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Archive for the ‘Interactive marketing’ Category



Posted on November 19th, 2008 by jguevara

I recently attended a panel discussion in New York on media fragmentation consisting of media agency execs including:

- Bant Breen (Interpublic - Initiative – President, Worldwide Digital Communications),
- John Donahue (Omnicom Media Group - Director of BI Analytics and Integration),
- Ed Montes (Havas Digital - Executive Vice President),
- Tim Hanlon (Publicis - Executive Vice President/Ventures for Denuo)

The discussion was kicked off of by Brian Pitz, Principle of Equity Research for Bank of America.  Brian set the stage for a spirited discussion regarding the continuing fragmentation of online media along with research on the issues posed by this.  The panel discussion touched upon many issues including fear placement around unknown user-generated content, agency lack of skill set to address this medium and lack of standards.  However, what surprised me most was the unanimous consensus in opinion that there is more value further out on “The Tail” of the online publisher spectrum due to the targeted nature of the content.  Yet the online media buying statistics conflict with this opinion (over 77% of online ad spending is still flowing to the top 10 sites).

When asked “why the contrast?” between their sentiment and the stats, the discussion revealed the level of uncertainty due to a lack of transparency into “The Tail”.  Despite the 300+ ad networks that have emerged to address this very challenge, the value chain lacks the data to confidently invest the dollars.  In addition, there was a rather cathartic moment when John Donahue professed that agencies should “Take Back Your Data From Those that Hold It Hostage”.

It is our belief that the opinions expressed by the panel serve as evidence of a shift towards a new era in media where evidential data will drive valuation across media rather than sampling-based ratings acting as the currency.  No one will be immune from this:

- Agencies need it to confidentially invest their clients dollars and show demonstrable ROI of their services
- Ad networks need it to earn their constituencies’ share of marketing budgets
- Ad networks need it to defend the targeted value and the appropriateness of their collective content
- 3rd Party measurement firms (comScore, Nielsen Online, ValueClick) need it to maintain the value of their objective value
- Advertisers need it to support the logic budget allocation decisions
- BIG MEDIA needs it to defend their 77% stake

You might be thinking, “The need for data is no great epiphany”.  However, I submit that the amount of data and the mere fact that all participants should have their own copy is a shift in thinking.  Gone are the days where:

- The value chain is driven solely by 3rd Party’s and their audience samples
- Ad Servers/Ad Networks are the only keepers of the data
- Service Providers can offer data for a fee

Posted on November 6th, 2008 by Mayank Bawa

I was at Defrag 2008 yesterday and it was a wonderful, refreshing experience. A diverse group of Web 2.0 veterans and newcomers came together to accelerate the “Aha!” moment in today’s online world. The conference was very well organized and there were interesting conversations on and off the stage.

The key observation was that individuals, groups and organizations are struggling to discover, assemble, organize, act on, and gather feedback from data. Data itself is growing and fragmenting at an exponential pace. We as individuals feel overwhelmed by the slew of data (messages, emails, news, posts) in the microcosm, and we as organizations feel overwhelmed in the macrocosm.

The very real danger is that an individual or organization’s feeling of being constantly overwhelmed could result in the reduction of their “Aha!” moments - our resources will be so focused on merely keeping pace with new information that we won’t have the time or energy to connect the dots.

The goal then is to find tools and best practices to enable the “Aha!” moments - to connect the dots even as information piles up on our fingertips.

My thought going into the conference was that we need to understand what causes these “Aha!” moments. If we understand the cause, we can accelerate the “Aha!” even at scale.

Earlier this year, Janet Rae-Dupree published an insightful piece in the International Herald Tribune on Reassessing the Aha! Moment. Her thesis is that creativity and innovation - “Aha! Moments” - do not come in flashes of pure brilliance. Rather, innovation is a slow process of accretion, building small insight upon interesting fact upon tried-and-true process.

