Influencer marketing- is a form of marketing that has emerged from a variety of recent practices and studies, in which focus is placed on specific key individuals (or types of individual) rather than the target market as a whole. It identifies the individuals that have influence over potential buyers, and orients marketing activities around these influencers.

As I will demonstrate it is possible, using some freely available analytics tools, to narrow the list of influencers. However, as our own Social Network sage Matt Thomson pointed out, the analytics tools fall short in becoming truly useful and further click-based research is really needed to establish credible, influencers who may become evangelists.

Given the current landscape I will choose Twitter and blogs as my universe from which to collect people. In this exercise let’s say that I wanted to find people in Colorado who have a large sphere of influence in the digital world. So let’s begin to find some influencers.

I want to start off with a list based on Twitter users for two main reasons:

1. - Hyper-active Facebook users and bloggers also seem to have accounts on twitter
2. - Twitter ranking tools are more predominant making it easier to build my initial people-of-interest list.

Influencer Search

There are a number of twitter ranking or grading sites. TweetValue provides a monentary evaluation of a twitter profile. TwitterGrader provides a “grade” for twitter users and TwitTown provides a list of the top 150 users. TwinFluence analyzes a twitter profile to derive reach and trending measurements. All of these “analytics” tools have one problem, they are not very searchable. Honestly though they server their purpose which is to rank users based on whatever formula each site thinks is best. Fine. But how can we use this?

TwitterHolic will provide the top 1000 twitter users and unlike some of the other sites it displays the location. So, using the old “Next Button” I iterate through the pages grabbing all the users in Colorado:

42. Darth Vader (darthvader) Empire, CO Followers: 19,484  Friends: 4,015  Updates: 250

201. zaibatsu (zaibatsu) Denver http://thedrilldown.mixx.com/ 6,494    6,386     1,732

227. Joshua Allen (fireland) Denver http://www.fireland.com/ 6,075    61    372

255. Lucretia M. Pruitt (GeekMommy) Denver, CO http://www.geekmommy.net 5,791   5,083   26,702

397. Jeff Walker (JeffWalker) Colorado http://www.productlaunchformu… 4,371    44  786

I abbreviated the list a bit but you get the gist. From this list I will use my logical deduction skills and determine that Darth Vader is probably not a good person to contact. Although he is very good at gaining friends and influencing people I think his desire for evil may create a rough relationship.

Zaibatsu on the other hand is a good suspect, although, per his website, he was banned from Digg. Good for him, great for publicity.

Joshua Allen is outright hilarious and you should read his blog/tweets. That said he is probably a good person to talk to. If nothing else we would get a laugh.

Number 255 is fantastic. Her blog already has emblems for an “Eleven Moms” community so she is definitely in the know and aware of her circle of influence. Along the same lines Jeff Walker looks like a good candidate.

If the list were longer or if I wanted additional information I would use TwinFluencer to trim the list or provide additional ranking metrics. Since I only have 4 users I don’t really need to perform any additional trimming.

No matches? Well if you have no users showing up out of the 1000 then the next step is to use a tool like Monitter and search for keywords in tweets with X miles or kilometers of a city or cities in your area of concern.  This should give you a good idea of who is active.  If you monitor the results for a time you will should be able to get a good starting list, maybe even better than by starting with high “ranking” twitter users.

Influential Blogs

Now I have some active people with large followers now lets see what their digital properties are like. There is a great blog here which discusses various ways to measure a blog’s influence.

Using Alexa we can get a feel for the amount of traffic a blog is receiving and where the traffic is originating. Here is the Alexa Chart for number 201’s blog, thedrilldown.mixx.com:

[caption id=”attachment_583″ align=”alignnone” width=”300″ caption=”Blog Hits”][/caption]

Sadly, in order for Alexa to display the metrics the site must be in the top 100,000. Usually this is a pretty high-order for individual blogs. What do we do then? Well, one rough and quick measurement is to use Google and find out how many sites link to each person’s blog/site. The syntax is: “link:<url to check linking to> for example: link:http://www.fireland.com/

Site Links General Search hits

www.fireland.com 250 21,300

http://www.geekmommy.net 369 4,170

http://thedrilldown.mixx.com/ 204 3,950

While at google we might as well just search on each person to see what happens, I put the search results count for these general searches in the last column.

Conclusion

From these results I would conclude that GeekMommy is the best candidate to tap on to become an evangelist or catalyst for some marketing or pr campaign. She has many more updates, friends and linkback than the second place finisher, Fireland.

