Wednesday, June 4, 2014

Holistic Analytics

Good old days

In the good old days everything was much easier... Before the time of social and mobile marketing, the era of Youtube advertising or remarketing it was simpler to set up goals and achieve them. Our goals were much less complex as all we wanted is more clicks or better position and lower cpc. If we were very innovative we tried to get more conversions with a lower cost. We had been living in a siloed data world, where the different marketing tools such as SEO, or PPC or email marketing were treated as stand-alone instruments which hardly correlated with each other.

The job was straightforward and even with limited knowledge or experience one could provide briliant results since most of the competitors were even less knowledgeable... Our reporting methodology adopted to this simplified world and we used mostly one metric KPI-s or basic segmentations to find insights, e.g.: Where are the most visitors coming from? What keyword do they use? Which is the best converting ad? Should I use landing page A or B?
Yes, we had also much more limited resources and tools to extract out insights but the truth is that most of the marketers were not able to ask the right questions. We focused on data, and were able to understand what happened in the past, but very rarely were we able to answer the 2 most important questions:

Why did it happen?
What should I do now, how should I use this information to use in everyday work?
That was the age of innocence in some way as we didn’t know what we didn't know and therefore couldn’t even conceive how simplified this thinking is...

End of the age of innocence

A few years later we began to understand the true complexity of digital marketing. As more and more tools, targeting options and enhanced reporting functionality became available every day we had to handle a large amount of data making our life far more difficult than before.
To understand the difference just think about your own behaviour as a customer: before a shopping decision how do we perform research and how many stages do we go through before conversion?

To illustrate the point let's have a look at the “typical” conversion path from top conversion path report:

A bit scary... And we haven't even looked at the following questions yet:
  • Which interactions happened from mobile?
  • Which paths brought the highest conversion value?
  • Which campaign, ad, landing page was effective in first interaction and which in the last
It all seems a bit hopeless and overcomplicated... but we have to be able to answer the most important questions at least:
How can I precisely identify my customers, i.e. each stage of the conversion funnel, and use the appropriate tools to target the right message to the right person at the right time?

And this is EVEN much more difficult than it looks ..

Wrong question - wrong answer

You have heard the saying: garbage in garbage out. What this means is that if the initial question or hypothesis is wrong one can not gain any meaningful information from it (and let’s forget about the bottom-line measurement problems, where data is actually not accurate in the system, because this is a topic of another post).

As marketers you may not know at all which metrics are available in Analytics, Adwords or Facebook Insight but if you can’t ask the right questions there is no analytical marketing or data science expert who can provide you the right answers...

How to ask the right question? Depends on what is your goal!

Do you need more new customer on a low CPA?

  • Concentrate on the new visitors and analyse the top converting visitors’ behaviour by segments that makes sense - time, device, medium landing page, ad, targeting, etc..
  • Try to understand where is the gap: you target the wrong people or use the wrong message or can’t engage them with the content or is there a huge abandonment at the end of the shopping process?

Do you have enough customer but need to increase the average order value?

  • Explore the characteristics of the best customers and the “savvy” ones..Which parameters are the differ from each others? Do they click on different messages? Do they start from different landing pages? Is there any common segments by device? Do people spend more on weekends or working days, morning or evening, etc?
  • Try to set up more hypothesis and test them: e.g. female spend more than male, low-quality smart phone visitors bounce, or should we focus on the cart abandonment rate, because we lose significant money there and this is easy to fix..

Do you have enough new customers, but customers are not loyal and bounce after the 1-2 purchase?

  • That requires quite a different strategy as the previous ones.You don’t have to persuade them to trust in you, because you already did..That means that you can exclude some of the potential problems (e.g. they can’t navigate the site) but have to look for other ones..
  • Why do they don’t return? Did a new competitor entered the market, who seduced them? Were they satisfied with the product/service or not? Did they have problem with customer care? Do you provide the sufficient information for them about new offers? Is your social marketing engaging and positive, which is able to interact with your customers and fans?
  • And we could continue so on..Don’t hesitate and look for the “perfect” hypothesis, but start to test and DO something.. Based on the above example, if you write on 10 types of similar hypothesis (female/male, new/returning, desktop/mobile converting best, or shopping cart navigation is confusing etc) it’s very easy to prove or refute them with one easy report. If you see any significant deviation in the data you can dig deeper as we showed in the last posts - how to turn data into actionable insight..

Don’t judge too fast

On one hand it is very true that many companies fail because of the strive for perfection..They want to plan at the first stage the ideal model and they mixed up, lost in the details and at the end they don’t do anything...But on the other hand there is also a very typical problem that marketers judge by one metric and conclude false consequences too fast..without attempting to be comprehensive just a few example for it:

Judge only by campaign metrics

Mobile campaign doesn’t work for us, we stopped it, because there was no conversion.
Yes it was true, but they have forgot to test the landing page, which was “not served” on mobile..After fixing this problem mobile campaign resulted very effective..

Judge a tool by poor deployment level

This is very common and could citate many examples, but 2 simple one:
Content network bring no conversion - yes, if you are B2B software company and use one general text ads, with “SMB” keywords targeting solely it won’t..But if you use the advanced targeting methods provided by AdWords and combine it with high-quality visual ads, set-up frequency capping, remarketing and enhanced bidding it will :-)

Judge by last click conversion only, instead of analyzing the whole customer journey

Few years ago we have seen only last click conversion, but now we have great reports for checking multichannel conversions and comparing different attribution models. If you see that a general campaign (or social marketing or etc.) perform poor in last click conversion, before you stop have a look at the first-click conversion performance of it..It might serve excellent as first interaction, although after 3-6 visits the final decision is made by a very specific campaign or by direct visitors..

Judge and decide without testing

At last my favourite: we don’t advertise for these keywords, because we are on top places in organic and therefore it’s unnecessary waste of money..
If I ask, whether you have tested it, the answer is no..Without going into details, the fact is, that almost 100% of cases if we advertise for these keywords and are there also in organic AND the paid top positions, our conversion metrics will improve..It results more conversion at a lower conversion cost.


Be aware of the complexity of digital marketing and try to understand your customers as deep as possible
Identify key problems and set up hypotheses for the reasons
Test basic hypothesis and look for significant deviation, if you find one dig deeper, look for the “why” and the “what to do now” instead of the simple what

Never forget about your goals:

  • Try to understand your customers as deep as possible - send the right message, to the right people, on the right device at the right time
  • Measure marketing effectiveness every stage of the customer lifecycle process and work out the appropriate KPI’s for it
  • Create and develop an integrated marketing strategy
  • The goal of Analytics efforts is to make data actionable and usable in everyday tasks

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