Building on this thesis, I focused my talk on using frontline data warehousing as an infrastructure piece that allows organizations to collect, store, analyze and act on market events. The incremental fresh data loads in a frontline data warehouse add up over time to build a stable historical context. At the same time, applications can contrast fresh data with historical data to build the small contrasts gradually until the contrasts become meaningful to act upon.

I’d love to hear back from you on how massive data can accelerate, rather than impede, the “Aha!” moment.

Aster Defrag 2008 97
View SlideShare presentation or Upload your own. (tags: systems data)

Posted on October 26th, 2008 by Steve Wooledge

In a down economy, marketing and advertising are some of the first budgets to get cut. However, recessions are also a great time to gain market share from your competitors. If you take a pragmatic, data-driven approach to your marketing, you can be sure you’re getting the most ROI from every penny spent. It is not a coincidence that in the last recession, Google and Advertising.com came out stronger since they provided channels that were driven by performance metrics.

That’s why I’m excited that Aster Data Systems will be at Net.Finance East in New York City next week. Given the backdrop of the global credit crisis, we will learn first-hand the implications of the events in the financial landscape.  I am sure the marketing executives are thinking of ways to take advantage of a change in the financial landscape, whether it’s multi-variate testing, more granular customer segmentation, or simply lowering the data infrastructure costs associated with your data warehouse or Web analytics.

Look us up if you’re at Net.Finance East - we’d love to learn from you and vice-versa.

Posted on October 6th, 2008 by Steve Wooledge

Aster announced the general availability of our nCluster 3.0 database, complete with new feature sets. We’re thrilled with the adoption we saw before GA of the product, and it’s always a pleasure to speak directly with someone who is using nCluster to enable their frontline decision-making.

Lenin Gali, Director of BI, ShareThis

ShareThis logo

Lenin Gali, director of business intelligence for the online sharing platform ShareThis, is one such friend. He recently sat down with us to discuss how Internet and social networking companies can successfully grow their business by rapidly analyzing and acting on their massive data.

You can read the full details of our conversation on the Aster Website.

Posted on October 6th, 2008 by Mayank Bawa

It is really remarkable how many companies today view data analytics as the cornerstone of their businesses.

acerno logoaCerno is an advertising network that uses powerful analytics to predict which advertisements to deliver to which person at what time. Their analytics are performed on completely anonymous consumer shopping data of 140M users obtained from an association of 450+ product manufacturers and multi-channel retailers. There is a strong appetite at aCerno to perform analytics that they have not done before because each 1% uplift in the click-through rates is a significant revenue stream for them and their customers.

Aggregate KnowledgeAggregate Knowledge powers a discovery network (The Pique Discovery™ Network) that delivers recommendations of products and content based on what was previously purchased and viewed by an individual using the collective behavior of the crowds that had behaved similarly in the past. Again, each 1% increase of engagement is a significant revenue stream for them and their customers.

ShareThis logoShareThis provides a sharing network via a widget that makes it simple for people to share things they find online with their friends. In a short period of time since their launch, ShareThis has reached over 150M unique monthly users. The amazing insight is that ShareThis knows which content users actually engage with, and want to tell their friends about! And in its sheer genius, ShareThis gives away its service to publishers and consumers free; relying on delivering targeted advertising for its revenue: by delivering relevant ad messages while knowing the characteristics of that thing being shared. Again, the better their analytics, the better their revenue.

Which brings me to my point: data analytics is a direct contributor of revenue gains in these companies.

Traditionally, we think of data warehousing as a back-office task. The data warehouse can be loaded in separate load windows; loads can run late (the net effect is that business users will get their reports late); loads, backups, and scale-up can take data warehouses offline –which is OK since these tasks can be done on non-business hours (nights/weekends).

But these companies rely on data analytics for their revenue.

·    A separate exclusive load window implies that their service is not leveraging analytics during that window;
·    A late-running load implies that the service is getting stale data;
·    An offline warehouse implies that the service is missing fresh trends

Any such planned or unplanned outage results in lower revenues.

