Predictive Marketing

Predict Market Success With Google Insights

We are awash in a sea of data. Thanks to web analytics tools, CRM systems, and social media, we have more data than ever about the behavior of customers and prospects. What is often lacking are the knowledge and skills necessary to turn this data into useful information.

Both are on display in a brilliant study conducted by Seth Stephens-Davidowitz of Harvard. As reported in the Wall Street Journal, using freely available data from Google Insights, skillful research, and clever thinking, he was able to determine that in the 2008 presidential election, racial attitudes reduced the number of votes garnered by President Obama by 3%-5%. His method of reaching this conclusion, which we’ll review here, represents techniques that can be used by all marketers in gaining insights into topics such as forecasting product demand, buying attitudes, geographical preferences, and buyer demographics.

Stephens-Davidowitz performed the study because of the notorious unreliability of surveys to capture the true racial attitudes of voters. Participants in surveys are highly likely to misreport their true attitudes due to embarrassment. Google-based measures of racial bias are more likely to accurately reflect voters’ attitudes, since they perform Google searches online while likely alone. In addition, information about Google searches is available at finer geographic levels, uses data that is more recent, and aggregates information from larger samples as compared to typical surveys.

The method used in the study was as follows:

  1. Choose a search term that represents the underlying attitude. In this case, Stephens-Davidowitz used a certain well know racial epithet that began with “n” for the representative search term.
  2. He had to make sure that the term represented a strong proxy for racial bias; he did this by:
    • Examining some of the output from Google Insights, which includes the top related search terms including the word. From the list of related terms, it was clear that the search was motivated by racial bias.
    • Verifying that Google search volumes correlate well with demographics one would more often expect to search the term. For example, the percent of a state’s residents who say they believe in God explains 65% of the variation of the search volume for the word “God”. The table below gives further examples:
    • Finally, the major potential bias with racial attitude survey data – misreporting due to embarrassment – is unlikely to significantly bias Google data. As mentioned previously, the conditions under which people search -online and likely alone – limit this concern. The following table documents substantial search volume for various terms that researchers suspect may be under-reported in surveys.
    • He then used Google Insights to determine the geographic variation in the use of this term in searches. Quite a wide variation was found by media market:
    • Markets with high racial bias have darker colors

  3. Stephens-Davidowitz next sought to arrive at an estimate of how this bias translated into votes. In order to do this, he arrived at a first estimate by comparing voting results by media market in the Obama – McCain election with results in the Kerry – Bush election using linear regression.
  4. To verify that his estimate of racial bias was a strong predictor of the difference in voting patterns between the two elections, he then added additional variables to his analysis that are known to affect voting outcomes.Stephens-Davidowitz concludes that

Estimating the effect of racial animus on voting is complicated by surveyed individuals’ propensity to misreport socially unacceptable attitudes. This paper sidesteps surveys using area-level Google search data and administrative voting records. I find that racial animus played a major role in the 2008 election. Relative to the attitudes of the most tolerant area, racial animus cost Obama 3 to 5 percentage points of national popular vote.

More details are offered in the full study, The Effects of Racial Animus on Voting: Evidence Using Google Search Data. The method described here can be used in a host of marketing applications, including forecasting product demand, buying attitudes, geographical preferences, and buyer demographics.

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A Vital New Marketing Metric: The Network Value of a Customer

New research is underscoring the influence of social networks in marketing. Researchers at Telenor, a mobile phone carrier in Scandanavia, developed a map of social connections based on calling patterns between subscribers to analyze the adoption of the iPhone since 2007. The research showed that an individual with just one iPhone-owning friend was three times more likely to own one themselves than someone whose friends had no iPhones. Individuals with two friends who had iPhones were more than five times as likely to have purchased an iPhone.

What is groundbreaking about this research is not the realization that friends and colleagues influence what you buy, but the unprecedented ability in today’s connected world to track, measure, and quantify the effects of social influence. This newfound capability calls for a dramatic overhaul of the way that businesses determine the value of their customers.

