Predictive Marketing

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 (

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

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.


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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A New Way to Segment Your Twitter Followers With Analytics

The Twitter list feature has made it possible to create groups of people and follow their twitter stream independently. It’s a new twist on a classic marketing technique – segmentation. In the case of Twitter, it’s an opportunity to listen to a group of followers with a common set of interests and learn what’s on their mind.

If you have a personal Twitter account, it’s easy enough to create the lists that might be most meaningful to you. For example, you might organize them by family, friends, golfers, wine enthusiasts, etc. When it comes to business, however, it’s a little less straightforward how to go about segmenting your followers. And if your business has thousands of followers, it can get pretty tedious trying to segment them manually.

Using Text Mining and Clustering to Segment Your Followers

There are several approaches to segmenting your followers on Twitter. The powerful approach I’ll illustrate here is to use classic text mining and clustering techniques to let the data you have regarding your followers organize itself into the most appropriate segments. For instance, you could use each follower’s Twitter bio as the data to be used in creating segments. I recently did this for a client that had over 3,200 followers on Twitter. An illumnating visual aid to use in this example is a word cloud. Here is the word cloud that resulted when combining all 3,200 bios.

As you can see, there are certain words that seem to stand out and suggest different segments, such as storage, marketing, virtualization, media, technology, etc. That seems straightforward enough, but not all of the key words I just listed necessarily appear only once in a bio.

For instance, how do you go about classifying the following bios?

  • Solutions for data storage, protection, availability, virtualization and collaboration.
  • I tweet about Internet marketing, social networking, business development, and technology.
  • Strategy, positioning, PR and Social Media for tech, B2B, consumer. Focus on clean, semi, storage and start-ups.

Each of the examples includes more than one of the most frequently used words listed above. You can see that assigning a follower to a segment on the basis of a single world is not a simple matter. If you were trying to attempt the segmentation of these followers manually, you could easily spend a lot of time trying to decide the most appropriate segment in which to place them.

That’s where the power of text mining can save the day. Using text mining and clustering algorithms, it’s possible to classify the bios into segments not just based on the appearance of a single word, but on the frequency of the appearance of all the words in the bio, and their tendency to appear together in the same bio. It’s the same principle used to find relevant documents when you use a search engine. In that way, all of the information in a bio is used to create the segments of followers who are most alike.

As an example, let’s take a look at the resulting word cloud for the storage segment.

As you can see, the bios in this segment are indeed dominated by a single word: storage, and secondarily, data. The text mining and clustering algorithms combine to create a very pure segment of followers that are focused on storage issues. The beauty of the algorithms is that they are not limited to the presence of a single word. For instance, consider the following segment:

Once you see this segment, it makes perfect sense. While the words social and media appeared separately in the word cloud of all bios, it turns out, not surprisingly, that the two words appear very frequently together, and create another very pure and distinct segment of followers. While you might be saying, well, that one was obvious, would either of the following two segments be obvious from the original word cloud?

Putting it All Together to Listen to Your Customers

The algorithms are finding patterns in the data that may not be intuitively apparent. This is a great illustration of the ability of text mining and analytics – in this case clustering – providing considerable added value in finding unexpected and fruitful patterns in data.

And here’s something that’s a real time saver: once the segments have been defined, as new followers are added, a classification algorithm can be used to place each new follower in the best segment, making the process of determining which list to place a new follower automatic.

Now let’s get back to the original reason for placing your Twitter followers on a list: to listen to what a group of people with similar charcteristics are saying. So here’s the icing on the cake: after placing your followers on a list, you can then collect their tweets over a period of time and use the same methodology: text mining and clustering, to classify their tweets into common areas of interest or concern. When you consider that you can process thousands of tweets this way, and find novel and unexpected patterns  in their comments, you have the ultimate opportunity to really listen to your customers.


  • The Twitter list feature has made it possible to listen to a group of followers with a common set of interests and learn what’s on their mind.
  • It’s possible to use text mining and clustering algorithms to let the data you have regarding your followers organize itself into the most appropriate segments.
  • The algorithms are finding patterns in the data that are not be intuitively apparent, providing considerable added value in finding unexpected and fruitful insights into the mindset of your customers.
  • As new followers are added, a classification algorithm can be used to automatically place each new follower in the best segment.
  • Text mining and clustering can then classify the tweets of individuals on the different lists into common areas of interest or concern, and find novel patterns  in their comments, providing the ultimate tool for listening to your customers.

