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.

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.

How To Maximize Your Reach on Twitter

Tweets are ephemeral. Chances are, unless a person is engaged with Twitter when you tweet, they aren’t going to have the opportunity to read it. Not only do people ignore or lose track of old tweets, they are dropped from the Twitter database. Unlike a blog, which is long-lived and indexed by Google for future reference, tweets are heavily time-dependent. If you’re running a business trying to reach as many customers as possible with your corporate message, the timing and frequency of your tweets are critical to your success.

In a previous post, I examined the question of what time of day is best to tweet. To determine the answer, I analyzed two sets of data representing the behavior of two different groups of followers. It turned out that each group had a different best time of day to tweet, and that a single tweet reached between 10% and 24% of the followers.

That brings up the question: if you employ multiple tweets, what percentage of your followers can be reached? Guy Kawasaki recommends posting your most important tweets 4 times, 8 to 12 hours apart, to reach as many of your followers as possible. Let’s take a look at this question for the same two groups of followers analyzed in my previous post.

A quick review: I collected data over the course of several weeks for two Twitter groups –  followers of a company supplying services to event professionals, and followers of a company selling CRM software. The data set consisted of several thousand tweets, including the username, the time and day of the tweet, and the tweet itself. For the purpose of this analysis, I assumed that the best indicator of a given follower’s availability to read tweets was whether or not they had tweeted within a given hour. I was then able to determine for any given hour of the day, how many unique followers were active, and presumably reading their Twitter stream.

To figure out the impact of multiple tweets on reach, I then ranked all of the hours in the day in order of how many unique twitterers there were during any given hour. Choosing each hour in order of priority, I then eliminated duplicates.

The results for the group of event professionals are as follows:

The graph displays the percentage of unique followers that can be reached for each tweet. For example, a single tweet during the best hour of the day can reach 24% of the followers, two separate tweets during the two most active hours of the day, 40%, and three 50%. The graph shows that using Guy Kawasaki’s rule of thumb, that you can reach 60% of your followers with four tweets (we’ll see later that these four tweets should not take place 8 – 12 hours apart). For this group of event professionals, it takes eight tweets to reach 80% of the followers.

Now let’s examine how reach is affected by multiple tweets for the CRM software group:

As you can see, there are dramatic differences between the two groups in the extent to which a given percentage of followers can be reached with the same number of tweets. For example, it takes ten tweets to reach 60% of the CRM software group, compared to the four tweets needed to reach 60% of the event professionals group.

Each group is different. If your business needs to make sure its message is reaching the widest possible audience, you need to develop a similar analysis for your group of followers.

The graphs above show only the number of tweets required to reach a given percentage of followers, but not what times to tweet.  The chart below reveals when each tweet should be deployed to achieve the reach shown in the graphs above.

Note that the first three tweets for each group, while not in identical order, occur in the 10:00 AM – 12:59 PM time period. After the first three tweets, the best time for additional tweets varies according to the group. In both cases, Guy Kawasaki’s rule of thumb – four tweets 8 – 12 hours apart – would not maximize reach. To be fair to Guy, his rule of thumb may well work for his group of followers. The point I’m making here is that you can’t generalize – there is a different strategy to maximize reach for each group of followers.

Summary

• If you are trying to maximize the effective reach of your message, the ephemeral nature of a tweet puts a premium on the timing and frequency of your tweets.
• A single tweet will only reach a fraction of your followers. For the two groups examined, the range was 10% to 24%.
• By analyzing the times during which your followers tweet, it is possible to develop a strategy to predict the percentage of your followers that you can reach with multiple tweets.
• It is also possible to determine the best times of day for multiple tweets. Note that the muliple tweets don’t necessarily have to take place during one day; they can be spread out over several days so as not to annoy your most attentive followers.
• Every group of followers is different. You need to analyze the tendencies of your followers to determine the optimal strategy for maximizing the reach of your most critical messages.

Predicting Market Demand With Social Media

Social media is now being extended beyond its original applications into a tool for predicting the future. The exponential growth in social media has helped create a large body of content that reflects the trends, experiences, evaluations, and sentiment of the marketplace. It is becoming increasingly apparent that this content can be mined and analyzed to help predict the size of markets, the outcomes of marketing campaigns, and marketing ROI. In this post I’ll take a look at three ways in which data generated by social media has been used recently for Predictive Marketing.

Predicting Movie Box Office Results with Twitter

A group of researchers at HP Labs recently published a paper describing how they used data captured from Twitter posts to predict box-office revenue at the movies. The researchers extracted 2.89 million tweets from 1.2 million users referring to 24 different movies over a period of three months. For each tweet, the timestamp, author, and tweet text were collected and used for analysis. The researchers focused on what they termed the “critical period” – the week before and the two weeks after the release of a movie.

