Tag Archives: analysis

NCDM Notes: Pragmatic Analytics

Presented by Portrait and AAA South

Overwhelming amounts of data combined with rapid growth (especially in unstructured data) makes our job difficult: difficult to gather isight and difficult to implement chages once we have insight.  We also need to make sure we are solving the right problem, and not waisting time on results which are not actionable.

Analysts need to minimize time required to understand and prepare the data: tasks that are necessary before any analytics or modeling can be started. 

There are a couple of solutions:

First, create analytical tables that summarize and aggregate data that is now manually pre-processed.  AAA builds a big table that aggregates data at the household level.  It icludes activity summaries (could be transactional and promotional), prefereces, and  demographics.  For AAA this includes about 150 colums, with some colums updated weekly and some monthly.

AAA uses SAS to create this analytical table, but Portrait could do this too.  AAA is moving towards having Portrait create the analytical table.  This table is kept on its own server that the analytics department controls, unlike the primary DB surver (DB2), which MIS controls.

This more structred data table allowed AAA to help managers do some analytics theselves.

Second, use tools that ease the analytical and modeling process.  AAA uses Portrait software.  The output AAA uses is a matrix of various segment intersections, which simplifies the understand of how each segment will perform; it shows how each element in the matrix will perform relative to the norm.

These two efforts shortened AAA’s modeling process from 5 months to 2 months.

Take-away: Modeling can be easy.  You only need the right structure and tools.

Dinner with Portrait Software

I had the great fortune last night to be invited to dinner by Portrait Software last night.  This is a great group of guys, smart and fun, and if you are considering marketing optimization, analytics, or campaign management software it is worth looking into their suite.

The trip to the Forge, in Miami Beach, was something to remember.  The poor driver had no clue how to get there, so three people immediately whipped out their iPhones to provide directions.  Unfortunately, the iPhone presented to the driver had the wrong address, so we ended up in some industrial area of Miami instead of at a restaurant in Miami beach.  It was a rather hilarious combination of a clueless driver and conflicting GPS advice.

When we got to the restaurant, we were seated around this massive table and in 10 foot tall, 4 foot wide, white leather, wing-back chairs.  These things were so heavy that the staff had to help you push them up to the table.  Once in, you were pretty much stuck.

The chairs also made it a challenge for the great service staff to serve the food and drink.  You”d be talking to somebody and suddenly see a waiter’s arm reach out between two chairs to poor wine, but you’d never see his body.  The contrast between the waiter’s black outfit and the white chairs made this even more comical.

Anyway, the Forge has atmosphere and attitude seeping out of its pores.  I ate too much but drank just the right amount (yeah, right).

NCDM Notes – Target Customers Effectively Through Advanced Analytics

Presented by Intuit & Netezza (a data warehousing company recently purchased by IBM)

The timeliess of integrating data into your marketing process is critical since data ages much more quickly than in the past.

Random note: Intuit is a significant shift from desktop software to SaaS tools, even for their financial products.

Intuit uses the usual variety of channels to acquire customers, but they have found paid ads work best for them.  They also have the usual problems of allocation and balancing spending across channels.  So what data can they use to help with this?  They use DoubleClick to track all converters and non-converters. (Does DoubleClick uses cookies and analytics to attribute customers to marketing efforts. No, it sounds like DC only provides the data, which goes in the Netezza warehouse.)

Omniture provides the data once a person is on their site.  Intuit uses Omniture only to provide data, which it passes to their data warehouse; they don’t use any of the Omniture analytical tools.  Omniture also reads the DC cookie so it can make the link between internal and external data.

With all the data showing what channels (aka media: online ads, organic search, ppc, affiliates, emails) a person was exposed to, they can then do the analysis to allocate the order. 

Intuit also looked at “channel interference” to see if one channel detriments another.  They saw only only a 3% occurrence of an affiliate click happening before a PPC click.  There was more of an overlap in the other direction (ppc click then affiliate) but it was still not at a concerning level.

The key point is that with prospect/exposure level data, they can do the analysis to see what channels or media overlaps.  (This is pretty exciting stuff!)

Behavior of customers is fairly consistent.  If they come in via a paid ad, they are likely to come back in via a paid ad.  If they come in via a specific PPC term, it is likely they’ll come back via that same term.  This suggests there is little cannibalization in the online channels.  (But what about canabilization between online and offline media.)

Renewal efforts at Intuit tanked for a short while when they showed returning customers only high-end products.  When they re-introduced the complete product line, including the free version, response bumped back up.

Since most of the discussion was regarding online marketing and attribution, I asked about their offline efforts and how they dealt with this cross-channel attribution.  The use customer unique vanity URLs (www.intuit.com/victornuovo, for example), which then gives them the data they need to align online and offline data.