Blue Chipper and Marketing Analytics
November 9, 2016
I think this write up “Reporter’s Notebook: McKinsey’s Heller Talks Analytics” is a summary plus odds and ends based on a McKinsey blue chip consultant’s lecture. McKinsey prides itself on hiring smart people, and it does some crafty buzzwording when it makes the obvious so darned obvious.
I noted this passage:
CMOS are asking: Do we have enough data scientists? Are we accelerating customer acquisition? Are we increasing customer value? What they care about is taking the intense amount of data that happens every day from call centers, Web sites and stores, then stitching it together and identifying new customer segmentation and new opportunities to create growth. The CMO is thinking about data science — how it can drive growth about the organization.
The idea is that federating disparate information is important from McKinsey’s point of view.
How does a marketer deal with data in a way that makes revenue? I highlighted this MBA formula: Get organized, plan, and hire McKinsey to help. The 4Ds will help too:
- “Data. Aggregate as much information as possible and everything you do downstream creates more value.
- Decisioning. Run advanced models — propensity models, churn models — against that data. You don’t become a data scientist overnight. The organization needs to do customer scoring and advanced analytics. Identify where the data fiefdoms are in your organization (people holding on to their data to protect their jobs) and get the right people together.
- Design. Managing the content, offers and experience the customer receives and being curious and experimenting. Testing. A/B testing. Once you have the models, what are the experiences these customers want to see?
- Distribution. Push both the decision data and test design into marketing. Close the loop and measure everything. If I’m in a room of marketers and I ask them what their roles are, they’re distributing marketing communications, just not in a truly data-driven way.”
But the marketing officer must embrace the five core beliefs behind “mobilization.” I bet you are eager to learn these five insights. Here you go:
- “Mobilize cross-functional leaders around the opportunity. The CMO needs CIO, store operations, different people to help break down the silos.
- Get creative about navigating the legacy … be relentless about solutions.
- Walk before you run. Identify a roadmap, pick some high priority areas and execute.
- Prioritize “lighthouse” projects to kick-start execution.
- Let data activation drive your new marketing operations model.”
What’s the payoff? Well, for McKinsey it is billable hours. For the client:
We see real aggressive growth with clients doing nothing wrong in the range of a 6X revenue capture. If I can increase the speed by which you test, you’re increasing revenue . Typically conversion rate increases from the low end of the 20s to high end of 150 percent plus range … on the digital sales side yield exponential gains of 2, 3, 5X. Just 1 percent, 2 percent or 3 percent of enterprise value creation for a multi-billion company — driven by digital — is huge.
Huge? That seems to be a trendy word. Where have I heard it before? Hmmm. Will McKinsey guarantee the measurable benefit of its consultants’ work? My hunch is that McKinsey sends invoices; it does not write checks when its work wanders a bit from the data in a presentation.
Stephen E Arnold, November 9, 2016
Demand for Palantir Shares Has Allegedly Gone Poof
November 7, 2016
I read “Ex-Palantir Employees Are Struggling To Sell Their Shares.” Let’s assume that the information in the write up is spot on. The main idea is that one of the most visible of Silicon Valley’s secretive companies has created a problem for some of its former employees. I learned:
Demand has evaporated” for the shares that make up the bulk of Palantir’s pay packages, and the company’s CEO seems aware of financial angst among his staff.
The softening of the market for stock options suggests that the company’s hassles with investors and the legal dust up with the US government are having an effect. Couple the buzz with the prices in Silicon Valley, and it is easy to understand why some people want to covert options for cash money. I highlighted this passage:
Some said they needed the cash to buy a house or pay down debt, while another said they took out a loan to fund the process of turning the options into shares. One said it was “infuriating” trying to sell their shares in a “crap” market.
I found this statement from a broker, who was not named, suggestive:
This person then quoted an unidentified broker as saying, “There is absolutely nothing moving in Palantir. People who have bought through us are trying to sell now. I don’t see it changing without the company changing their tone on an IPO.”
With the apparent decision relating to the US Army and it procurement position with regards to Palantir going the way of the Hobbits, perhaps the negativism will go away.
One thought: Buzzfeed continues to peck away at Palantir Technologies. Palantir Technologies has a relationship with Peter Thiel. The intersection of online publications and Peter Thiel has been interesting. Worth watching.
Stephen E Arnold, November 7, 2016
DataSift and Its Getting the Most from Facebook Series
November 6, 2016
There’s been some chatter about Facebook’s approach to news. For some researchers, Facebook is a high value source of information and intelligence. If you want to get a sense of what one can do with Facebook, you may find the DataSift series “Getting the Most from Facebook” helpful.
At this time there are six blog posts on this topic, you can locate the articles via the links below. Each write up contains a DataSift commercial:
- Types of social networks
- What data analytics can be used on Facebook data
- Facebook topic data
- Topic data use cases and drawbacks
- Why use filters
- Pylon specific tips but these apply to other analytics systems as well.
