Have Social 'Influence' Scores Become Another FICO?


Klout thinks I'm a 'Networker'

Not a day goes by without another article being published about how social media will change business forever.  Several companies have sprung up in the past several years including Klout, Twitalyzer, and PeerIndex that attempt to measure the value of social media usage, or more broadly, ‘social influence’.  As I read articles about how social influence is now used by companies to ‘Fan-gate’ or ‘Klout-gate’ their pages with special content and offers, I can’t help but draw a comparison to the ubiquitous FICO credit score.

FICO:  Likelihood a customer will go 90 days delinquent within 2 years

What was once just use to determine credit-worthiness, FICO has morphed into a way to customize car insurance rates, evaluate candidates for job openings, decide whether to rent an apartment to a tenant, etc.  While arguments have be made that there is a correlation between low FICO scores and a lot of undesirable behaviors, it’s quite another to blindly segment customers using credit attributes for non-credit purposes.  Yet this behavior happens all the time…

Social influence score:  “The probability of…”, what exactly?

The problem with trying to assign a value to social media interactions is that it’s completely business-specific.  Unlike FICO, which at least has a strict definition (ignored as it may be), social ‘influence’ can mean any number of things, depending on whether the person uses social media for work or pleasure (and in many cases, both).  Even better, the number can be gamed depending on which accounts you allow to get scored as part of your ‘influence’ (although, adding accounts only leads to increases in your score…for now).


Despite these different social influence score shortcomings, it is easy to see why companies like Audi, Subway, TNT Network, and others are willing to take a gamble that social influence (in this case, Klout score) has a correlation to something; as multi-million/billion dollar companies, the only way to get top-line growth is to experiment with new channels.  As it stands now, I’m having a hard time believing that the Klout ‘Perks’ is an effective way to market (that’s a whole ‘nother blog post!), but again, I can’t fault companies for trying.

Sanity still prevailing…

While I can see a parallel of social influence scores and FICO, luckily industry practitioners (the web analysts and marketers most likely experimenting in these new channels) are speaking out pretty loudly about understanding the positives and the cautions behind these scores.

I’m also glad to see (at least in the case of Twitalyzer), score providers participating in the conversation to discuss the issues surrounding the use of social influence scores in general.   Eric certainly has a lot of clout (pun intended!) in the web analytics community, so the message is definitely being heard there…but it’s up to all of us measurement folks to get the message out further in the marketing community on the proper usage of any model score.

Not FICO…not now, not ever

Ultimately, social influence scores will never achieve the level of widespread abuse that the FICO score has seen in the business world.  For one, there’s the voluntary nature of social media, which keeps large populations of people from ever being scored.  There’s also the fact that social influence is only calculated based on ‘affirmative’ activity (people ‘Like’ your contributions, they retweeted your articles, etc.), which cannot never be as predictive as also including negative interactions (like the FICO score does with missed payments).

But just because social influence scores probably won’t get abused in the same way, that doesn’t mean that us digital measurers should relax.  It’s up to us to make sure to keep stressing that just because companies can do something, doesn’t mean they should!  If anything, Kenneth Cole’s PR disaster should show that not all ‘influence’ is good, even if it makes your Klout score go up by 30 points!

“Without any clear strategy around what you’re going to DO with all these fans – you’re really just kind of a Facebook Marketing ho, with no direction.”  - digitalanalytics101.com

UPDATE - 10/27/2011:  With Klout making a change to their algorithm yesterday, and many heavy social media users seeing large drops in their scores, it seems like there ARE businesses and industry practitioners trying to use Klout as a pseudo-FICO score.  While my score dropped about 20% (from 51 to 40), I’m like most who see the whole “social influence” scoring as nothing more than an amusing game.

