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?

Getting Gmail under control: an email intervention

2911924363_e9cd37dbd8_mIt is frustrating that my personal email inbox is so out of control! It wasn’t like this in the beginning of 2013, when I had only a handful of unread messages. But by last week, I counted more than 2,000 unread messages in my inbox. While I could have just taken the “nuclear” route and deleted everything in sight, I decided on an alternative, yet still aggressive, approach to get my inbox under control.

I tackled this project in phases. I assumed that the clutter was primarily driven by the influx of commercial email: I subscribe to a variety of daily deal and technology alerts, weekly industry updates, and other monthly email newsletters. So I started with an inbox review to determine who’s the real culprit behind the clutter.

Phase 1: The inbox analysis.
I’ve been using Gmail for 9 years and while it is overall robust, Gmail does not possess some basic functionality like sorting by sender. Thankfully I found an online tool from MIT Media Lab called Immersion that visualizes your email metadata (aka, the to/cc/bcc/from information) over time. The app generated the following graph:

Each circle in the graph above represents a sender. And the line between each circle represents senders that were frequently copied on the same email.

A quick look at this graph revealed that the majority of emails were either from my wife (she’s the red circle at the center of the image above) or the people in my sons’ cub scout pack. Since both were important messages, I realized that I’d have to adjust my purge strategy by introducing a selective automation step.

Phase 2: Inbox automation.
My goal for inbox automation step was to presort and reroute emails as they hit my inbox. To accomplish this, I took advantage of the basic email management concept behind Gmail:

An incoming email message appears in the inbox by default because it is automatically tagged with the Inbox label. This label remains associated with the message until the message is archived, regardless of whether is is read (or not).

This meant that if I wanted to keep non-critical unread emails out of my inbox, I would have to strip the Inbox label and replace it with an alternate one. So to reroute emails, I created a few basic labels:

tags-list

The Bills/Buy label was created for any paperless bills (that needed to be paid) and any items that I emailed myself that I wanted to buy. The Orders/Subscriptions label was created for any confirmation or shipping notices for online order, or online services that I signed up for. The Cub Scouts label was created for scout-related emails. And finally, the Research label was created for any alerts/newsletters that I received on a regular basis.

The second step was to create “filters” that apply the right label to each incoming emails. Here’s a filter for my daily emails on ad agencies:

gmail-filter-example

The filter both removes the Inbox label from the email and applies the Research/Agency label to the message. NOTE: Even though the Inbox label is stripped, the message remains unread!

While most emails were sorted by the sender, emails for items that I wanted to buy were not easily identifiable. So I decided to tackle that issue by taking advantage of the disposable email address trick in Gmail. This trick allows me to append a plus (“+”) sign and any combination of words or numbers to my current username. I added the word “+buyit” to my Gmail address (aka, [email protected]) and then created a filter based on this address to reroute these emails to my Bills/Buy list.

Phase 3: The big purge.
Next, I went through my entire inbox and selectively deleted any unread messages that were more than 30 days old. When I came across a message that I knew I received on a regular basis, I would stop and perform an advanced search using the “from:” operator and the sender’s name. This allowed me to find all emails from a particular sender and delete them in one swift swoop.

Within a few hours, I knocked my 2,000+ unread email count down to 4 unread emails.

Phase 4 (BONUS): Unsubscribe!
As I previously mentioned, I subscribed to a variety of email lists and some were no longer relevant. As I purged emails from my inbox, I went back to and also unsubscribed from those messages that were basically junk mail. For a few that were the email simply recapped the new articles on the website, I opted for the RSS feed. That way, I could catch up on these when/if I had time.

Conclusion
Since implementing these steps, I have been able to keep my inbox down to 2 unread messages. And I resolve to adjust these in order to keep my inbox under control throughout 2014. I’ll keep you posted on my success.

How are you keeping your inbox under control? Please use the comments below to share your thoughts.

PHOTO BY Domenico / Kiuz