An initial analysis of the tweets revealed that:

• The tweets built up in volume the week before the movie release; peaked at the time of the release; and fell during the two weeks following the release.
• The average number of tweets made by individuals about a particular movie was between 1 and 1.5.
• The distribution of tweets by individuals showed that a handful of individuals made many tweets; the distribution followed the “long tail” power distribution that frequently occurs on the web.

The team then proceeded to analyze the data for predictive power. First, what didn’t prove to be very good predictors of box office success:

• Prior to the release of a movie, studios promote the film heavily via TV, print, news releases, interviews with the stars, and trailer videos. The researches classified tweets according to whether they contained urls, indicating that they could reference trailers, movie reviews, or other PR about the movie. It turned out that although 22% to 40% of the tweets contained urls, such tweets were only mildly predictive of box office success.
• The percentage of retweets was in the 11%-12% range, and was even less predictive of box office success. This is surprising, given that retweets are indicative of word-of-mouth.

There were three factors that proved to be powerful predictors of box office revenue:

• The tweet rate, or the number of tweets about a given movie per hour. This is indicative of the overall attention and interest that the movie is generating. This factor is particularly important in predicting the box office reevnue for the opening weekend.
• Positive sentiment about the movie. The researchers created a customized method for analyzing positive and negative sentiment about movies for the purposes of this study. Sentiment proved to be an important factor in predicting box office revenue in the weeks after the opening.
• Distribution, or the number of theaters in which the film screened. The wider the distribution, the more opportunity that existed for revenue generation.

Using these data as a predictive model, the team was able to demonstrate that they could predict opening weekend box office revenue with 97.3% accuracy. This compared favorably with a well-known prediction tool for movies, the Hollywood Stock Exchange (HSX), that had 96.5% accuracy. The results for these two techniques are shown in the graph below:

The exciting implication of this study is not that this particular application of using social media to predict box office revenue, but that a model has now been developed to to use social media to predict a wide variety of outcomes from product sales to elections. The researchers conclude:

While in this study we focused on the problem of predicting box office revenues of movies for the sake of having a clear metric of comparison with other methods, this method can be extended to a large panoply of topics, ranging from the future rating of products to agenda setting and election outcomes. At a deeper level, this work shows how social media expresses a collective wisdom which, when properly tapped, can yield an extremely powerful and accurate indicator of future outcomes.

Using Social Media to Predict Election Results

The predictive model used to forecast outcomes using social media developed by the HP Labs team is one of the few that is customized, well-defined, and mathematically rigorous. However, that hasn’t stopped people from predicting outcomes from social media trends, even if they lack such a powerful tool.

One of the most stunning election outcomes in the past few years was the victory of Scott Brown over Martha Coakley in the special Massachusetts U.S. Senate election to replace the vacant seat created by the passing of Ted Kennedy. Larry Kim published a blog post five days before the election forecasting the upset for Scott Brown.

At the time, conventional polls suggested that the race was too close to call, despite the fact that the Democrat Coakley had been the early front runner and had a seeming lock on the seat, given the nature of the Massachusetts electorate. However, while Coakley coasted through the campaign, the hard work and grass roots effort employed by the Brown campaign paid huge dividends. Kim’s analysis of social media trends showed that Brown had developed a huge advantage is social media presence:

• 10:1 Advantage in YouTube video views
• 4:1 Advantage in Facebook fans
• 3:1 Advantage in Twitter mentions
• 10:1 Advantage in estimated web traffic

The trend in web traffic as measured by Alexa was particularly telling:

While the data looked overwhelming, Kim, to his credit, lacking a quantitative predictive model such as that employed by the HP Labs team, cautioned that the type of people who were heavy users of social media were undoubtedly a biased sample that was perhaps not representative of the electorate as a whole. However, the trends looked so overwhelming that, on the basis of this data, Kim concluded that Scoot Brown was headed for a victory. Five later Brown proved the prognostication accurate.

Using Social Media to Predict Fashion Trends

One final example of the predictive power of social media involves the notoriously fickle and unpredictable fashion world.

Luke Brynley-Jones of Our Social Times reports that Geoff Watts from Stylesignal has developed a new social media monitoring tool that helps to track new fashion trends. The tool is used to monitor the sites of opinion leaders in the fashion world.  The data collected by the tool is then analyzed offline to predict fashion trends. Case studies on the site claim that the StyleSignal has helped correctly predict the colors, shapes, and styles that become trend setters.

Summary

It has become increasingly clear that social media can be used to predict future events with accuracy. Now that predictive models have been developed for quantifying and measuring the accuracy of predictions, you should expect to see explosive growth in the use of social media in forecasting.