The explosion of connected smart phones, tablets and desktop computers has made the individual web user more connected than ever, enabling marketers to monitor and collect engagement information at every touchpoint of the purchasing funnel.
At the same time, the evolution of cloud technology and near-infinite computing power connected through real-time networks, has made data collection more attainable for more businesses. The amount of data available to marketers now is unprecedented, but what is it for? How is it useful?
The author of Web Analytics 2.0, Avinash Kaushik, once gave a brilliant definition of digital analytics:
“Digital analytics are the analysis of qualitative and quantitative data … to drive a continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes (both online and offline).”
He goes on to say that only with the right skills, processes and technologies, can you uncover the vital information about the effectiveness of your engagements and turn them into business results. In this post we’re going to deconstruct this definition and explore some challenges it highlights:
One of the most important steps, and usually the first step in any marketing activity, is to determine your ultimate business objectives: what do you want achieve and how are you going to measure it?
A clearly defined measurement strategy will guide the implementation and analysis.
Outcomes vs outputs: an output is a piece of data with little or no meaning, for instance, traffic. A 20% increase in organic traffic is a great output, but what is the implication here on the ultimate goal, say, profit?
The outcome is the final result of all outputs that success should be judged against. In the connected world, the majority of business objectives can be categorized into five segments and each usually has a measurable primary outcome. These are:
We just mentioned qualitative and quantitative data. Let’s talk about that:
Examples of qualitative data are: heatmaps, consumer surveys, user reviews and ratings, and live chat feedback. All of these methods help us to understand the user experience. It’s notoriously more labour intensive to analyse this kind of data, but it is increasingly valuable.
For example, live chat logs could indicate that soft enquiries are more valuable than online sales because of the opportunity for a salesperson to cross-sell a more profitable product. This could inform how you target and measure the success of your campaigns.
Collecting all this data is great, but solving attribution is where data becomes useful to marketers.
Tracking users continuously across multiple touchpoints both online and offline can be difficult but, depending on your situation, there may be ways to improve the accuracy of your attribution.
There are a number of attribution challenges.
Challenge: can’t identify a conversion that started on mobile and converted on desktop
Solution: user accounts, analysing attribution models to understand user behaviour on different devices and in different scenarios
Challenge: how do you know when offline advertising is driving online sales?
Solution: using specific campaign URLs in your print, TV or radio advertising allows you to track the success of that landing page.
Challenge: how do you know when your online efforts are driving offline conversions, ie. Phone calls?
Solution: call tracking software overlays tracked numbers onto your website and online advertising, allowing you to combine call and website data.
Challenge: when users interact with more than one channel before converting, where should the marketing budget go?
Solution: make sure you’re using an appropriate attribution model(s) for each campaign. Some campaigns are designed to generate traffic early on in the user journey, like a brand campaign, and so conversions should be attributed on a first-click basis. Other campaigns, like remarketing, are about getting people across the line, and are more appropriate for last-click attribution.
Assisted conversion reports can also shine a light on how much revenue each channel has played a part in generating:
Digital analytics should run in parallel with other marketing activities providing the business with real-time supportive information. Our analytics cycle consists of four steps:
The analytical cycle should run in parallel with other marketing activities; it should monitor ROI, provide unbiased actionable recommendations, give additional insight into the results of marketing and drive continual improvement.
Business: Car Leasing
Outcomes: ecommerce transactions and telephone sales
4. Continue to measure
5. Redefine desired outcomes
Have you spoken to our digital analytics experts about your ultimate business objectives? Do you have a comprehensive digital analytics strategy, implementation and measurement plan that will safeguard your marketing investments?
Do you want to drive more ROI by getting a picture of your business? What are the common obstacles and challenges you face in your business with regard to digital analytics? Contact us and we’ll be more than happy to discuss your goals with you.
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