The write ups illustrate why law enforcement and intelligence professionals find some Facebook information helpful. Markets are probably aware of the utility of Facebook information, but to get optimum results, discipline must be applied to the content Facebookers generate at a remarkable rate.
Stephen E Arnold, November 6, 2016
Model Based Search: Latent Dirichlet Allocation
November 5, 2016
I worked through a presentation by Thomas Levi, a wizard at Unbounce, a landing page company. . You can download the presentation at this link but you will need to log in in order to access the information. There’s also a video and an MP3 available. The idea is that concepts plus tailored procedures in models provides high value outputs. I noted this passage:
utilizing concepts in topic modeling can be used to build a highly effective model to categorize and find similar pages.
I noted the acronym LDA or Latent Dirichlet Allocation because that struck me as the core of the method. For those familiar with the original Autonomy Digital Reasoning Engine, there will be some similar chords. Unbounce’s approach provides another example of the influence and value of the methods pioneered by Autonomy in the mid 1990s.
Stephen E Arnold, November 5. 2016
Self Service Business Intelligence: Some Downers
November 2, 2016
Perhaps I am looking at a skewed sample of write ups. I noted another downer about easy to use, do it yourself business intelligence systems. These systems allow anyone to derive high value insights from data with the click of a mouse.
That’s been a dream of some for many years. I recall that one of my colleagues at Halliburton NUS repeating to anyone who would listen to a civil engineer with a focus on wastewater say, “I want to walk into my office and have the computer tell me what I need to know today.”
Yep, how’s that coming along?
The write up “9 Ways Self Service BI Solutions Fall Short” suggests that that the comment made by the sewage expert in 1972 is not yet a reality. The write up identifies nine “reasons,” but I circled three as of particular interest to me and my research goslings. You will need to read the original “Fall Short” article for the full complement of downers or “challenges” in today’s parlance.
- Hidden complexity. Yep, folks who don’t know what they don’t know but just want a good enough answer struggle with the realities of data integrity, mathematics, and assumptions. A pretty chart may be eye catching and “simple”. But is it on point? Well, that’s part of the complexity which the pretty chart is doing its best to keep hidden. Out of sight, out of mind, right?
- Customization. Yep, the chart is pretty but it does not answer the question of a particular user. Now the plumbing must be disassembled in order to get what the self service BI user wants. Okay, but what if that self service user who is in a hurry cannot put the plumbing together again. Messy, right?
- Cost and scalability. The problem with self service is that low cost comes from standardization. You can have any color so long as it is black. The notion of mass customization persists even through every Apple iPhone is the same. The user has to figure out how to set up the phone to do what the user wants. The result is that most of the iPhone users make minimal changes to the software on the phone. Default settings are the setting for the vast majority of a system’s users. When a change has to be made, that change comes at a cost and neither users nor the accountants are too keen on the unique snowflake approach to hardware or software. The outputs from a BI system, therefore, get used with zero or minimal modifications.
What are the risks of self service business intelligence? These range from governmental flops like 18F to Google’s failure with its fiber play. Think of the inefficiency resulting from the use of business intelligence systems marketed as the answer to the employee’s need for on point information.
When I walk into my office, no system tells me what I need to know. Nice idea, though.
Stephen E Arnold, November 2, 2016
The CIA Claims They Are Psychic
November 2, 2016
Today’s headline sounds like something one would read printed on a grocery store tabloid or a conspiracy Web site. Before I start making claims about the Illuminati, this is not a claim about magical powers, but rather big data and hard science…I think. Defense One shares that, “The CIA Says It Can Predict Social Unrest As Early As 3 To 5 Days Out.” While deep learning and other big data technology is used to drive commerce, science, healthcare, and other industries, law enforcement officials and organizations are using it to predict and prevent crime.
The CIA users big data to analyze data sets, discover trends, and predict events that might have national security ramifications. CIA Director John Brennan hired Andrew Hallman to be the Deputy Director for Digital Innovations within the agency. Under Hallman’s guidance, the CIA’s “anticipatory intelligence” has improved. The CIA is not only using their private data sets, but also augment them with open data sets to help predict social unrest.
The big data science allows the CIA to make more confident decisions and provide their agents with better information to assess a situation.
Hallman said analysts are “becoming more proficient in articulating” observations to policymakers derived in these new ways. What it adds up to, Hallman said, is a clearer picture of events unfolding—or about to unfold—in an increasingly unclear world.
What I wonder is how many civil unrest events have been prevented? For security reasons, some of them remain classified. While the news is mongering fear, would it not be helpful if the CIA shared some of its success stats with the news and had them make it a priority to broadcast it?
