Do content marketing reruns work?

I’m glad to be back from vacation after a week completely off the grid. Talk about a drastic change in lifestyle, going to a place where devices don’t even work (thus removing the temptation to “just check in”). I recommend it heartily.

Before I left for vacation, I thought I’d run an experiment using reruns on social media to power my social media postings for the week. Instead of my normal routine of a new blog post each day plus a welcome message (2 links back to my website per day), I went to five reruns plus a welcome message (6 links back to my website per day). Each rerun was a link back to a past popular post of mine from the past two years.

Now, going into this, the logical hypothesis would be a 300% increase in website traffic, right? I literally tripled the number of direct links back to my website. In fact, it should be even more, because my audience has changed and grown in a year. Last year on Twitter alone, I had 7,000 fewer followers:

Followers_-_Twitter_Ads

So with an audience that’s bigger and triple the number of links, let’s see what the results were:

All_Traffic_-_Google_Analytics

Cue the womp womp trumpet, please. Yes, folks, you read that correctly. I had 43% LESS traffic this year compared to the same calendar week the previous year. The traffic source that drove the loss? Organic search traffic, where I had half the visitors from last year.

It’s been shouted far and wide that Google loves relevance, freshness, and diversity of content. Re-runs with no new content paint a bulls-eye on your butt for freshness and diversity, and in the world of the content shock, someone will always be creating more relevant content today than content you made a year or two ago.

The bottom line? Re-runs didn’t work for me in this particular test case. My site took a beating on organic search traffic by my taking my foot off the gas for a week. Does this mean re-runs won’t work for you? Of course not – as always, you need to test for yourself. However, go into that test with a modified hypothesis, now that you’ve seen at least one test case where the result fell far short of the hypothesis.


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Play to your strengths or mitigate your weaknesses?

When you’re thinking about business strategy, one of the most basic questions you have to confront is whether to play to your strengths or mitigate your weaknesses.

Playing to your strengths at first seems like the logical answer. Do what you’re best at. Push the envelope. Hit your numbers hard.

By contrast, mitigating weaknesses doesn’t seem like it improves very much in the short term. Sure, you’re less bad at the things you’re bad at, but forward-moving progress isn’t always obvious.

Here’s why both matter. Playing to your strengths is great in the short term, but ultimately your strengths have a limit on them. At a certain point you have growth-limiting factors. This is true in so many areas. It’s true in macroeconomics – structural problems can be masked with economic stimulus for a little while, but eventually stimulus stops working. It’s true in marketing. Your marketing will only succeed as well as the weakest link in the chain; poor customer service or a bad product can nullify even the best marketing program. It’s true even in something as simple as running. The weakest part of you – nutrition, equipment, health, form – ultimately inhibits the strongest part of you.

Those weaknesses create a ceiling that limits your strengths. They cap your growth. Only by removing those weaknesses can your strengths continue to flourish.

This is what it might look like, visualized:

IMG_1429

Here’s the challenge: many weaknesses take a long time to overcome, longer to overcome than playing to your strengths at that moment. The sooner you can start raising the weakness cap, the sooner your strengths can shine to the greatest extent possible.

Ultimately, the answer comes down to balance and careful measurement. Once you start to see the hints of a plateau in your strong metrics, start shifting focus and resources to remediate your weakest metrics.


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Cherry picking your marketing data

Over the holiday weekend, I had a chance to bring a statistics aphorism to life, as I went cherry picking at a local farm. If you’re unfamiliar with the expression, cherry-picking one’s data means selecting only those case studies or data points that reinforce your point, while ignoring the rest. This expression never made a ton of sense to me until I actually went cherry picking.

IMG_9194Believe it or not, half of these cherries aren’t ready to eat.

Here’s why it now makes sense. Cherry trees have a wide, wide spectrum of fruit ripeness. At any given time, on any given tree that is in season, about 5% or so of the cherries will be picture-perfect, ready to pick and eat. About 20% are reasonably close to ripe, but might need a few more days to mature. 5% or so will be past ripeness and on the way to rotten. 10% will inevitably be partially eaten by pests. The remainder will be in various stages of ripening but nowhere near ready to eat.

From a statistical perspective, if you wanted a true understanding of a tree’s ripeness, you’d randomly pick cherries from it and get a wide selection of cherries at various stages of ripeness. If, however, you wanted a more practical, more useful harvest, you’d only pick the ones that were ripe or near ripe, even though your harvest would be statistically non-representative of the tree as a whole.

Cherry picking one’s data isn’t universally bad, however. It’s bad if what you’re after is statistically representative data. It’s good if you only want to look at certain pieces of data. For example, while understanding where your entire marketing database is in terms of readiness to purchase is important, cherry-picking only those prospects who are close to buying or ready to buy makes logical sense from a resource management perspective. You want your sales and marketing efforts to focus first on those opportunities that are most ripe before they cross into overripe (and likely buy from someone else).

Understanding what your end goal is – statistically valid representation or the best of the best – will help you to understand whether cherry-picking your data is a bad or good choice.


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