  • Using RSiteCatalyst With Microsoft PowerBI Desktop
  • RSiteCatalyst Version 1.4.14 Release Notes
  • RSiteCatalyst Version 1.4.13 Release Notes
  • RSiteCatalyst Version 1.4.12 (and 1.4.11) Release Notes
  • Self-Service Adobe Analytics Data Feeds!
  • RSiteCatalyst Version 1.4.10 Release Notes
  • WordPress to Jekyll: A 30x Speedup
  • Bulk Downloading Adobe Analytics Data
  • Adobe Analytics Clickstream Data Feed: Calculations and Outlier Analysis
  • Adobe: Give Credit. You DID NOT Write RSiteCatalyst.
  • RSiteCatalyst Version 1.4.8 Release Notes
  • Adobe Analytics Clickstream Data Feed: Loading To Relational Database
  • Calling RSiteCatalyst From Python
  • RSiteCatalyst Version 1.4.7 (and 1.4.6.) Release Notes
  • RSiteCatalyst Version 1.4.5 Release Notes
  • Getting Started: Adobe Analytics Clickstream Data Feed
  • RSiteCatalyst Version 1.4.4 Release Notes
  • RSiteCatalyst Version 1.4.3 Release Notes
  • RSiteCatalyst Version 1.4.2 Release Notes
  • Destroy Your Data Using Excel With This One Weird Trick!
  • RSiteCatalyst Version 1.4.1 Release Notes
  • Visualizing Website Pathing With Sankey Charts
  • Visualizing Website Structure With Network Graphs
  • RSiteCatalyst Version 1.4 Release Notes
  • Maybe I Don't Really Know R After All
  • Building JSON in R: Three Methods
  • Real-time Reporting with the Adobe Analytics API
  • RSiteCatalyst Version 1.3 Release Notes
  • Adobe Analytics Implementation Documentation in 60 Seconds
  • RSiteCatalyst Version 1.2 Release Notes
  • Clustering Search Keywords Using K-Means Clustering
  • RSiteCatalyst Version 1.1 Release Notes
  • Anomaly Detection Using The Adobe Analytics API
  • (not provided): Using R and the Google Analytics API
  • My Top 20 Least Useful Omniture Reports
  • For Maximum User Understanding, Customize the SiteCatalyst Menu
  • Effect Of Modified Bounce Rate In Google Analytics
  • Adobe Discover 3: First Impressions
  • Using Omniture SiteCatalyst Target Report To Calculate YOY growth
  • Google Analytics Individual Qualification (IQ) - Passed!
  • Google Analytics SEO reports: Not Ready For Primetime?
  • An Afternoon With Edward Tufte
  • Google Analytics Custom Variables: A Page-Level Example
  • Xchange 2011: Think Tank and Harbor Cruise
  • Google Analytics for WordPress: Two Methods
  • WordPress Stats or Google Analytics? Yes!
  • Getting Started With MapD, Part 2: Electricity Dataset
  • Getting Started With MapD, Part 1: Docker Install and Loading Data
  • Parallelizing Distance Calculations Using A GPU With CUDAnative.jl
  • Building a Data Science Workstation (2017)
  • JuliaCon 2015: Everyday Analytics and Visualization (video)
  • Vega.jl, Rebooted
  • Sessionizing Log Data Using data.table [Follow-up #2]
  • Sessionizing Log Data Using dplyr [Follow-up]
  • Sessionizing Log Data Using SQL
  • Review: Data Science at the Command Line
  • Introducing Twitter.jl
  • Code Refactoring Using Metaprogramming
  • Evaluating BreakoutDetection
  • Creating A Stacked Bar Chart in Seaborn
  • Visualizing Analytics Languages With VennEuler.jl
  • String Interpolation for Fun and Profit
  • Using Julia As A "Glue" Language
  • Five Hard-Won Lessons Using Hive
  • Using SQL Workbench with Apache Hive
  • Getting Started With Hadoop, Final: Analysis Using Hive & Pig
  • Quickly Create Dummy Variables in a Data Frame
  • Using Amazon EC2 with IPython Notebook
  • Adding Line Numbers in IPython/Jupyter Notebooks
  • Fun With Just-In-Time Compiling: Julia, Python, R and pqR
  • Getting Started Using Hadoop, Part 4: Creating Tables With Hive
  • Tabular Data I/O in Julia
  • Hadoop Streaming with Amazon Elastic MapReduce, Python and mrjob
  • A Beginner's Look at Julia
  • Getting Started Using Hadoop, Part 3: Loading Data
  • Innovation Will Never Be At The Push Of A Button
  • Getting Started Using Hadoop, Part 2: Building a Cluster
  • Getting Started Using Hadoop, Part 1: Intro
  • Instructions for Installing & Using R on Amazon EC2
  • Video: SQL Queries in R using sqldf
  • Video: Overlay Histogram in R (Normal, Density, Another Series)
  • Video: R, RStudio, Rcmdr & rattle
  • Getting Started Using R, Part 2: Rcmdr
  • Getting Started Using R, Part 1: RStudio
  • Learning R Has Really Made Me Appreciate SAS