June 23-26, 2013
INFORMS Healthcare 2013
October 6–9, 2013
2013 INFORMS Annual Meeting
June 5-6, 2013
Customer Analytics Summit 2013
June 10-14, 2013
Predictive Analytics World
September 8-14, 2013
2013 ASE/IEEE International Conference on Big Data
“Data science begins with data. Nothing gets built without data. Data science continues with science. Accurate, persuasive and effective prediction requires patterns. The process of discovering that pattern is science. Any product worth building requires a reliable pattern to exist in the data.”
– Christopher Berry, co-founder and chief science officer of Authintic, in his article on recommendation engines in the current issue of Analytics.
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FICO will deliver its analytic-powered customer engagement services via the new FICO Analytic Cloud, for creating, customizing and deploying analytic-driven applications and services. Application developers, FICO clients and FICO partners will be able to take advantage of these services to rapidly create, execute and manage high-volume campaigns that engage customers in real-time with mass personalization across channels including brick-and-mortar, social and mobile.Read More
Special ArticlesStudy: Who can best manage ‘voice of the customer’?
Over the next three years, global organizations will make understanding and interacting with the customer their top priority. So says a new study from The Economist Intelligence Unit titled, “Voice of the customer: Whose job is it, anyway?” Yet only 56 percent of respondents to the survey, sponsored by SAS, believe their companies clearly understand the customer today.Read More
Marketing Analytics in Online Social Spaces
Analyst at a digital marketing agency outlines basic concepts and early experiences in an emerging field.
By M. Kevin Geraghty
A great marketing divide separates creative types and analytic types. An ad agency lives or dies by its creative work, and the creative director is usually the star. This is because creative can affect many aspects of a company’s brand and brand is what any company lives or dies by. In contrast, analytics has traditionally been seen as primarily part of a direct response model or at least focused on more mundane matters such as ROI (Return on Investment). The CEO would not think to ask the director of marketing analytics how the company’s reputation is holding up. At best, analytics would be used to design and evaluate the results of a focus group or survey. Marketing in on-line social spaces presents analytics with an opportunity to cross this great divide. There are three aspects of the social environment that create the opportunity:
- What was hidden is now visible. Conversations about brand preference, customer experience and other aspects of brand health are now regularly posted on blogs. A company no longer has to wonder what people think of them. They just need to listen.
- What was anecdotal is now accessible to automated capture. Online content may be downloaded and analyzed. Previously, market research relied on focus groups, surveys and clipping services.
- Analytic tools draw ever-deeper layers of meaning from data. Natural Language Processing and other technologies for handling large amounts of unstructured data are starting to render meaningful insights.
This article does not pretend to forecast what will happen in analytics for social marketing or the size of the opportunity that is unfolding. Instead, I will walk through some basic concepts and early experiences based on my role as head of analytics at 360i, an independent digital marketing agency based in Atlanta. The lack of agreed upon definitions and rapidly changing landscape means that I will miss much that is important. Even the term “social marketing” is sometimes used to mean marketing in online social media and sometimes used to mean marketing for social causes. What I can offer are those items and issues that are most in the mind of an analyst at a digital marketing agency. The foremost item is measurement.
Measuring Digital Assets
Understanding the performance of digital assets requires a different approach to traditional online media. The IAB (Interactive Advertising Bureau) has standards for rich media metrics and WAA (Web Analytics Association) has developed standards for Web analytics, but metrics for digital assets, such as widgets or games, are only recently finding agreed upon standards. For example, distribution and interaction are the two key areas of measurement for widgets. Distribution measures reflect the unique characteristic of this kind of media’s ability to “travel” though the Web. They include:
- Placements is a measure of the number of different “locations” that the widget has moved to. There are a number of variations on this theme, including unique placements, active placements and new placements.
- Grabs is the number of times that the widget has moved to a new placement. This is a measure of the action of creating a placement. It also has variations including grab rates and attempts.
- Transmission Rate is a key measure of virality, the ability of a widget to spread.
- Hubs is a measure of the domains that provide access to the widget for placement. Certain domains can be import distribution points for marketers to recognize and cultivate.
As a widget expands across the Web it creates a directed network that emanates from seed points. By contrast, interaction metrics are somewhat similar to more traditional Web analytics metrics including:
- Views — the number of times that a widget is viewed is generally measured as the number of times the widget is loaded. Since we don’t know the exact position of the widget on a page, this can lead to overstatement when the actual impression would require the user not just to load the page but also to page down.
- Clicks — the number of clicks a widget receives is a stronger measure of interaction.
- View Time — the amount of time the page the widget is on is viewed by the user.
- Interaction time — generally measured from some interaction start point, such as a mouse over, to the point at which the widget interactivity is terminated.
