Simplexity Analytics Framework

Simplexity Analytics Framework

In 1985, Neil Postman published a book ‘Amusing Ourselves to Death’ which provided an interesting juxtaposition between two authors who predicted dystopian futures, George Orwell (with Nineteen-Eighty Four) and Aldus Huxley (with Brave New World).

“We were keeping our eye on 1984. When the year came and the prophecy didn't, thoughtful Americans sang softly in praise of themselves. The roots of liberal democracy had held. Wherever else the terror had happened, we, at least, had not been visited by Orwellian nightmares.

But we had forgotten that alongside Orwell's dark vision, there was another - slightly older, slightly less well known, equally chilling: Aldous Huxley's Brave New World. Contrary to common belief even among the educated, Huxley and Orwell did not prophesy the same thing. Orwell warns that we will be overcome by an externally imposed oppression. But in Huxley's vision, no Big Brother is required to deprive people of their autonomy, maturity and history. As he saw it, people will come to love their oppression, to adore the technologies that undo their capacities to think.

What Orwell feared were those who would ban books. What Huxley feared was that there would be no reason to ban a book, for there would be no one who wanted to read one. Orwell feared those who would deprive us of information. Huxley feared those who would give us so much that we would be reduced to passivity and egoism. Orwell feared that the truth would be concealed from us. Huxley feared the truth would be drowned in a sea of irrelevance. Orwell feared we would become a captive culture. Huxley feared we would become a trivial culture, preoccupied with some equivalent of the feelies, the orgy porgy, and the centrifugal bumblepuppy. As Huxley remarked in Brave New World Revisited, the civil libertarians and rationalists who are ever on the alert to oppose tyranny "failed to take into account man's almost infinite appetite for distractions." In 1984, Orwell added, people are controlled by inflicting pain. In Brave New World, they are controlled by inflicting pleasure. In short, Orwell feared that what we fear will ruin us. Huxley feared that what we desire will ruin us.”

This juxtaposition provides the perfect metaphor for the debate about data in the world right now. Data is being influenced by ‘the three V’s’:

  1. Volume: the sheer amount of data that is being generated digitally at exponential rates
  2. Velocity: the unprecedented rate at which data is moving and being collected
  3. Variety: the vast and diverse types of data that are being generated from different sources

On each succeeding year, we create more data than all of the preceding years of humanity combined. With the growth of global online access, and new networked channels like social media, this data is also becoming a technologists nightmare - unstructured, complex and variable.

You can’t read any sort of marketing prediction article without stumbling upon the apparent holy grail of “Big Data” - a concept that inevitably touches on the fears of Orwell. On the other side is “Small Data”, or the conventional data that every organization should be focused on. Invariably this suffers from the Huxley metaphor, and the threat of drowning in the sea of irrelevance or egoism.

In this essay, I’ll examine some of the defining factors of big and small data, and run through a simple framework to build a digital data ecosystem that can actually deliver actionable results for organizations.

The AARRR Framework: Metrics for Pirates

The AARRR Framework: Metrics for Pirates

I am always on the lookout for good digital frameworks, especially when it comes to metrics, as they really help simplify the complexity of our modern business environment. Complexity can lead to infinite choice, which often leads to paralysis.

Dave McClure’s AARRR framework (or Startup Metrics for Pirates) is an excellent antidote to this situation. The following essay outlines it in detail.

This framework was originally created for startups. A startup is essentially a search for a repeatable and scalable business model, so testing a series of hypotheses about the various parts of the business. It needs to adapt over time, and tell if the business model is worth scaling into a company.

Established organisations already have a repeatable and profitable business model. Any business school will tell you that the numbers to track are Income Statements, Balance Sheets and Cash Flow Statements, however more and more as platforms are becoming the backbone of these organisations, adopting the startup mentality to product is increasingly worth paying attention to.

An example would be a large organisation switching to offer their product through a managed e-commerce website. Traditional activity switches from broad scale mass marketing to targeted and measurable activity on the platform, with new tactics required to evolve to shifting customer needs.

To this end, I’ll phrase this essay to be inclusive of both startups and established businesses who are managing a product - as a definition product will refer to any sort of digital platform (e.g e-commerce website, widget or service or sharing based platform).

Lean User Research: Perfectly Execute the Right Plan

Lean User Research: Perfectly Execute the Right Plan

I recently watched a keynote by Tomer Sharon, UX Researcher on Google Search at the Google I/O 2014 conference entitled 'Perfectly Executing the Wrong Plan'. This talk was so good I have decided to break out the topic and explore it as a framework in this post.

Tomer is at the forefront of what is being called 'Lean User Research', which applies agile methodologies to the existing techniques of User Experience (UX). With connectedness leading to greater and greater complexity, and the need to get products to market rapidly so we can generate insights, it makes sense that we need to start to re-evaluate some of the more traditional UX processes that can often eat up time and resources.

The keynote itself was centred around app development, but this can definitely be applied to any product. It also is more focused on startups, but definitely has applications in all organisations. I'll build the framework around this broader product categorisation.

Perfectly Executing the Wrong Plan

Why do a huge number of products fail in the market? One of the primary reasons can be the fact that the product being built isn't actually solving a problem for the customers, which means that they will ultimately not care about what is on offer.

