Performance Firestarters 8: Data, Real-Time and Creativity


I think the relationship between data and creativity is one of the most interesting dynamics in our industry right now. Some parts of our sector are arguably under-estimating the role of ad tech and the potential impact of an ever-increasing role played by algorithms, others are seemingly under-playing what Tim Leake calls the 'art and craft of creating an unreasonably powerful piece of behavior-changing advertising'. Yet there is surely plenty of significant opportunities in how data can inform creative, how real-time and personalisation can enhance customer experience, and even for how creative can become highly adaptive at scale.

So for our eighth Performance Firestarters will be focusing on these challenging, but fascinating questions and as always we have a great line up of speakers. Since we've had such success in previous Firestarters with our short, punchy talk format, we will have five talks of no more than 15 minutes each that will come at the subject from a variety of perspectives.

Will Sansom, Director, Content & Strategy at Contagious will begin by talking about whether one day, we might see algorithms running agencies. Simon Andrews, from Addictive, will be talking about automating creativity at scale, Katherine Maryon from Periscopix and Alex Emberey from Ladder Digital will be giving their perspective on the new ways in which they're using data, algorithms and automation with creative, and author and content marketing specialist Jon Burkhart will be speaking about the power of real-time content. It's going to be a truly thought-provoking but fun evening.

The event takes place on July 9th, 6.00pm at Google Central St Giles, London. As always I have some free passes to give away to readers of this blog so if you'd like one, message me direct or leave a comment below.

A Five Stage Model for Digital Maturity

‘Digital maturity’ is another one of those phrases that has lost some of its meaning through over-use but since so many companies are undergoing some form of digital transformation I do think it useful to have some kind of framework for development. So what might the progression in digital competence look like?

The four stages of competence model (or ‘conscious competence’ learning model) used in Psychology to describe the psychological states involved in various stages of learning might be a good place to start. The model is typically used in the context of positioning individual learning and attainment of skill, but I think it is just as applicable to how an organisation (for a company is but a group of people) might learn and improve capability. Applied in the context of digital transformation, it progresses from unconscious incompetence through to unconscious competence thus:

Unconscious incompetence

At this initial stage, the organisation (or in the original model the individual) is not only unaware of how to effectively deploy digital technologies, they are also blissfully unaware of what they don’t or should know. In order to move on to the next stage, the company needs to first recognise the value of digital transformation, and the degree of stimulus or impetus to learn determines the amount of time spent at this stage.

Conscious incompetence

The company may not fully understand digital or how to deploy it but they are at least aware of their short-comings and of the value in developing new competencies to address the deficit. Experimentation and learning from failure becomes an important part of the learning process at this stage.

Conscious competence

By this stage, the company has developed competency in digital, but deployment or execution requires conscious effort, focused involvement, and likely planned, definitive steps.

Unconscious competence

A true ‘digitally-native’ organisation. Digital becomes second-nature, executed easily, intuitively, allowing for greater efficiency and capability in execution. 

What I like about this model is that it progresses from unconscious (we don’t know what we don’t know) to conscious (we know what we do know and what we don’t know) to innate (we know it so well it is intuitive). 

The Dreyfus Model for skills acquisition takes this a stage further, with five distinct stages of learning - novice, competence, proficiency, expertise, and mastery. From the Wikipedia entry:

“In the novice stage, a person follows rules as given, without context, with no sense of responsibility beyond following the rules exactly. Competence develops when the individual develops organizing principles to quickly access the particular rules that are relevant to the specific task at hand; hence, competence is characterized by active decision making in choosing a course of action. Proficiency is shown by individuals who develop intuition to guide their decisions and devise their own rules to formulate plans. The progression is thus from rigid adherence to rules to an intuitive mode of reasoning based on tacit knowledge.”

In this model, the same progression from unconscious incompetence to unconscious competence takes place, and as the company (or, in the original model, the student) becomes skilled, there is less dependency on abstract principles and more on real-world experience. So in the context of digital transformation, the stages of competence look like this: 

1. Novice 

The company follows established rules, including those that have been created for specific circumstances without any contextual adaptation, and feels no responsibility for outcomes

2. Advanced beginner

As experience grows, new 'situational' elements come into play meaning that rules can be applied to specific and related conditions. But decisions are still made through the application of rules, all aspects of work are treated separately, and with little prioritisation or responsibility taken.