- Interaction details — most widgets offer the user a number of options for interaction. This is particularly true of multifunction and game widgets. Each action, as well as the ordering of all actions, provides insight into the appeal of the widget and changes that can be made to enhance that appeal.
- Ad supported metrics — widgets that carry ads must track interaction information about the widget itself and for any ad that the widget displays.
As with all metrics, the devil is in the details, and variances in the definition of these metrics can create significant differences in our understanding of a widget’s performance. For example, terminating an interaction metric on the last widget-based action will produce a smaller time value than terminating it when the user navigates away from the page hosting the widget. So measuring digital assets requires the kind of thoroughness and precision that analytics professionals are familiar with. Let’s look at a campaign that leveraged digital assets to maximum benefit.
Digital Word of Mouth
Digital word of mouth (DWOM) is defined by the Word-of-mouth Marketing Association as “giving people a reason to talk about your goods and services, and making it easier for that conversation to take place.” One of the most effective techniques in DWOM is to distribute content, in the form of digital assets, to key influencers among the population you are trying to reach.
One of the problems with content on the Web is that it is easily “borrowed,” and the benefit derived from that content goes to the “borrower” rather than the creators. Working with 360i, NBC found a way to deal with this issue for some of its premier content. NBC shared SNL (Saturday Night Live) clips with major blogs and media sites but wrapped that content in a portable video player, which could be embedded on sites outside of YouTube and other video sites. 360i developed a DWOM program to promote the clips on major blogs and online media outlets. In addition, 360i optimized the player for natural search.
Figure 1: The DWOM campaign drove SNL video views to record numbers and hit 3.1 million views following the season finale.
The result was that the DWOM campaign drove 77 percent of all SNL video interactions and 63 percent of daily unique users. SNL videos were featured on hundreds of top sites including Popcrunch, Defamer, Huffington Post, TV Squad and The New York Times. Video views reached almost 1 million on multiple days during the campaign and hit 3.1 million views following the season finale.
But digital assets are just one building block in effective social media marketing. The following section describes a more comprehensive social media strategy.
Figure 2: H&R Block implemented a holistic social strategy based on listening to their customers and delivering engaging, educational content.
Social Marketing for H&R Block
In 2007, H&R BLOCK engaged 360i to create a social media strategy for the 2008 tax season that would drive consideration for H&R Block’s online tax products and build up an online presence for the brand. The goals of the campaign were to change perception of H&R Block as simply “brick and mortar” to a digital brand with a strong online presence, increase awareness of H&R Block’s online tax products and build online communities that engage consumers with fun and educational information on taxes.
|What is an Online Influencer?
|1. An online influencer is a person whose opinion
matters to a sizable online audience, especially if it
is in a particular area of expertise. A blog such as
Fashionista can influence its readers to purchase
fashion products or also blog about them. In the case
of Twitter, an influencer would be someone with a large number of followers.
The campaign centered on creating a fun and educational online experience for consumers looking to find out more about taxes and H&R Block’s tax solutions. Working together, H&R Block and 360i developed portable assets that proved taxes could be fun and engaging, including games and quizzes, which were then spread across the Web for consumers to find and share with friends.
In addition, 360i created a robust social media strategy to distribute these assets and to support H&R Block’s fictional “brand evangelist,” Truman Greene. The social media program also included:
- creating a brand presence in social spaces;
- giving consumers a platform to interact with H&R Block on MySpace, Facebook and eHarmony;
- highlighting H&R Block’s digital products through interactive and sharable widgets; and
- starting conversations with online influencers  using Twitter.
Figure 3: Block of Fortune is a fun and engaging widget that provided helpful advice and information about taxes to consumers.
The effort raised awareness about H&R Block’s online tax programs that strongly positioned them as a digital brand with a strong Web presence. The campaign results:
- total brand awareness increased 52 percent;
- word-of-mouth awareness increased 55 percent;
- Internet advertising awareness increased 171 percent.
Most importantly, the social marketing strategy put H&R Block directly in touch with its target customer. Many consumers began to ask questions about the brand’s products directly on Facebook, MySpace and Twitter. In these fun and useful community-based environments, H&R Block became the go-to brand for knowledge about online tax preparation.
Online Brand Management
The more strategic approach has more strategic goals such as brand awareness and engagement. eMarketer estimates that U.S. advertisers will spend $4.7 billion on display ads and another $3.1 billion on other branding-oriented ads including rich media and video in 2009. Traditional brand measurements typically rely on a survey methodology but the Web offers alternatives.
Figure 4: Online listening takes on many forms. A simple one-question poll can be more effective than extensive surveys that tend to have significant selection bias.