On the flip side, a faster load/query provides the service a competitive edge – a chance to do more with their data than anyone else in the market. A nimbler data model, a faster scale-out, or a more agile ETL process helps them implement their “Aha!” insights faster and gain revenue from a reduced time-to-market advantage.

These companies have moved data warehousing from the back-office to the frontlines of business: a competitive weapon to increase their revenues or to reduce their risks.

In response, the requirements of a data warehouse that supports these frontline applications go up a few notches: the warehouse has to be available for querying and loading 365×24x7; the warehouse has to be fast and nimble; the warehouse has to allow “Aha!” queries to be phrased.

We call these use cases “frontline data warehousing“. And today we released a new version of Aster nCluster that rises up those few notches to meet the demands of the frontline applications.

Posted on June 11th, 2008 by Mayank Bawa

I had the opportunity to work closely with Anand Rajaraman while at Stanford University and now at our company. Anand teaches the Data Mining class at Stanford as well, and recently he did a very instructive post on the observation that efficient algorithms on more data usually beat complex algorithms on small data. He followed it up with an elaboration post. Google also seems to believe in a similar philosophy.

I want to build upon that observation here. If you haven’t read the posts, do read them first. It is well-worth the time!

I propose that there are 2 forces in action that help simple algorithms on big data beat complex algorithms on small data:

  1. The freedom of big data allows us to bring in related datasets that provide contextual richness.
  2. Simple algorithms allow us to identify small nuances by leveraging contextual richness in the data.

Let me expand my proposal using Internet Advertising Networks as an example.

Advertising networks essentially make a guess about a user’s intent and present an advertisement (creative) to the consumer. If the user is indeed interested, the user clicks through the creative to learn more.

Advertising networks are used today on a CPC model (Cost-Per-Click). There are stronger variants of CPL (Cost-Per-Lead) or CPA (Cost-Per-Acquisition) but these variants are as applicable to the discussion as the simpler CPC model. There is a simpler variant of CPM (Cost-Per-Impression) but an advertiser ends up effectively computing CPC by keeping track of click-through rates for money spent via the CPM model. The CPC model dictates that Advertising Networks do not make money unless the user clicks on a creative.

Today, the best advertising networks have a click through rate of less than 1%. In other words, advertising networks correctly interpret a user’s intentions 1% of the time, 99% of the time they are ineffective!I find this statistic immensely liberating. Here is a statistic that shows that even if we are correct 1% of the time, the rewards are significant. ☺Why is the click-through rate so low? I think it is because human behavior is difficult to predict. Even sophisticated algorithms (that are computationally practical only on small datasets) do a bad job of predicting human behavior.It is much more powerful to think of efficient algorithms that execute across larger, diverse datasets to exploit the richness inherent in the context to enable a higher click-through rate.I’ve observed people in the field sample behavioral data to reduce their operating dataset. I submit that a sample of 1% will lose the nuances and the context that can cause an uplift and growth in revenue.For example, a Content Media site may have 2% of their users who come in to read Sports stay on to read Finance articles. A sampling of 1% is certain to reduce this 2% population trait to a statistically insignificant portion in the sample. Should we or should we not derive this insight to identify and engage the 2% by serving them better content?Similarly, an Internet Retailer may have 2% of their users who come in to buy flat-panel TV have also bought video games recently. Should we or should we not act on this insight to identify and engage the 2% by offering them better deals on games? Given that games are a high-margin product, the net effect on revenue via cross-sell could be higher than 2% in dollars.We often want to develop an algorithm that is provably correct under all circumstances. In a bid to satisfy this urge, we restrict our datasets to find a statistically significant model that is a good predictor. I associate that with a purist way of algorithm development that was drilled into us at school.Anand’s observation is a call for practitioners to think simple, use context and come up with rules that segment and win locally. It will be faster to develop, test and win on simple heuristics than waiting for a perfect “Aha!” that explains all things human.

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