3. Competence 

At this stage the numbers of rules increase, perhaps to the point where we need to adopt organising principles, or specific and particular perspectives. So relevance of information becomes more important, decision-making then becomes active, planning more deliberate, and so implying more responsibility for choices and decisions.

4. Proficiency

By now, the company is able to take a more holistic view of a situation, better understand contexts, prioritise the importance of particular aspects, note deviations from norms, employ guiding principles and adapt to the situation at hand. Diagnosis of situations is becoming more intuitive but conscious decision-making is used in the formulation of plans, and real-world previous experience can inform decisions. 

5. Mastery/Expertise

Digitally-centric approaches are second-nature, there is a strong vision of what is possible, there is an intuitive grasp of situations based on deep, tacit understanding but analytical approaches might be used in new situations or contexts. Decision-making is intuitive, with no need to deconstuct situations into discrete elements to understand them, the company does what works, pattern recognition might extend to the plan as well as the diagnosis.


Stubborn on Vision, Flexible on Details

Jeff Bezos once said:

“We are stubborn on vision. We are flexible on details…. We don’t give up on things easily. Our third-party seller business is an example of that. It took us three tries to get the third-party seller business to work. We didn’t give up.” 

If you’re not stubborn, you’ll give up on experiments too soon. And if you’re not flexible, you’ll pound your head against the wall and you won’t see a different solution to a problem you’re trying to solve.”

Many organisations are now recognising the value in more iterative, experimental, adaptive ways of working in response to rapidly changing competitive contexts, customer expectations and technologically-driven possibilities, but this can frequently raise questions around direction. If we spend all our time iterating our way towards our future (so the question goes), where is our strategic direction? And how can we make the kind of creatively-driven leaps forward that change the game and enable us to leap-frog the competition? 

The answer to this lies in the right balance between the directional guidance given by a compelling longer-term vision and the flexibility enabled through highly adaptive and responsive approaches and ways of fulfilling that mission. Iteration and experimentation without vision is chaotic. Rigidly pursuing a plan without adaptiveness leads to declining performance, missed opportunities, limited learning. Stubborn on vision, flexible on details. 

Bake a Bigger Pie

In the Q & A following her wonderful talk at Gooogle Firestarters last Wednesday, Sue Unerman drew from this quote, taken from Guy Kawasaki's book Enchantment:

“There are two kinds of people and organizations in the world: eaters and bakers. Eaters want a bigger slice of an existing pie; bakers want to make a bigger pie. Eaters think that if they win, you lose, and if you win, they lose. Bakers think that everyone can win with a bigger pie.”

What a fantastic analogy.

The Importance of Reflection


Consistently building in reflection time at the end (or indeed in the middle) of projects is something that most companies rarely do well and often end up considering as something of a luxury. In the rush of the day-to-day we get very good at being relentlessly forward focused, immediately moving on to the next thing, seldom taking the time out to pause and really understand what happened and why, and how it might be done better next time.

And yet given how important developing a learning culture is now for just about every business, it’s surely something we should all be doing more of. There's some well known examples of companies that have been able to create the space for employees to explore new ideas (Google 20% time of-course, 3M's Time to Think, GDS's FirebreakFacebook's Hackathons, Spotify's regular hack days etc) but in the age of continuous experimentation it's also about reflecting on what we're learning as we go along. I like the way that Pinterest, for example, go to great efforts to embed reflection time in their culture and practice so that it becomes a habitual way of gathering learning as they go.

One of the simplest, and therefore the best, frameworks that I've come across for this is the so-called ‘after action review’. It originated in the US Military who would use it in their de-briefs as a way of improving performance, and features four simple questions that can be answered after an action of some kind:

1. What did we expect to happen? Knowing that you have to answer this question afterwards means that you go in with a greater clarity of objective and desired outcome.

2. What actually happened? A blameless analysis, that identifies key events, actions and influences, and creates a consensus

3. Why was or wasn't there a difference? What were the differences (if any) between desired and actual outcomes, and why did this difference occur?

4. What can you do next time to improve or ensure these results? What (if anything) are you going to do different next time? What should you do more of/the same/less of? What needs fixing? What worked and is repeatable or scalable? The idea is that at least half of the time of the review should be spent answering this question.

Sounds obvious. But then the most useful things often do.