Analyzing the Success of the 33 Sticks Remote Team

33 Sticks CEO, Jason Thompson, sat down with the team from Workfrom to answer questions about the the company’s success and to share his thoughts about best practices for distributed organizations. Read the full interview here: https://workfrom.co/magazine/story/analyzing-success-33-sticks-remote-team  ...

33 Sticks Welcomes Joe Orlet

A decade ago I started working in Digital Analytics. During this time I worked with brands such as MasterCard, Home Depot, HP, Hasbro, and McDonald’s. Along with working across multiple market verticals as a consultant, my digital analytics tenure also includes experience as a product end-user and employment with a product vendor. Over the years, I observed a number of Digital Analytics efforts become mired in a cycle of implementation and re-implementation. Reasons vary between companies, however, mitigating the impact falls upon the the Digital Analytics Professional. While not applicable to every situation, I submit two general guidelines to follow: advocate implementation simplicity, target project self-sustainability.   Implementation Simplicity Implementation simplicity applies across many fronts, from eliminating overlapping products to collection of necessary data. When defining requirements consider not only business value, but implementation and maintenance cost. The more complex an implementation, the likelihood of full deployment decreases, in turn, the cost of maintenance and mistrust of data increases.   Project Self-Sustainability Partnering implementation simplicity is project self-sustainability. While complete self-sustainability is an infrequent occurrence, the product of the goal is often worth the investment. Foremost, clearly document the business logic around data collection and error handling. This business logic allows breaking away from the spreadsheets full of URLs and corresponding variables.   Self-sustaining implementations forgo rigid spreadsheets, rather relying upon rules defining variable structure and format. Often with the addition of simple programmatic logic, implementations adapt as digital properties change. While relinquishing a certain level of control, it allows the implementation a safe degree of self-management.     Trying to incorporate these two basic guidelines often deliver a...

Adobe Analytics Amazon Alexa Skill

If you are already familiar with the Adobe Analytics APIs then creating an Adobe Analytics-Amazon Alexa Skill is a pretty simple process.     Building Alexa Skills with the Alexa Skills Kit STEP 1: Create a New Skill If you haven’t already, your first step is to create an Amazon Developer account. Once you have an account, then you can create your first skill. From the Alexa Skills area, click ‘Add a New Skill’ STEP 2: Skill Information Name: This is the name of your Skill should you choose to deploy it to the Alexa App Store. Invocation Name: This is the name a user will use to call your Skill e.g. Alexa ask Adobe…. Application Id: This is a unique identifier for your application and is use as a check, within your endpoint script (more on that later), to ensure that the service calling your script is your Alexa Skill and no one else.   STEP 3: Interaction Model   The Interaction Model defines how users will interact with your skill. Intent Schema The Intent Schema is a JSON Object that defines the key words and phrases that are spoken by a user and how it maps to your Skill. For this example, we are building a very basic skill that simply accepts a reporting time period as an input. We accomplish this by defining an intent, GetDate, and a Slot that will hold the key reporting time period values. The Slot is defined using a name, ReportDate, and in this instance a list of valid dates for the Skill defined as LIST_OF_DATES.  Custom Slot Types In this example, we are using a custom type...

ObservePoint: How to Sell Your Boss On a Digital Data Layer

What is a Data Layer?   For many, the concept of a data layer is still elusive—and for those who grasp it, there is always more to learn. At its most basic form, a data layer is a layer of programming language that contains various types of information including online user behavior data, mobile app activity, transaction information, etc. A data layer sits between the user interface and the numerous applications required to run your site or mobile app and transfers the user activity data from the user interface to those vendor applications. Read the full post here:...

Jason Thompson, Co-Founder of 33 Sticks, to Present at ObservePoint’s 2016 Analytics Summit

SILICON SLOPES, Utah – November 2, 2016 – ObservePoint is pleased to announce that Jason Thompson, Co-Founder at 33 Sticks, will be speaking at the first annual Analytics Summit, hosted by ObservePoint. Thompson will be discussing a recent hot topic in the analytics industry: how to convince your boss of the need for a data layer. Read the full Press Release here:...