Well that was the non-automated, quick and dirty way to try to find influential social network users in a given regions.  Yes the process leaves a lot to be desired and is not very robust but it should give you an idea of how much research is still required to gleen usable, actionable information out of the social networks.  If you are intersted in hearing about some of our social networking analytics tools and ideas let us know and give us some information on what you would like out of such tools.  We only build things that people ask for.

In the past, and even today, a hosting company managed hundreds to thousands of servers and when a new customer wanted to go online the customer deduce what size server is needed or whether they can share a server with other customers. That arrangement works well but it can be expensive and it does not scale very well so what else is there? The alternative is a new type of hosting environment: Cloud computing.

Cloud computing (also known as utility or on-demand) enables companies like Istobe to buy chunks of processing power, memory and storage space as we need it. Previous to cloud computing the determination on what size server we needed from our host was kind of like trying to buy boxes for a move. You know that you want to get more than enough boxes to package all your goods but you do not want to have too large of a surplus. Having cloud computing as an option is like having a box machine available during your move, when you need another you push the “create box” button and out pops another perfect box. Once your move is complete you pay for the boxes you used.  What does this mean for our customers?

Cloud computing provides a lot of direct value to our customers.  Cost savings is one, other less obvious benefits are:

• Data Security- Each customer has an isolated instance of our product, eliminating the possibility of one customer gaining access to another’s data.
• Quick Feature Release- Isolated instances allow us more flexibility in deploying new features or features for customer-specific needs because we can easily perform “soft” roll-outs to determine if the features are helpful and usable.
• Performance- Since we can scale our products based on the needs of each customer we are able to ensure that each customer has the highest performing system available. With a dedicated environment for each customer we reduce the possibility of high-usage customers causing other customers to have a negative experience.

Cloud computing is just one technology that will enable Istobe to make enterprise level analytics solutions available to anybody.

I will be the first to admit that our industry, predictive analytics to support retail marketing strategies, is dense with technical words, phrases and methodologies all wrapped up into various umbrella groups or plans such as “Market Segmentation Analysis”, “Customer Analysis”, “Predictive Marketing”, “Retail Pricing Strategy” etc. Although each sounds a bit different all of these approaches strive to reach the same outcome:

• Use available data to describe customers and understand their needs, wants and actions

and each takes a different approach but all the approaches are based on well established principles of computer science and statistics.

As much as I would like it to be, the truth is the algorithms and data analysis procedures that are used in market segmentation have been around for decades. In fact Bayesian approaches (A common algorithm used to analyze data) have been used since the 1950s. The military used the same statistical principles to create the first radar systems, code-breaking methodologies in WWI, and plan various logistic strategies in WWII. There is no magic and no basement-scientist voodoo in our Market Segmentation Research or our buying predictions; just mature, proven calculations sprinkled with statistical know-how. So if Market Segmentation is more historical non-fiction then what makes it exciting? Innovation.

Although the fundamentals have been around for decades, modern day computers and storage have enabled us and others to apply those proven ideas to large sets of data. Today’s computers allow for innovation to take place within the domain of Market Segmentation Research in the form of applicability. In fact the Association of Computing Machinery has published several recent papers documenting how traditional segmentation analysis is applicable to today’s retailer (see references below). At Istobe our contribution to innovation is demonstrated by our ability to quickly convert data to actionable predictions to help define:

• retail marketing strategies,
• retail pricing strategies

and optimize and create:

• direct email marketing campaigns
• targeted email campaigns
• email promotion for internet marketing

So Market Segmentation is an old academic subject but the science fiction is in how we apply it.

ACM Market Segmentation Research Examples:

Research on customer segmentation model by clustering

In the paper, we use credit card consumption data as our model-building samples and present a modeling framework for building segment-level predictive models that utilize pattern-based clustering approach and signature discovery techniques. We devise monetary matrix and fluctuate-rate matrix to study various modes. Through clustering on both matrixes, we uncover different customer characteristics. Utilizing these characteristics, we can build two-dimension Consumption-Based customer segmentation model.

Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer

Most marketers have difficulty in identifying the right customers to engage in successful campaigns. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketing campaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. This investigation identifies customer behavior using a recency, frequency and monetary (RFM) model and then uses a customer life time value (LTV) model to evaluate proposed segmented customers. Additionally, this work proposes using generic algorithm (GA) to select more appropriate customers for each campaign strategy. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a Nissan automobile retailer to segment over 4000 customers. The experimental results demonstrate that the proposed method can more effectively target valuable customers than random selection.