This leads us to the most important concept - whoever was creating the product failed to fall in love with a problem, which means there was no opportunity.

Paired Metrics

Paired Metrics

There is no question that information is flowing at exceeding rapid rates. This increased complexity is often creating uncertainty, and in extreme cases paralysis for organisations.

We often look for shortcuts or simplifications in this environment, and this is especially true of the data that is being generated as part of doing business. As a result, both business leaders and agencies often try and simplify data into a single key metric to measure goals and performance.

As pointed out by Avinash Kaushik, the danger of this approach is that single metrics can often hide valuable insights, and even worse drive bad behaviour. 

The solution he proposes is to always ensure you partner up your 'golden metric' that is important for the business with a paired metric (in essence acting as a backup and sense check to the data). The goal here is to ensure the partner metric is immediately adjacent or offers contextual value - it should add further insight into the specific primary goal you are focused on. 

Below I'll go through several key paired metrics Avinash outlines as examples. Remember, all businesses are very different, and have largely differing goals, so use these as inspiration.

The HEART Framework

The HEART Framework

As the use of A/B testing and other analysis methods grows, the need for effective metrics becomes more and more important. After all, you don't want to get stuck measuring the wrong thing, or being flooded with too much information that could lead you to paralysis.

Traffic metrics like page views or unique users are a good baseline when applied to websites, however don't often help as much when evaluating specific interfaces.

The UX Researchers at Google (found via Kerry Rodden) have come up with a simple framework to put some more rigour around evaluating the quality of user experience changes, by measuring both the quality and effectiveness against overall goals.  

The framework is divided into two areas:   

  1. The quality of the user experience (the HEART framework)
  2. The goals of your product or project (the Goals-Signals-Metrics process)

Let's take a look.

An Acquisition, Behaviour and Outcome Metrics Framework

An Acquisition, Behaviour and Outcome Metrics Framework

In Digital, often success lies in the ability to reduce complexity, and to focus on the minimal key ideas that can drive the biggest impact. We have abundant choice, but as we know from the science behind decision making, this choice can in fact lead to paralysis.

Finding the elements to drive focus requires an understanding of the business, a knowledge of what is possible, and the ability to balance current objectives with the future in mind - not an easy task, but all the more difficult without a solid base of data.

The always intelligent Avinash Kaushik has a simple framework to try and reduce the complexity around digital marketing metrics by providing a simple view top line view for decision makers. Let's break it down.

Acquisition, Behaviour and Outcomes (ABO)

The full spectrum of the digital customer journey involves three very top line steps - how we acquire traffic or customers, their behaviour once they land on our platforms, and the business outcomes that we generate as a result of this. While different agencies or departments may be focused on individual parts of this journey, we need to create a framework that takes a view of all three together - we often need to break the heavy silo focus that can exist in many organisations and their relationships with partners.

Analysis of the full and complete customer journey is the key to success.

Building an Analytics Infrastructure

Building an Analytics Infrastructure

Why is it that so many digital marketing initiatives fail? There are always excuses ("the banner ad wasn't creative enough", "the people involved were idiots", "the MD killed it before it could get off the ground"), but the root cause may in fact be a lack of an effective measurement plan to allow the identification of campaign or platform success.

In an attempt to make this process simple, Google have developed a framework to try and simplify the process of developing a solid digital analytics infrastructure in any business.

Why are Analytics important?

There are two trends driving the need for digital analytics to be a high priority.

The first is access; the Internet has now made the world's information available to nearly everyone at the click of a button. To add to this, mobile has created the ability to connect to everyone 24/7.

With this access, the consumer journey has forever changed. People are now empowered with information - they can read product reviews, get expert opinion, connect with friends for recommendations, check store inventory, or look up competitive pricing instantly from the device in their pocket.

The second trend is the invention of cloud computing, creating cheap, nearly infinite computing power. This trend has now empowered organizations to collect and analyze more business data than ever before

See-Think-Do: A Digital Marketing Framework

See-Think-Do: A Digital Marketing Framework

Digital is adding a huge amount of complexity to marketing. While there have been a number of mental models thrown together to try and help simplify this experience, by far the best one I have seen recently is the See-Think-Do framework developed by Google Evangelist Avinash Kaushik.

The purpose of this model is simple:

  1. To generate a much simpler view of your digital marketing efforts.
  2. To provide a sense check against measurement strategies.
  3. To Identify any gaps in activity that could be utilised to generate higher profits.

The Customer Journey to Online Purchase

The Customer Journey to Online Purchase

With the growth of online commerce, the path or journey undertaken by a customer to lead to a purchase decision has become increasingly complex. To use an analogy used by Nielsen in their 2013 Australian Connected Consumer Report, when you think of the landscape as a Rubik's cube, consumers rarely fit on one side; they fit across multiple blocks and their path isn't finite.

The result of this is that marketers need to start to rethink the way they view the customer journey in order to get a more accurate picture of their marketing efforts.

So while we understand that marketing channels such as email, display, paid search and social influence customers at different points, they in fact can also influence each other, meaning we shouldn't judge them in isolation.