Whitney Grace, November 2, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Companies to Watch: Geo-Data Analytics
November 1, 2016
I noted “NGA Chooses 16 Orgs for Disparate Data Challenge Phase 2.” “NGA” is the acronym for the National Geospatial Intelligence Agency. The geo-analytics folks at this unit do some fascinating things. The future, however, demands that today’s good enough is not sufficient. NGA tapped 15 outfits to do some poking around in their innovation tool chests. Here are the firms:
- App Symphony
- Blue Zoo
- CyberGIS
- Diffeo
- Enigma
- Envitia
- GeoFairy
- MARI
- MediaFlux
- Meta DDC
- Paxata
- Pyxis
- RAMADDA
- SitScape
- Sourcerer
- Voyager
Recognize any of these outfit? Familiarity might be a useful task.
Stephen E Arnold, November 1, 2016
Web Marketers: Get Ready for the Google Disruption
October 28, 2016
The GOOG is shifting from desktop search to mobile search. The transition will take time and make life exciting for the Web marketers who have to [a] justify their budgets, [b] generate traffic, [c] keep their jobs. The search engine optimization wizards will be looking a McMansions and BMW convertibles. Business is likely to boom for the purveyors of fairy dust and jargon.
Navigate to “50+ Web Measurement KPIs – Analytics Demystified.” The write up presents four dozen ways to accomplish your objectives. The write up groups the analytics some folks view like the Rosetta Stone. The principal categories are:
- Key Performance Indicators to Measure Return on Investment
- KPIs to Measure Lead Generation Campaigns
- KPIs to Measure Intent to Purchase
- KPIs to Measure Website Engagement
I worked through the long write up, complete with mini MBA comments and screenshots of the magic data. The thought I had was that some folks are reaching for straws to build their career. The number that matters is the revenue produced by a digital marketing program.
Intent? Probably to sell consulting.
Stephen E Arnold, October 28, 2016
Google Introduces Fact Checking Tool
October 26, 2016
If it works as advertised, a new Google feature will be welcomed by many users—World News Report tells us, “Google Introduced Fact Checking Feature Intended to Help Readers See Whether News Is Actually True—Just in Time for US Elections.” The move is part of a trend for websites, who seem to have recognized that savvy readers don’t just believe everything they read. Writer Peter Woodford reports:
Through an algorithmic process from schema.org known as ClaimReview, live stories will be linked to fact checking articles and websites. This will allow readers to quickly validate or debunk stories they read online. Related fact-checking stories will appear onscreen underneath the main headline. The example Google uses shows a headline over passport checks for pregnant women, with a link to Full Fact’s analysis of the issue. Readers will be able to see if stories are fake or if claims in the headline are false or being exaggerated. Fact check will initially be available in the UK and US through the Google News site as well as the News & Weather apps for both Android and iOS. Publishers who wish to become part of the new service can apply to have their sites included.
Woodford points to Facebook’s recent trouble with the truth within its Trending Topics feature and observes that many people are concerned about the lack of honesty on display this particular election cycle. Google, wisely, did not mention any candidates, but Woodford notes that Politifact rates 71% of Trump’s statements as false (and, I would add, 27% of Secretary Clinton’s statements as false. Everything is relative.) If the trend continues, it will be prudent for all citizens to rely on (unbiased) fact-checking tools on a regular basis.
Cynthia Murrell, October 26, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Sugar Polluted Scientific Research
October 19, 2016
If your diet includes too much sugar, it is a good idea to cut back on the amount you consume. If also turns out if you have too much sugar in your research, the sugar industry will bribe you to hide the facts. Stat News reports that even objective academic research is not immune from corporate bribes in the article, “Sugar Industry Secretly Paid For Favorable Harvard Research.”
In the 1960s, Harvard nutritionists published two reviews in medical journals that downplayed the role sugar played in coronary heart disease. The sugar industry paid Harvard to report favorable results in scientific studies. Dr. Cristin Kearns published a paper in JAMA Internal Medicine about her research into the Harvard sugar conspiracy.
Through her research, she discovered that Harvard nutrionists Dr. Fredrick Stare and Mark Hegsted worked with the Sugar Research Foundation to write a literature review that countered early research that linked sucrose to coronary heart disease. This research would later help the sugar industry increase its market share by convincing Americans to eat a low-fat diet.
Dr. Walter Willett, who knew Hegsted and now runs the nutrition department at Harvard’s public health school, defended him as a principled scientist…‘However, by taking industry funding for the review, and having regular communications during the review with the sugar industry,’ Willett acknowledged, it ‘put him [Hegsted] in a position where his conclusions could be questioned. It is also possible that these relationships could induce some subtle bias, even if unconscious,’ he added.
In other words, corporate funded research can skew scientific data so that it favors their bottom dollar. This fiasco happened in the 1960s, have things gotten worse or better? With the big competition for funding and space in scientific journals, the answer appears to be yes.
Whitney Grace, October 19, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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