At GMAC Insurance, I worked on a project for evaluating brand metrics explicitly from customer behavior. GMAC partnered with Insweb, an online Insurance agency that presented our offers together with competitive offers. By appropriately masking our competitors, Insweb was able to let us see when we won and lost competition for a customer and when the customer’s decision was based on price. We estimated that we could command a 7 percent average premium in the marketplace and up to 30 percent for customer segments with clear preference for the GMAC brand. In an industry that runs an underwriting profit margin of around 5 percent, a premium of 7 percent is a key competitive advantage.
Advanced Analytics Techniques
Automated techniques FOR deriving information from text data have come a long way in the past 10 years. However, most large advertisers still want to know that a human, preferably an expert in digital public relations rather than an intern, has read the blog posts as well. The major steps in mining text are: 1) information extraction, 2) analysis and 3) summarization.
Technorati and Google blog search provide broad search capability for the blogosphere. Most social networks allow varying degrees of access to data though developer APIs. Tools such as Radian 6 or Buzz Logic provide great listening capability and summarization in dash-boards and widgets with self-explanatory names such as River of News or Influence Viewer.
Figure 5: A Google blog search or Technorati gadget on your home page is a great way to keep track of your competitors, your executives, market sentiment or new developments in your area of expertise.
360i Advanced Analytics is also experimenting with NLTK, an open source Natural Language Processing toolkit for the Python programming language. Our first use of NLTK was to build out “long tail” keywords for Paid Search campaigns. First we tapped Web server logs and social media sources for prospective keywords. Promising keywords must be assigned CPCs (Cost per Click) that we are willing to pay in order to make a reasonable return. The Bayesian classifier in NLTK does a good job of matching prospective keywords with those in the existing Paid Search lists. The trick is to design effective feature extractors. These are small Python functions which identify features of the text such as contains a brand term or location specific.
Accurately assessing sentiment, even simple sentiment polarity such as “positive” or “negative” is more difficult than processing keyword lists. Slang, sarcasm and irony, remarkably creative spellings, and tortuous negations conspire to hide meaning. For example, a retailer may be interested in understanding various aspects of shopping behavior. Specific social shopping sites such as Kaboodle or Zebo do not have easy data extraction features, so we went to Twitter to see what people were doing as they shop. A simple extract from the Twitter API for the keyword “shopping” on Oct. 16, 2009, produced 1,090 tweets. Nineteen of these tweets contained the word “earn,” which is a feature of tweets from tweeters looking for marketers to earn money from home. After cleaning up the data set we start discovering useful tweets:
- “shopping via world wide web.”
- “At Target…I love shopping so early in the day…I got the store to myself!”
- “Happy Friday early release day Shopping at 1:30 – here I come”
And some less useful:
- “shopping jobs”
- “Can I go shopping nooow pleaaase”
Figure 6: A sample Radian 6 dashboard shows historic post counts and up to the minute tweets and video.
One of the nice things about the Twitter API is that the tweets can be tied to demographic information if the tweeter has provided it, so we can start to build a picture of which age groups, genders and geographies have distinct behaviors. Customer segments, sometimes represented by online persona descriptions, may closely tie to online behavior.
The Yahoo Buzz graph shows the build of search volume and the spike for Black Friday, the first shopping day after Thanksgiving.
Figure 7: Online Persona are a good way to summarize customer segments derived from demographic and online behavior in an accessible way for humans. The information provided by members of online social networks makes this straightforward.
Social networking sites tend to value themselves based on their membership rather than visits to a Web site. One of my first a ssignments in the social networking sphere was to develop models for monitoring and predicting the growth of what was to become a large (80 million member) social network.
The key metric for viral growth is transmission rate. When the transmission rate is greater than one for an active population, growth becomes exponential. We focused on refining the key touch-point for transmission: the invitation e-mail that an existing member sent to their friends to encourage them to join. Over the summer of 2005 this was consistently improved until, in September, we went viral. In the next three months we picked up as many active members as we had in the previous 12. Despite, or perhaps because of this, there was a consistent growth in engagement statistics such as time on site.
Figure 8: A quick check in on Sunday, Nov. 29 shows no instances of “earn” in 1,441 tweets. I guess marketers get Sundays off. Five percent of tweets indicated they were online and 2 percent offline on Nov. 29 versus 3 percent and 1 percent, respectively, on Friday, Oct. 16, which may tell us more about tweeting behavior than shopping behavior.
Where is Digital Marketing Analysis Going?
Digital marketing analytics has traditionally focused on monitoring Web site performance with tools such as Omniture Site Catalyst. Paid search has provided a fertile field for forecasting and optimization models focused on tight ROI goals. Marketing in social spaces requires rigorous and meaningful measurement but also opens up more diffuse challenges around brand health and consumer intent.
Figure 9: Virality depends primarily on transmission rate. Tweaking the campaign factors that affect transmission rate is the key to campaign success.