Category Archives: Analytics

Analytics

Data analytics and the future of digital marketing

big-data-marketing-trendsLast week I visited with fellow technologist and Big Data evangelist Flavio Villanustre at LexisNexis. During the visit we discussed advances in data-driven marketing, a topic that I’ll be covering with a panel of experts at the upcoming OMMA Atlanta conference.

Here are some of the big takeways from our conversation:

  • The biggest challenge that marketers face today is having access to data. The more data one has, the better data models one can build. And better data models drive better predications. Marketers must take every opportunity that they are given to collect and share data.
  • The role of unstructured data (eg; photos, videos, audio) in data analysis will increase over time. For example, Google announced in 2012 that researchers used 1,000 computers to find cats in pictures. The impressive thing about this finding is the ability of computers to identify a particular object with accuracy without human intervention. This level of machine learning demonstrates that computer-enabled data analysis is something that we can take advantage of in the not-so-distant future!
  • We are recognizing the value of predictive analytics. While descriptive analytics, or the collection of basic metrics (eg: visitors, page views, leads, likes, +1′s, pins, etc.), is important to understand what’s happened in the past, companies want to leverage data to predict the future and drive more revenue/increase profit.
  • A very small number of companies worldwide (only 3%; according to Gartner Research) are beginning to use prescriptive analytics. Prescriptive analytics is a complex type of predictive analytics that allows one to test out multiple marketing models. It provides an optimal solution given a set of objectives, requirements and constraints. This is where say a company can test the impact of various promotions/discounts and shipping rates on a customer’s purchasing behavior.
  • Marketers have to accept the successes (and failures) of data-driven decisioning. We hate to turn decisions over to a computer because we believe that we’re smarter — we’re human after all! Unfortunately, computers typically beat our “gut feel” — our intuition is just inherently faulty (according to HBR). Marketers need to accept that we’re biased and that we must adopt a “test and optimize” processes where the answer from one set of marketing experiments inform the next set of experiments.

It is easy to see how these advances in marketing — whether it is through the introduction of marketing technology platforms, data collection practices or data analysis processes. While it is tough to predict the future (thanks Yogi Berra), I believe that marketers that ignore data-driven decisioning are poised to lose (in the long run).

How retailers can beat Amazon’s anticipatory shipping

amazon-shipping-boxLate last week, Amazon made headlines (again) for receiving a patent on a method that predicts the eventual shipping destination of a product (see coverage on TechCrunch). The proposed method enables Amazon to leverage various data points to begin shipping an item before it is actually ordered — something that is both creepy and cool! In a way, it isn’t any different than the big data solutions that the NYPD is actively using to fight off crime or what top national universities use to recruit students.

While it may seem somewhat futuristic, it actually isn’t much different than a grocer watching the weather and ordering extra beer in anticipation of a run before a snowstorm. For Amazon, it is also the natural extension of their Subscribe & Save Program which gives customers a 15% discount on regularly purchased products, such as toilet paper, cereal, and dog treats. As this program enters its third year, Amazon is poised to not only see an incremental improvement in operating costs through real-time ordering/inventory management but also to prevent others from taking this same path.

As some retailers may be concerned that their competitive advantage is slipping while data-driven organizations like Amazon are gaining marketshare, the reality of the situation is that retailers aren’t that far behind. They simply need to initiate a “test and learn” methodology where there’s a focus on measuring an outcome through data collection and analysis. As part of a holistic data strategy, retailers can collect and stitch together information from several good data sources. For example, retails can understand:

  • Shopping intent by collecting web traffic data on product reviews, weekly circulars, online shopping lists/wish lists.
  • Buying patterns by collecting in-store transactional data (along with loyalty program data).

The key is for retailers to define a strategy!

This also reminds me of a recent slide that I came across from a Big Data presentation by McKinsey that showcases the profitability that UK-based Tesco experienced over the past 15 years:

tesco-profitability-and-big-data

It seems that the inflection in profitability didn’t occur until a significant investment was made in data collection/analysis.

For retailers looking at this graph, it is telling a story that there’s no better time than now to make such an investment.

What additional steps do you think retailers should take to secure a foothold in the marketplace?

Email analytics and Gmail image caching: the 5 things you need to know

Gmail EmailGoogle stirred up a hornet’s nest recently when it announced that Gmail now cached images within emails. While image caching improves the user experience, the email marketing community scrambled to understand how caching impacts image downloads, which serve as the mechanism for tracking email open activity. After reviewing the responses from several notable email service providers, including Campaign Monitor, Constant Contact, ExactTarget, MailChimp, and Responsys, it appears that the overall change is positive for email marketers.

Here are the 5 things that you need to know:
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