For marketers, the web is a gold mine. Consumer information abounds the web: What drives consumers to your page? Who browses your website? How much time do they spend on your pages? You can measure the effects of a digital campaign and identify what worked and what didn’t with remarkable accuracy.
We all know how important it is for a company to not only be client-focused, but to also be data-driven. Now with this in mind, let’s look at some aspects of data collection and analysis geared towards digital marketing.
DATA-DRIVEN APPROACH
With traditional media, gathering data that concerns the progress and performance of campaigns has always been a daunting task: The data is very much sought after, but it is also very scarce. Advertisers and companies could get a rough idea of how consumers were responding to their messages and campaigns, but it was difficult to know exactly what was happening and why.
In the digital age, data is everywhere. Every single mouse click can be recorded, and this produces a very large amount of data, which can be used to understand what’s going on, and to know when, where, how and why the user is doing one action instead of another. And as data-driven information provides a solid and objective basis, given the right analysis, it is easier for digital marketers to make decisions and to obtain better results.
Beyond the analytics and the data science itself, you don’t need to be a wizard with numbers: In the last years, Analytics software has become more user-friendly as it enables us to produce reports that are easy to interpret and understand. Such software allows marketers with a good understanding of basic principles to explore dashboards and reports at ease, while they let data scientists focus on the data to produce these fancy reports.
PERFORMANCE AND TREND MONITORING
For marketers, data analysis primarily revolves around monitoring user behaviour and understanding trends in marketing campaigns. To discover and make sense of trends and tendencies, it is necessary to have a dynamic view of the data, and thus to analyse this data over different timeframes.
There’s little value in knowing that “…10% of this month’s web traffic was converted…”. Is this a positive or negative result? On the other hand, if this figure represents 20% more than last month’s conversion rate, then we have a positive change (which may be part of a trend).
DATA ANALYSIS
To test the success of your web marketing initiatives, bear in mind the TAO of conversion optimization:
- Track
- Analyse
- Optimise
A number is just a number until you interpret it: Interpretations should tell you how your users interact with your website or digital campaign. Adjustments and corrective actions should then be applied before the “Track-Analyse-Optimise” cycle repeats.
KEY ELEMENTS FOR ANALYSIS
Analyse behavioural data to understand your visitors’ intentions. Try to find out Why they visit your website. User behaviour can reveal a lot about needs and intentions. By looking at the referring URLs and keywords that were used to land on your site, you can uncover a lot about the context in which visitors are browsing. Have you reached your target audience or not? Run a thorough SEO analysis to find out which keywords your website ranks best with.
Analyse your Outcome metrics: Are visitors attaining the browsing, engagement and conversion objectives that you’ve set? At the end of the day, do you want users who visit your site to perform a specific action (purchase, conversion, registration, …)? The analysis of objectives and KPIs can indicate where there’s room for improvement. Check whether your website meets the viewers’ expectations and whether this matches your objectives. Consistently integrate content that meets the needs of your target customers (content marketing).
Other important data will explain the user experience. What are the patterns of user behaviour? Which devices are they browsing from? How long do they spend on specific pages? What is the click path? How can we influence users to achieve the goals that we’ve set? To determine the factors that influence the user experience, it’s necessary to test and determine the users’ “behaviour patterns”.
Understanding how users behave on your website will allow you to understand how this behaviour can be influenced to improve your results and meet your objectives.
THE IMPORTANCE OF SEGMENTATION
Each visitor is different, but they each have similar characteristics to other users, and therefore can each be placed in different homogeneous groups whose metrics can be analysed (and here you can see an application in contextual marketing). This is segmentation in a nutshell. Google Analytics offers various ways to segment visitors through different dimensions. These segments could lead you into comparing seemingly complex detailed behavioural patterns with relative ease:
- How does the click path of a first-time visitor to your site differ from that of a returning visitor? Which parts of the site are most important to first-time visitors (page views metrics)?
- What is the difference in navigation behaviour for users who arrive on your site via search engines to those who type in the URL directly? Or perhaps to those who get directed to your page from a link in an online news article…
Digital marketers will be concerned with numerous metrics, including conversion rates, click-through and exit pages, user acquisition cost… Is there anything you can do to improve your users’ experience?
Connection speed, operating system and browser
It is also important to take into account the technology used, as it can affect user behaviour. A high ‘bounce rate’ for users with low bandwidth, for example, could indicate that your site is taking too long to load pages. Even different browsers can display the same website in different ways. How are you displaying for mobile devices? For tablets? For desktops?
Geographical location
Users from different countries or cities generally behave differently on the website. How can you optimise the experience for these different viewer groups?
TOOLS
Which Analytics tools will you choose? Specific software is needed for data collection and analysis. Remember that if you decide to change the analysis tool, you may lose the historical data of previous activities.
ANALYTICS BEST PRACTICE FOR DIGITAL MARKETERS
One of the most common complaints from consumers is that they’re constantly overwhelmed with non-personalized and irrelevant marketing content – which deteriorates their browsing experience. On the other end of the equation, marketers feel that they don’t sufficiently grab consumer attention, nor do they enhance the consumer experience or increase the brand perception. The problem with inefficient marketing primarily stems from the fact that there isn’t a clear view of each consumer and of what each consumer wants. This is where analytics can help drive better decisions. As with any technology implementation, Analytics requires best practice and enterprise processes optimization to be successful.
For a major European telecommunications provider, we worked with a team of behavioural scientists to reshape their approach to analytics and to obtain a consistent view of the needs and demands of their consumers. To succeed in the project, we followed some relevant best practices.
1) Define the outcomes
Our client had been inefficient in their approach to analytics. There were no clear objectives set and they had been aimlessly spending time and resources to track, collect and analyse large sets of consumer data without a clear objective. We directed their focus on what was most relevant to their needs. (ex: Acquisition vs Retention? Product vs Bundle placement?) Analytics cannot be ambiguous and “general purpose”.
2) Collect what you need
The client had been tracking and collecting a very large set of data from a plethora of sources, and centralised all of this information in their existing “analytics repository”. This operation was highly inefficient and resulted in a gargantuan waste of human and computational resources. We helped the client identify the data that was most relevant to them, and to find better alternatives for the data that was not. This in itself made the task of data analysis leaner, easier, and more efficient.
3) Organizational approach
Our client was repeating a mistake that we witness all too often with other clients across all industries: Digital Marketing Analytics fell under the responsibility of the Client’s IT team. The request for analytics data came from Marketing, and yet Marketing had very limited exposure to their initial analytics design phase. Fusion teams are on the rise, and with the user-friendly and intuitive analytics tools that are available on the market today, digital marketing is no exception. Needless to say, our client’s marketing team evolved with a few data-inclined marketers and some proper direction to design for their own needs.
4) Deep dive and present your data
As we’ve often witnessed with other clients, our client had extended their digital marketing analytics processes into Excel and PowerPoint. The inefficiencies were countless. With proper training, our client’s marketing team quickly mastered their Analytics tool – at first to leverage pre-built dashboards and reports, and later to customise these with minimal effort thanks to visual configuration. Whether they were pre-built (out-of-the-box) or custom, both reports and dashboards dazzled with their high-impact graphics.
Quince Market Insights reports that the global data analytics market has been estimated to reach USD 24.63 billion in 2021 and is projected to grow at a CAGR of 25% during the forecast period till 2030.
Source
How to measure digital marketing: metrics for assessing impact and designing success
The use of Web analytics for digital marketing performance measurement
