— hideh

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Studying online behavor starts with my own. At least for me, using my own online activity allows me to get a sliver of understanding of how companies market online. It is by no means a lens into the vast world of online marketing, but for someone who has grown up with digital around me, there must be a few others who can relate. How does behavior translate to effective marketing? That’s something online marketers have been trying to quanitfy since online advertising started in 1994.

Web based advertising started with a 468×90 pixel banner ad from AT&T in 1994 on HotWired.com (a favorite of mine at the time, it was the digital magazine of Wired, which also discussed the original banner). Back then, there were no analytics to track click throughs, no ad server networks, and no marketplace (selling was done manually). This is what the banner ad looked like:

While there are no analytics to back this up, online discussion estimated around 70% click through rates for the initial banner ads. Fast forward 17+ years later. Clickthrough rates of 0.3% (or less) are more the norm (that’s 3 people of 1000 viewers). As a comparison, email marketing has response rates of 2% to 12%. I’m accustomed to ignore any ad on a browser or on an app on my mobile device. However, I do find value when I use a geo specific app such as Foursquare or a hyperlocal blog showing ads from local businesses or a brand that is interacting with customers on social media sites. I’m more inclined to support a brand that personalizes and communicates directly with their customers.

Google Research released a paper last week titled “Measuring Ad Effectiveness Using Geo Experiments”. In these experiments, a region (e.g. country) is partitioned into a set of geographic areas, which are called “geos”. These geos are randomly assigned to either a treatment or control condition and geo-targeting is used to serve ads accordingly. The experiments then measured the impact of advertising on consumer behavior (e.g. clicks, conversions, downloads, etc.). Its conclusion is that measuring effectiveness is a challenge, but geo experiments “can be applied to measure a variety of user behavior” and don’t require the tracking of individual user behavior over time and therefore avoid privacy concerns that may be associated with alternative approaches.

This tells me there are too many variables to quantify advertising effectiveness, which is where we started with in the 1994 AT&T banner ad. So how does a brand or business measure effectiveness when someone like me ignores online ads? Stop obsessing over click throughs and response rates for banners and email, but rather find out what customers are asking for. These values can be used as indicators to compare the success of similar online advertising programs with similar marketing schemes, but rather than using them as a measurement for purely ‘ROI reasons’, it’s more important to build relationships with those who are supporting the business with a mix of tactics. Measuring brand building exercises are difficult to measure, so think of how you build your real life relationships. Reach out via social media, respond to emails and thank them from time to time. How do you connect with others, and share stories relevant to them?

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We know that the use of analytics for business is the key to unlocking its data’s value. Getting actionable data translates to positive business outcomes can be used to reduce operational efficiency, meeting compliance requirements, or anticipating market needs. But the complexity of large business data can make it difficult to unlock the value.

Data can exist in various formats in various servers, files, hosting providers, and data warehouses. This also implies that an integrated data warehouse already exists. Even if there is an existing warehouse, shifts in business strategy or the use of technology can create disperate data locations that can’t be used in a data analytics platform. This is something I experienced as a corporate CIO. We knew there was a great amount of information we could use to better serve internal and external customers, but compiling the data into a usable report took too much time and was not on-demand. We did not lack good tools or the talent to generate insight. We had the support from BI vendors such as Oracle, Microsoft, SAS, and others. And our team was one of the best in the business. But the complexity and shifting requirements (either by the business or compliance reasons) added complex variables into the equation. Additionally, unstructured data didn’t necessarily ‘fit’ into our data warehouse for analysis.

In an Internet startup, analytics is essential as well, but is easier in the sense that there are no legacy systems, historical data, and outdated models that need to be pulled together. Jason Goldberg, CEO of Fab.com, posted his thoughts on how their data was used to raise $40million. Data analytics providers such as RJMetrics (customer acquisition metrics and customer lifetime value), SEOmoz (SEO data), Salesforce (CRM data), and Google Analytics (visit data, referrals, and traffic) can provide value for much less implentation costs.

Combining data from these services, they can provide a good view of where the business is now. While easier to build in analytics from the beginning, you still need to know what you’re looking for and how to set goals for your business. Without those, you as the visionary CEO won’t know where you’re paving the road towards so your team can get there.

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Here’s an interesting HBR article in the December 2011 issue on targeting customers at the right moment across the right channel using predictive analytics.

I like one quote in the article, because it’s true for me and probably many other parents:

“Clubcard shoppers who buy diapers for the first time at a Tesco store are mailed coupons not only for baby wipes and toys but also for beer. (Data analysis revealed that new fathers tend to buy more beer, because they are spending less time at the pub.)”

Combining social, mobile, and location based data is a fairly new concept for retailers, but can be a powerful target for customers’ needs. The use of analytics can uncover so much more insight on customers’ lifestyles to anticipate their needs.

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