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Why you should question everything

One day, Tiger Woods was playing in the British Open, up near Liverpool. He was teeing up on the 1st tee and he heard a man tutting in the crowd. “Looks easy,” he heard the man say.

Tiger went up to the man and said, “You think this is easy?” The man offered to give it a go.

Tiger handed over his driver and the man asked, “Now what do I do?”

“You need to hit it 400 yards up towards the flag.”

The man took a swing and the ball flew up the fairway, on to the green and rolled to within an inch of the flag.”

Tiger was amazed. He invited the man up to the green. When they got to the ball, Tiger said, “Now, you need to tap the ball into the hole.”

The man complained, “Why didn’t you tell me that back there?”

Question everything

One of my mantras is “question everything”. Albert Einstein once said, “If I had an hour to solve a problem and my life depended on the solution, I would spend the first 55 minutes determining the proper question to ask, for once I knew the proper question, I could solve the problem in less than five minutes.”

When it comes to digital marketing, or any form of marketing, asking questions is vital. Decisions based on assumption could send you down the wrong path.

Consider analytics, for example. A huge collection of data that you can slice and dice every which way is marvellous. Is it any good, though, if you don’t know why you need it or what you are looking for?

In her article on the CMI website, Analytics For Intelligent Content: Are You Asking The Right Questions?, Marli Mesibov says, “A screen full of analytics data looks like a secret code, and in a way it is. That data has a lot of information in it, and it’s impossible to make sense of it without the key. Put another way, data can give answers, but only if you ask the right questions.

The best way to kick off analytics for any site is to determine the site goals or the team’s questions about the site’s audience or performance. Once the questions and goals are identified, the team can set up an analytics program, such as Google Analytics or Omnigraffle, to pick up specific data that will address those goals or questions.

What do people want to buy?

Data can be flawed too, don’t forget. If you don’t collect it properly, you get incorrect information. Many companies use surveys to gather quantitative data to answer questions. That data can be flawed if you ask the wrong question.

For example, did Whiska’s cat food really prove in a survey that eight out of ten cat owners said their cats prefer Whiskas? Sounds unlikely to me. This is the line that inspired the name of the popular celebrity panel show.

Engineering the answer with a trick question

If you ask a double-barrel question, you will skew the answer. For example, “Would you like to leave the EU and have Nigel Farage as your Prime Minister?”

Many people who want the leave the EU may vote no, because of the Farage option. Survey results could then say that most people want to stay in the EU.

This happened with the alternative vote referendum. Many people in the UK would like to reform the electoral system to use a two-tier process, a proportional representation method, or a multi-round affair. I would also like to see the ability to vote at both local and national level, to avoid having to select a Prime Minister through a vote for a local party representative.

In 2011, the Conservative Government agreed, at the insistence of its coalition partners the Lib Dems, to hold a referendum to ask the public if it wanted to change the voting system. Except it didn’t ask that question. The question was two-pronged with only one alternative option.

At present, the UK uses the “first past the post” system to elect MPs to the House of Commons. Should the “alternative vote” system be used instead?

Almost 68% of the voters said no. But what were they saying no to? Changing the system at all or accepting the alternative vote method? It was a clever tactic by the Conservatives to make sure the result didn’t change anything.

If you don’t question statistics and results, you could easily believe the spin that comes with them.

When is a 50% increase not good?

Getting back to marketing, let’s think about good versus bad stats? “This month we have sold 50% more widgets than we sold last month.” Sounds good, but what if, last month, we sold 80% less widgets than the month before? What if this month’s big spike was due to a huge order that won’t be repeated?

When looking at marketing stats (traffic, leads, sales etc), it’s always good to look at different time lines. Numbers in an isolated point in time may not reflect a long-term trend.

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