Using Little Data to Create Better Content

“Product Managers often orchestrate the exchange of ideas, conduct collaborative brainstorming sessions, and ensure that vital data reaches its destination, broken down into what we call Little Data, the understandable, actionable molecules.”  – @NikkiElizDemere Source

I read this quote and I liked it a lot. Partly because I’m a product management geek and partly because I’ve been thinking a lot about how ‘data’ and analytics fit into marketing and content.

At school I liked both English and Maths. I liked English because I could let my ideas run away with me, writing until my hand cramped up and everything became illegible. It was cleansing and yet exhilarating. Then, I loved going to Maths.

Maths was the satisfaction of finding the right answer. Of being right or wrong with no shaded area in between.

In marketing the two converge more often than you might expect and again, I kind of think they’re a great match.

The ‘English’ is the creative part. The marketing campaigns, the ideas, the words and the execution. The maths is what you gain from it, how you test and measure and how you make it even more successful the second time around.

The key way to achieve the merging of the two is through ‘little data’ – the little nuggets of information that guide us in marketing towards the next article we should write, or the next headline we should try.

Big data vs little data

Big data sets are important in marketing and business generally. But for marketers and those working in SMEs, start ups etc. is it often the little data that helps us become more actionable in the day-to-day.

Little data connects people with small, timely insights. Some of the sources you can use to capture little data include:

  • Social media analytic packages and tools (Buffer, Sprout Social, Brandwatch etc.)
  • Google Analytics
  • CRM (Salesforce, Hubspot)
  • Email analytics (Mailchimp, Totalsend, Campaign Monitor)
  • Marketing automation and customer service tools (Intercom, Drift, Zendesk)
  • Billing packages (Recurly, Stripe, Chargebe etc.)
  • Your own Excel monitoring spreadsheets (I have lots)

If you use any of these tools in your marketing stack, you’re already capturing little data. The next step, is to understand it.

Understanding little data

The difference between big data and little data, as I understand it, is that the latter combines what you know from your metrics and what this means for your KPIs.

With every piece of content you produce the little data needs capturing and analysing before you can decide on whether it was a success. When this doesn’t happen (and often it doesn’t) content is sent out into the ether and is never looked at again.

This is the problem when ‘PR’ or ‘blogwriting’ contracts ask for X amount of articles per month as a marker of success. This is great for quantity and aspects such as back links, SEO etc. but it doesn’t mean anything when it comes to seeing what works and reiteration for future campaigns. You can achieve 100 PR articles per month but if you’re not looking at your web traffic to see referrals and conversions or seeing who’s talking on social media, you can’t really see if your money is worth spent.

A good use case for little data is social media. If you schedule 20 tweets per week and it takes you 2 hours, how do you know how well those two hours have been spent? Here’s how I would go about finding out:

Set KPIs – before you begin sending tweets you need to work out the aim of your Twitter campaign. Only then, can you set KPIs and begin using little data to measure them. For this purpose, let’s say your aim is to get more blog traffic.

Optimise for results – before you begin using little data to track the results against the KPIs I would first use it to try and optimise for your best case results. For example, if you schedule tweets with Buffer, you can begin to look at which tweets get the most click-throughs.

Buffer's most clicked tweet

From this, you can see if there are any obvious trends between content type, timing of tweets, hashtags used and click through rate. Once you have this you can create a series of ‘ideal’ tweets that you know get great results. Or the ability to schedule your tweets within set times and formats and so on.

Analyse effect – now that you know your tweets are working potentially as effectively as they could be, you can look at whether they’re fulfilling your KPIs. From Google Analytics you can review how much traffic you are receiving from social media and the amount of this that is coming from Twitter. You can even look down the funnel to see how much of this is converting to leads/sign-ups/conversions. You can also look back at your scheduling tool and see how many click-throughs you’ve had from individual content, rather than from Twitter as a general channel.

In conclusion – data is everything

Data on its own is useless. To go back to my Maths analogy, random numbers don’t mean anything alone. You have to put them into formation and make them do something together before they mean anything or apply themselves to any type of use case.

Once you get into the habit of using little data, you begin to make connections between the numbers and your creativity. You begin to be much more effective at content marketing.

The little data becomes stepping stones to the articles people want to read, the tweets they want to click on and the campaigns that actually lead to conversions.

Also, data is everyone’s job

Just because you’re a marketer, content writer or creative, data is still your responsibility. Only through this mindset can you create better content. It’s how you go from just writing blogs to becoming a content marketer who can transform a brand and get a company known for really great storytelling.

And once you’ve mastered the little data? The big data is only a formula away.

If you’re interested in drilling down into this topic more, let me know! I’d love to discuss how little data can be captured and analysed in more detail.