Epic fail: imagination & data - Part 2

Take a look at part 1 for what inspired this piece...here are some issues and solutions.

Where is your imagination?

It all comes down to when and how one asks “what if” – booking.com may or may not be doing it wrong. And apparent success then masks failure – making £100m can conceal the fact that you could be making £200m.

For example, I was talking to a senior manager at an online gaming company the other day. What he said was highly telling:

We have loads of data on how people use our product and what they want from it. We still dont understand our consumer.

However, he has no traction within his organization because they’re making enough profit that there’s insufficient motivation to really engage with their consumers and reach their true commercial potential.

They’re not looking in all the right places, or in the right ways. All the talk about data and being consumer-responsive rings hollow. Shareholders suffer, but in ways that are not visible to them.


Give me imagination!

You have to remember where decisions are made to get to a campaign based on data:

  1. Data: To get a data set you have to decide what to collect, from whom, from where and how.
  2. Information: You then have to decide how to convert a mass of data from raw data into usable information.
  3. Insight: A further decision needs to be made on how to interpret the information to provide insights on which you can act.
  4. Strategy: You then need to decide exactly how to act on that insight.
  5. Create: You must execute the strategy in an engaging way.[1]

Imagination is required at all 5 stages. Pepijn’s piece seems to indicate that it is only being used from stage 2 – too late if you got it wrong at stage 1. That seems to be the failing of the gaming company too. There could be other failings, but let’s be generous to both of them.

You cannot be serious

You may think this sounds obvious. I certainly think so. Unfortunately, in my experience, the Bill Walsh group-think quote applies to significantly more marketers than not. They are, therefore, failing to deliver full value to clients/shareholders.

We hate seeing this and would love to fix it. Doing that is very difficult, and to illustrate, here is a final example based on our efforts to help a major global retailer to escape the data-fail trap:

Retail Giant thinks its core consumer is young, that they understand their consumer and appeal to them successfully. Their team of, almost exclusively, middle-aged white men achieves this, they believe, because they have a range of data on the consumer group from one of the big research companies. All their insights, strategies and campaigns seem to line up to the data.

Unfortunately, when one looks at the data critically, it is weak and does not support any significant conclusions. It was collected without imagination. The insights are then generated from a poor foundation, and also appear to be generated without any real imagination. Even the best strategy and creative work cannot make up for these shortcomings.

Several efforts to suggest alternative approaches, however positively expressed, are met with resistance from the brand and marketing teams. At one point we even received the response that, to paraphrase, we do not understand their business.

The kicker? Retail Giant consistently reports a loss in the UK.

I know what Bill would say. I don’t know what to do. Answers on the back of a postcard (or email, or in the comments).

[1] I put this together with a data science-loving friend who works at a competitor agency, but is getting so frustrated with colleagues and clients and wishes to remain unnamed. That’s how delusional

Adam Papaphilippopoulos

Partner, Reluctantly Brave