Time evolution of the iPhone adoption network. One node represents one subscriber. Node color: represents iPhone model: red=2G, green=iPhone 3G, yellow=3GS. Node size, link width, and node shape (attributes which are visible in Q3 2007) represent, respectively, internet volume, weighted sum of SMS and voice traffic, and subscription type. Round node shape represents business users, while square represents consumers. Source: Product Adoption Networks and Their Growth in a Large Mobile Phone Network (http://www.sundsoy.com/asonam_product_spreading.pdf)

The Lifetime Value of a Customer

Traditionally, determining the lifetime value of a customer has long been the starting point for calculating  the ROI of a marketing campaign. The lifetime value of a customer is defined as the net present value of the profit a business will realize on the average new customer over a period of years from that customer’s purchases. This number is critical, because it indicates exactly how much it is worth to acquire a given customer. Armed with this information, a business can manage its marketing programs not as an expense, or for short term profits, but as a long-term business investment.

A New Metric – The Network Value of a Customer

As the research on iPhone adoption illustrates, with the rise in the popularity of social networks, it has become increasingly clear that the true value of a customer goes beyond how much he or she might buy from you directly. Traditional measures of customer value ignore the influence a customer may have on how much others buy. For example, if a customer buys your product, and then, based on his recommendation, three of his colleagues buy your product as well, his effective value to you has quadrupled. On the other hand, if a prospect makes his decision based purely on what others tell him about your product, you will be better off spending your marketing dollars on his colleagues.

The implication for marketers means that the lifetime value of a customer can no longer be considered to have captured the true value of a customer.  The advance in the understanding of how social influence effects purchase decisions has lead to the creation of a new metric – the network value of a customer.  The network value of a customer is the expected increase in sales to others that results from marketing to that customer.

The Factors That Determine The Network Value of a Customer

Which customers have a high network value? There are few businesses that have access to the kind of data that the Telenor researchers had at their disposal – billions of call records. However, by considering the characteristics of customers that have a high network value, there is data that you can collect that will begin to help you identify and target the customers that you have with the highest network value. The customers with high network value share these common characteristics:

  1. A high level of satisfaction with your product
  2. Is highly likely to recommend your product to others
  3. Is highly connected to other potential buyers
  4. Is highly influential, an opinion leader

How to Target Customers With High Network Value

Even if you don’t have access to billions of records detailing the social connections and behavior of your customers, like the researchers at Telenor, there is data that you can easily collect about your customers that can help you target the customers that you have with the highest network value. They include:

  • Collect a Net Promoter Score from each customer – The metric is simple to collect and straightforward to determine, as described on netpromoter.com:

By asking one simple question — How likely is it that you would recommend [Company X] to a friend or colleague? — you can track these groups and get a clear measure of your company’s performance through its customers’ eyes. Customers respond on a 0-to-10 point rating scale and are categorized as follows:

  • Promoters (score 9-10) are loyal enthusiasts who will keep buying and refer others, fueling growth.
  • Passives (score 7-8) are satisfied but unenthusiastic customers who are vulnerable to competitive offerings.
  • Detractors (score 0-6) are unhappy customers who can damage your brand and impede growth through negative word-of-mouth.

Source: Netpromoter.com

With this one metric you can capture the first two characteristics of a customer with high network value – they 1) have a high level of satisfaction with your product, and 2) are likely to recommend it to others.

  • Collect social network information about your customers – many companies are starting to ask customers for their Twitter and/or Facebook usernames, in addition to other contact information such as email address. The very fact that a customer is willing to give you this information is an excellent indicator that the customer is actively involved with you product. In addition, it allows you to invite them to follow/friend you on Twitter and Facebook. Also, in the case of Twitter, it allows you to follow them, and collect vital publicly available information about them that indicates how many friends and followers they have, how many tweets they have made, and their bio. This will give you a measure of the third characteristic of high network value customers – how highly they are connected to other buyers.
  • Perform a social network analysis of your Twitter and Facebook followers – you can analyze your own Facebook and Twitter followers to determine which customers:
    • have the highest number of connections
    • are most likely to pass key marketing messages along to their followers
    • have the highest influence and are opinion leaders

This information allows you to fill in the final piece of information you need to get a handle on the network value of a customer – the fourth criterion, whether they are highly influential and an opinion leader. Now you’re ready to start testing and scoring groups of customers according to their network value.

Optimize Your Marketing Programs

Clearly, ignoring the network value of a customer may lead to suboptimal marketing decisions. By collecting the information you need to assess the network value of your customers, you can now model both the likelihood that a given customer will buy from you, and the influence that customer has on other’s buying decisions. Then you can select a subset of your customers, and determine not just how much they will buy from you, but the total amount of revenue that they might generate from their influence over others. This enables you to determine the optimal set of customers to market to that will generate the highest ROI.

