury godiva.com
White Chocolate Godiva sees.com
Brownie Lindt dovechocolate.com
Brownies Lindor harryanddavid.com
Truffle Sharffen Berger fanniemay.com
Chocolate truffle Vosges dovechocolate.com
Cadbury Easter egg Edible arrangements lindtusa.com
Easter eggs Nestle ediblearrangements.com/fruit-
Easter bunnies Harry & David baskets.aspx
46. A Look at Age, Income and Gender
$160
A
v $140
e Brand A Loyalists
r Female Index: 132
a $120
g
e
$100
Holiday Buyers
I Female Index: 117
n $80
c
o Chocolate Lovers
$60 Female Index: 110
m
e
$40
44.0 45.0 46.0 47.0 48.0 49.0 50.0 51.0 52.0 53.0 54.0
Average Age
48. Food, gift and cooking sites see lifts
vs. previous week activity
Shopping
for
Chocolate
Begins
291% Food Shopping
52% Cooking
48% Food Products
4% Flowers
49. Results
Customer UP UP
Purchasing 13% Brand Favorability 13%
Direct / Loyalty UP UP
Purchasing 20% Sales Growth 10%
Online Ad UP Loyalty UP
Awareness 75% Membership 5%
51. Auto Manufacturer, New Vehicle Launch
Who is shopping the competitive set?
Do they shop multiple brands or only one?
Where do they shop for cars?
Where else do they go online?
What makes this population unique?
52. Who Is a Luxury Sedan
Buyer?
White Collar 65% Male
56% between 50-59
36% between 40-49
Apple Device Tennis
Athletic Home&Garden
College Graduate Running/Jogging
Military/Government College
Sailing Wine Appreciation
Do-It-Yourself
Jewelry Real Estate
Smart Phone Frequent Flier AMEX
Snow Skiing Satellite Radio
53. 57% visit 3 or less auto sites
within a 60 day period
Duration
30%
57% engaged
1-60 days
17%
10% 9%
7% 7% 6%
5%
3% 2% 2% 2% 1%
1 1-30 31-60 61-90 91-120 121-150 151-180 181-210 211-240 241-270 271-300 301-330 331-360
Day Engaged
Frequency
30%
60% visit
1-3 sites
18%
12% 13%
8%
6%
4% 3% 2% 2% 2%
1 2 3 4 5 6 7 8 9 10 11+
Number of Category Visits
54. Top online content implies a
well-connected male audience
Top 10 Domain Categories
News & Media - Sports 407
Blogging - Business & Finance 388
Automotive Dealers 377
Science & Technology 337
Wireless Manufacturers 336
Automotive Manufacturers 331
Government 329
Business Magazines 327
Travel 307
Golf 287
0 100 200 300 400 500
55. Newspaper sites top visits and time spent
Time on Site Index
Visitation Index *Sized by Percent of Visitors
56. Distinct Web Segments Result
Competitor Ready to Buy
Sedan 11%
Brand C
12%
Competitor
Sedan
Brand A
16%
Research Only
Competitor
39%
Sedan Brand B
22%
59. Hotel Guests
Where do my customers go to select a
property?
Do they go to third party travel sites, blogs, or
direct to the corporate site?
What other content do they consume online?
What makes this population unique?
60. Guest Online Journey
Appreciation for Everyday Living
Quality, Substance, Permeated by
and Style Technology
Hotel
Guests
Expert Travelers
Seeking Ideas that who look for
Matter Credible Peers
63. Networked Experiences
SHOP & CONSIDER PLAN & PREPARE STAY & ENJOY RE-LIVE & SHARE
Paid
search Retargeted Retargeted
EARNED
Display Display
Display media media
media
Partner
Email
Customer Hotel Blog
Service Property &
Reservations Guest Services
Sitelets
OWNED
SEO Hotel
Website
Hotel
Website Email
Mobile
3rd party WOM
BOUGHT
review 3rd party
Tumblr Twitter
review Facebook
Facebook You 3rd party
Tube Pinterest review
Instagram
Pinterest Foursquare
OBJECTIVE
REINFORCE OBTAIN KNOWLEDGE MAXIMIZE ON-PROPERTY DRIVE LOYALTY
DIFFERENTIATION OF PREFERENCES EXPERIENCE AND REFERRAL
64. Key Takeaways
Quantifiable
Remember Don’t Limit
Insights
the Listening to
Drive
Why Social Media
Action
It’s about the Today, target Cross-channel
approach, not the audiences can be strategy is easier to
defined by online implement today
technology
behavior than ever before
Different segments Remember
Online media buying
identified online can be
treated differently options continue to the
whether they come to be more targeted Why
your domain or not
65. In Closing
“whoever does not
recognize the
personal, individual
drama of man cannot
lead them.” [or sell to
them, for that matter]
- Anais Nin
Editor's Notes
A single source consumer view (web behaviors-cross category purchases-demographics-lifestyles)
Provide insights around key consumer audiences:Rewards Members (Loyalists)Chocolate Lovers at largeHoliday chocolate lovers…in terms of their Web Behaviors, Purchase Behaviors, and Demographics and Lifestyles in order to inform and optimize future Brand A efforts
When looking at a Brand’s Relationship with customers, it’s critical look through the lens of various segments. We gather extensive demographic, psychographic, behavioral, and attitudinal data about all of our survey respondents so we can isolate different customer groups of interest.
Step 1 – identify the target audience based on online behavior. Look for individuals who have searched on particular keywords – both product-type keyword words and brand-type keywords. Also identify individuals who have visited selected sites (own brand and competitor)
They have expensive interests – sailing, wine, new technologies. They’re highly educated, athletic/sporty, don’t mind getting their hands dirty in a DIY project, and are tech-forwardOnline Luxury Sedan Conquesters are: White Collar, male (65%/35%) Middle-aged:36% between 40-4956% between 50-59 Have higher HH incomes and net worth than control US Population (with income > $100K) Higher home value and land value, but not at the highest levels
30% of Luxury Sedan Conquesters make one search or visit to a competitive nameplate in 12 months 36% search or visit within a three month periodMEDIAN DURATION 83 DaysConsumers make multiple visits:44% visit or search competitive nameplates 2-5 times in a 12 month period
Prevalence of sports interests indicate an active audienceScience/technology and wireless point to a group that is well-informedBusiness blogging sites indicate this group is connected online
Software and Technology sites lead with top visits; News sites, automotive, and research/resource dominate the time spent within the top domains
Conversion = Likelihood to Locate a dealer Configure a vehicle Consider financing on competitor sitesConversion activity happens on the same day as the first activity. After 3 days the conversion rate drops