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Extend Your Reach on Twitter With Viral Marketing

One of the many attractions of Social Media is the opportunity to amplify your message through viral marketing.  In theory, if you can deliver the right message to a select number of the right people, you can reach thousands, or even millions of people on a shoestring budget. In previous posts, I have analyzed how to maximize the effective reach of your message on Twitter by deploying your tweets during the best time of day to your followers, and repeating that message at strategic times to extend your reach even further. The objective of both of these techniques was to reach as many of your followers as possible with your message. Let’s now examine the opportunity of extending your reach beyond your group of followers through viral marketing on Twitter.

The Lure of Viral Marketing

Every marketer dreams of the following scenario: You convey your message to a select group of individuals. Each of these individuals then repeats your message to one or more of their friends, who in turn repeat the message to one or more of their friends, and before you know it, your message has successfully reached millions of people.

The most recent example of this dream scenario was the  Facebook campaign to have Betty White host Saturday Night Live.  A 29 year old man from San Antonio started the campaign with the modest goal of gaining 5,000 fans on the Betty White to Host SNL (please?)! Facebook page. He reached his goal in a month and wrote a letter to Lorne Michaels, the Executive Producer of SNL, to encourage the selection of Betty as a host. The story was then picked up by major news agencies. A few months later, the Facebook page had 500,000+ fans, Betty White hosted SNL, and the show grabbed its highest ratings in 18 months.

Viral Marketing on Twitter

Dream scenarios are by definition rare. How much can you reasonably expect to extend your reach beyond your group of followers through viral marketing on Twitter?

The vehicle for viral marketing on Twitter is the retweet. It’s the indicator of how many times your message is repeated throughout the Twittersphere.  To get an idea of what the typical results of viral marketing on Twitter are, let’s take a look at what some of the most retweeted users are able to achieve.

I have focused the above analysis on business or news oriented sites, since I am addressing the question of how business marketers can extend their reach through Twitter. Therefore, no celebrities or other non-business entities are included.

The data shows the total number of followers for each user, the average number of tweets they make each day, the largest number of retweets they generated from a single tweet during the week of May 13-19, and an estimate of the % increase in reach they achieved over and above their follower base with their best tweet of the week.

In the calculation of the percentage increase in reach, the analysis makes the assumption that on average, each user who retweets is followed by 300 people. Although 93.6% of Twitter users have less than 100 followers, I’ll use Hubspot’s estimate of an average of 300 followers for the most active 5 million Twitter users. This makes intuitive sense, since the users most likely to retweet are among the most active, and in a long tail distribution the average is higher than the median due to the effect of the users with the most followers.

The data is surprising. I would have expected that the number of retweets would have been much higher for the six users with more than one million followers. Mashable wins the award for most retweets at 1,018.  The award for the greatest increase in reach goes to a tweet by HubSpot at 135%, more than doubling its reach via retweets.  Although it was only retweeted 159 times, the much smaller number of followers of HubSpot in comparison with Mashable translates into a greater percentage increase in reach.

The data seems to indicate that the users with the relatively smaller following have the opportunity to gain the most in percentage reach. This is not surprising, since for users whose following exceeds one million, there is only so far that they can extend their reach.

The Limits of Viral Marketing on Twitter

The above examples show how much the best tweets of some of the most retweeted users are able to extend their reach via viral marketing on Twitter during a typical week. The two best case scenarios, for HubSpot and Avinash Kaushik, range from a 60% – 135% extension of their reach. In terms of the dream scenario for viral marketing, these gains may not seem like much, but in practical terms, any time that you can increase the effectiveness of your marketing by 60%+, that’s significant.

Twitter is not the best platform for viral marketing. Tweets are ephemeral; they come and go. Twitter lacks the permanence of a blog post or a Facebook page, making it harder to achieve the explosive exponential growth of a true viral campaign. And the dream scenarios of viral marketing are not achieved via a single marketing medium; they are achieved through a perfect storm of mutually reinforcing marketing media. For example, the Betty White campaign was heavily reinforced by traditional news media.

When it comes to Twitter, it may be best to remember Avinash Kaushik’s tweet: “Success on twitter comes fm participating in conversations & adding value. It does not come fm “social media campaigns”.

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