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How Analytics is Revolutionizing Audience Development

I was recently invited to speak at the IAEE meeting in Boston to shed some light on how analytics can be used to increase attendance at events. In recent years event producers have found it more difficult to attract attendees, due to the rise of the Internet, the growing inconvenience of travel, and an economic recession. As event producers have struggled against these forces, they have in many cases not yet taken advantage of analytic techniques such as data mining, CRM, web analytics, social network analysis, and test and learn strategies to grow attendance levels. My session explored how to apply analytic techniques to radically improve the results of audience development campaigns. I have used these techniques on over 100 conferences, trade shows, and special events to achieve significant increases in attendance.

The topics I covered included the following:

The Lifetime Value of a Customer – A discussion of how to determine the lifetime value of a conference attendee is followed by the an examination of the much more difficult question of how to determine the lifetime value of an exhibit attendee. These attendees usually attend at no charge, and usually generate revenue only indirectly by attracting exhibitors and sponsors. In addition, I review an example of how knowledge of the lifetime value of an attendee can be crucial in decision making.

Closed Loop Marketing – A closed loop marketing system allows event managers to measure the results of all the various components of their audience development programs.  With accurate measurement of program results, they can accurately gauge the ROI of marketing programs, run controlled tests to optimize ROI, and identify key leverage points.

Email Optimization – Email is the keystone of many audience development programs. It is vital to optimize the revenue and response generated by email marketing through a comprehensive testing program. Properly done, email optimization can improve response by 50% or more, and in some cases double or even triple response. The presentation provides examples of how to identify key email test elements, implement carefully designed tests, and analyze the results.

Customer Profiling – Using the information about attendees collected during the registration process, prospects can be targeted with increased accuracy, and the results of marketing programs can be markedly improved.

Predictive Modeling – Moving beyond simple customer profiling, models can be developed that accurately predict which customers are likely to respond to promotions, and which customers are likely to defect. A case study is included on how predictive modeling helped triple conference revenue.

Segmentation Analysis – A highly effective way to identify which customers will respond to which promotions. Event managers can create custom-tailored marketing messages that address the needs of each segment to increase response, lower the cost of customer acquisition, increase retention, and increase cross-sales, up-sales, and referrals. An example of how a segmented campaign increased response by 20% is reviewed.

Web Site Optimization – Small increases in conversion rates can have a dramatic increase in registrations. An example of how minimizing abandonment rates during the registration process helped increase registrations by 30% is discussed.

Social Media Optimization –  Analytics can help event producers amplify the results of their audience development campaign through the optimal use of social media. By mining social networks to identify influential customers and prospects, adding social media profiles to the CRM system, and using predictive modeling to target high probability prospects, an event increased attendance by 30%.

As more event producers take advantage of these analytics techniques, they’ll be able to attract more and better qualified attendees to their events. Face-to-face meetings, the original channel of social media, will remain a vital method of marketing. Here are the slides from the presentation:

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Twitter at Events: Find Out What Attendees Really Think

The immediacy of Twitter has provided an unprecedented window into the collective mind of conference and trade show attendees as they share information on what they are doing and thinking right now. Just ask Evan Williams, the co-founder of Twitter. At his keynote at SXSW earlier this year, when he was interviewed by Umair Haque, Director of the Havas Media Lab, the negative comments on Twitter about the session came fast and furious while it was happening. “The guy behind us is snoring” tweeted one attendee, while another tweeted “walked out of the keynote…not very compelling”. This is not an isolated incident by any means. Recently, there was one call for banning Twitter at conferences, by a speaker who was dismayed that the audience was more engaged with tweeting than they were listening to the presentation.

Twitter has now made conference evaluation sheets and post-show surveys seemingly obsolete. If you really want to know what’s on the mind of your attendees, analyze the Twitter stream that flows from the attendees during a conference or trade show. There you will find the unfiltered and unvarnished truth about what attendees really think from the most vocal and most influential attendees at the event.

There is a wealth of information that can be gained from a Twitter stream during an event, well beyond the occasional negative comments that emanate from a keynote that goes flat. As an example, I archived the Twitter stream at a recent technology conference. In order to protect confidentiality, I’ll call it the Open Source Technology Conference. Let’s take a look at some of the information that can be gleaned from it.

