Getting Run Over By a “Paid For” Truck

I stared at the email and fumed. What do you mean we lost the business?

This wasn’t driven by pride or ego. We really HAD provided them our best price, quoting a pricing level where we should have been assured of victory. This proposal was offered at break-even pricing to protect other business in the account. On a generic spec, commodity grade product. Where we had the lowest cost in the wholesale industry.

And we had just… been owned. Not merely losing the business, but getting buried.

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Strategic Flaws of Statistical Price Targets

These days, everyone in the analytics space has a pricing product to talk about. Toss enough transaction data points at the problem, you’re going to get a few insights that may help you earn higher margins. Even better, moving the pricing process to a modern software tool often improves your control over pricing errors and sales rep concessions.

Can this work? Absolutely. For an organization with weak pricing practices, the first wave of pricing optimization often yields brilliant results.  For a typical manufacturer or wholesale distributor, this can raise your net profit margin by one to three percentage points. Given the low margins in many of these industries, this is a meaningful improvement in the overall profitability (and thus, valuation) of the business.

But you need to have a careful plan for Act II. There are limits to how far you want to ride this horse.

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Investing In Existing Websites: Growth and Failure Rates Three Years After The Auction

Several years ago, we completed a website revenue study focusing on small websites. The goal of this study was to understand how much a typical site should make. We accomplished this by looking at 100 websites listed for public auction at a major site. We recently ran an update on this study to understand how the sites were doing today.

This update measured two things:

  • What % of the sites were still online and indexed by Google?
  • Has the site gotten better or worse since the sale (using competitor analysis tools)?

Of our original 100 sites, 93 were in a position to be effectively tracked using 3rd party competitive research tools such as SEMrush. The remaining sites got a hall pass; these had been executed as confidential sales (no site name), where we vetted the site anonymously and were unable to find them for a follow-up. Since a basic due diligence review was run on every site included in the study, we are comfortable that each of the sites was operating as a legitimate business at the time of the sale – although many were “window dressing” from a SEO and traffic generation perspective to boost their statistics.

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Accelerating Analytics: Decrease The Cost of Asking Useful Questions

After twenty years in the business, I am giving up on the idea of asking brilliant questions. They don’t exist. Ironically, most of the questions which have delivered serious money in the past tended to look like relatively dumb ones…

The first set of significant wins I had in my analytics career was in direct marketing, where I moved the campaign analysis process for a $5MM/year program in-house. From a technical perspective, this was pretty straightforward: write a SAS program to merge our mailing list with our customer file then aggregate response and sales data. Since a common key existed on both files (finders file number), it was a simple matter to join the files and summarize the data into an Excel Pivot table. Intern level stuff.

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Linkedin Endorsements: How Might They Affect Linkedin’s Search Algorithm?

Linkedin rolled out their one-click endorsement feature this past month. As I’ve traded clicks with friends and colleagues, I’ve been trying to figure out what their real goal is. On the surface, this feature feels redundant with their existing “recommendation” feature. Given their aggressive efforts to promote this new feature, the data they are gathering is clearly important to the development of the algorithms behind their services – but how?

Linkedin has been fairly quiet about the inner workings of their search engine. This is likely to prevent people from manipulating the results, since there is significant value in being on the first page of a Linkedin search for a lucrative professional skill. They share some basic pointers about how to “be visible” on their help page. Key points from their page:

  • There is no single rank for Linkedin Search – results are unique to each user/query
  • The profile keywords of both parties (searcher, results) play a significant role
  • Rankings are adjusted based on how prior searchers have reacted to your profile

While the above metrics are fine for identifying which candidates are relevant to a search, they don’t rate candidate quality: who actually knows their stuff? What’s missing here is a broader assessment of “page trust” (graph model analysis concept) that candidates possess the skills that they reference on a profile. For example, Google’s search algorithm incorporates an evaluation of the credibility of a site using link patterns, brand signals, and social activity. With these new features, it looks like Linkedin may be trying to adapt Google’s Pagerank algorithm (or something similar) to ranking candidates for specific skillsets.

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Escaping The Walled Garden of Enterprise Analytics: Using R and Python For Data Analysis

In which an experienced analytics guy advises the younger generation to leave the walled garden of enterprise analytics tools and learn how to write code using a real programming language. Specifically advocating the use of R and Python for data analysis and related programming. But hey, I’m flexible on that point…

The use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offense.

– Dijkstra

I was taught a long time ago in some Management 101 course to sandwich constructive criticism between two compliments. So I’ll open with this statement:

SAS and the other BI vendors have done a nice job of bringing statistical computing techniques within the reach of the typical college graduate.

Now pull up a chair and grab yourself some popcorn, since I’m going to bite the hand that fed me for the first half of my career. I spent the first seven years of my career in roles involving significant usage of SAS and a variety of drag & drop query tools. The COBOL of the analytics world.

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The Online Marketing Sanity Test: Is Your Website A Waste of Money?

‘Cause only one thing counts in this world: get them to sign on the line which is dotted.

– Glengarry Glen Ross

Here’s one simple question that will identify about 85% of the completely worthless website and content development efforts out there:

Does Google show your site to people who don’t already know your name?

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Website Revenue Models: Real Revenue Statistics For Small Websites

Driven by a passionate desire to “scratch our own itch”, we released the first draft of a little project we’ve been working on this weekend. As our regular readers are aware, we built our first public website earlier this year. We started running ads earlier this summer (just to pay the server bills) and wanted some perspective on “what good looks like”. This is where things get furry: there aren’t any reliable and comprehensive public sources on revenue benchmarks for a small website. So we decided to build our own data set and create some benchmarks around how much a small website could earn. Here you go…

As you can see below, profit-per-visitor numbers vary widely. More after the jump…

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The Best Course I Ever Took: Combinatorics Problems

I sat quietly in the chair, number six or seven on George’s agenda of young hopefuls. The on campus recruiting process was a brutal cattle call. On the positive side, we had a large number of great companies coming to visit. Unfortunately, the vast majority of my 1500 classmates were tipped off about their arrival. Moo!

His eyes skimmed down my resume – and locked onto a phrase halfway down the page.

“Combinatorics Problems? What the heck is that?”

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The Addictive Nature of Real Time Analytics and Website Monitoring – Is Less More?

I spent most of my twenties managing direct marketing campaigns for the financial services industry, where we would release several multi-million dollar programs per year in the hope that they would generate enough customers to justify our salaries. Measuring and predicting the performance of these campaigns was a simple statistics problem – given X expectation and Y performance Z effective days since delivery into the campaign, estimate the difference between actual results and expected results.

The tricky part was getting Z (effective days since delivery) right early in the campaign, since you were trying to predict how fast a slug of bulk mail moves through the postal service system. This was subject to all kinds of minor disturbances such as holidays, weekend timing, and potentially even how your letter shop sorted the file for production. This was before the Internet-based tracking systems we have today, so we were very old school and did a lot of estimation.

As the program manager, I cultivated this Zen state for the first couple weeks of a major program. We knew there was a zone of uncertainty: the most rational response to any developments was “wait and see what happens next”. While we could make good predictions several weeks into a campaign about the outcome, it was actually better not to look at anything from a strategic perspective for the first couple of weeks.. There was just too much noise to get a good read on the signal.

Flashing forward to the present, we put up our first website (a little AJAX based word game solver)  a couple of months ago and had to keep tabs on what was happening.

Read moreThe Addictive Nature of Real Time Analytics and Website Monitoring – Is Less More?