The Most Influential Attendees

I collected a total of 1462 tweets that took place during the course of the event. 74% of the tweets were original tweets, 31% contain a @user reference, 39% contain hashtags, 33% contain a URL, and 26% were retweets. There were 312 distinct users that tweeted in the course of the event. Not surprisingly, the distribution of their tweets follows a power law (long tail) distribution.

The top ten most active users tweeting included the following:

What’s even more interesting are the conversational habits of the users, which can be illuminated by building a graph of their conversational patterns. The figure below shows a directed graph in which the users are the nodes and the edges represent mentions or replies between them.  In order to make the graph more visually intelligible, it shows only users shows users who have ten directed messages or more.

Each node in the graph represents a user. The size of the node is scaled to show the relative number of mentions and replies each user had. The color of each node ranges from red to blue. The more red a node is, the higher the authority value of the user – meaning that they are the users that receive the most mentions from others. The more blue the node is, the more the user tends to send @reply messages, and is thus more of a hub for conveying information to other users. The graph makes it clear that some users are more influential than others. The most important authority at the event was user “AmilCarta”, as is evidenced by the large red node representing that user’s interactions. This individual is an important person for the event organizers to recognize and interact with. The large size of user “theexhibitsgroup”, and its purple color, show that it is the second most important authority figure, but is also a hub that conveys important information to other attendees. All of the individuals on the graph, given their high level of interaction, are important for the event organizers to develop a close relationship with in order to ensure the success of their event.

Note that the volume of tweets generated by a user doesn’t necessarily mean that they interact with other users via mentions or replies. TSUS, the event organizer, was the seventh most active tweeter, but didn’t interact with attendees. TSUS used tweets to primarily make announcements about upcoming sessions, speeches, and awards programs. You can chalk this up as a major missed opportunity by the events organizer – by not interacting with attendees, it forfeited the opportunity to participate in the flow of the conversation.

What Attendees Were Tweeting About

The Twitter stream also sheds light on what topics were foremost on the mind of attendees. One way to get a handle on this is to look at the most frequently used hashtags by attendees in their tweets. By studying hashtags, we can determine what the key messages were that attendees want to spread via Twitter. The dataset for this event, in which 39% of the tweets contain hashtags, versus an average of 5% on Twitter as a whole, show a strong desire on the part of attendees to emphasize particular messages that will be found not only by other attendees, but by anyone interested in the particular topic represented by the hashtag. The top ten hashtags used at the event were as follows:

It’s not necessary to stop the analysis at this level. For instance, it’s possible to drill down into each of these topics and create a word cloud to get a better sense of the buzz around the topic. Below is the word cloud for the tweets containing the hashtag #ibm:

The word cloud gives an instant impression as to the content of the 94 tweets for anyone familiar with the event. It isn’t necessary to be limited by hashtags in trying to distill the content of the 1462 tweets. One can also use the text mining and data clustering techniques I described in my post  A New Way to Segment Your Twitter Followers With Analytics to discover the major themes of conversations at the event.

Even More Information…

I’ve really just scratched the surface as far as what you can learn about an event from analyzing its Twitter stream. There is much more that you can learn and implement:

  • Find out more about the interests, sentiment, and affiliations of your attendees by analyzing the content of linked URLs within tweets.
  • Get extra insight as to what activities generate buzz during your event by examining the timing of heavy periods of tweet activity.
  • Identify unique communities within your Twitter network. Based on the graph of interactions displayed above, algorithms can be applied to the network structure to identify groups of attendees who tend to communicate with each other more frequently than with the rest of the group. These communities may have different interests than the rest of the network, which can be used to custom tailor your communications with that community.
  • Cross reference and apply everything that you learn about the topics, conversational patterns, and communities of attendees that tweet during the event to your entire group of Twitter followers, and the friends, fans, and subscribers in all of your various social networks.
  • Determine the network value of an attendee. The valuable information that you can learn from analyzing the Twitter stream at your event underscores the importance of capturing in your CRM system the social media user name and/or identity of your prospects and attendees. Once captured, you can begin to determine the network value of an attendee – how much that indivdual may influence others to attend within your network of prospects. These attendees can be targeted with custom tailored communications, referral program incentives, and rewards programs.

If you have more ideas about the information that can be learned by analyzing the tweet stream at an event, or have questions, please leave a comment or email me at rhodgson@predictive-marketing.com.

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