Getting deep insights on the Digital Journey of customers is vital to creating a better customer experience. By improving the customer experience, gains can by seen in customer engagement, referrals, and loyalty which contribute to the success of your overall digital marketing efforts.
However, the complexity of today’s websites combined with the Omni-channel path a customer takes can make the analysis to get these insights all the more challenging. Nonetheless, the value for marketers can be tremendous to provide business insight in key areas.
Business Insights From Customer Digital Journey Analysis
A few examples of the types of questions you can answer include the following:
- When looking at the complete digital journey, what initially introduces the customer to your brand? Was it organic search, paid search, or maybe a social media interaction?
- What is the true conversion cycle, the time from the customers very first visit to conversion?
- What role is played by each channel throughout the customer journey?
- Are there detectable differences in behavior between converters and non-converters?
- Is there key content that influences conversion?
- Is there content that hinders conversion?
- Do the answers to the above vary significantly across different groups of customers?
Yet, answering the above questions is no easy task. Tools such as Google Analytics report session-level metrics which aggregate and obscure much of the needed information. This provides the familiar site metrics like bounce rates, pages per session, and time on site but does little to truly connect-the-dots across the Omni-channel digital journey that today’s customer will take.
[barquote color=’red’]Getting a detailed view of the customer journey across sessions and across devices was virtually impossible. Until now.[/barquote]
Google BigQuery + Analytics 360
Google BigQuery is a powerful, enterprise-level platform that enables data analysis on a fast, scalable platform. Dubbed “serverless” by Google, the platform provides a powerful toolset to allow the integration of data from virtually any source. These sources may include web analytics data, CRM, marketing campaign data and much more.
While beyond the scope of this article, BiqQuery uses a next-generation data model called Capacitor. While based upon SQL, BigQuery adds powerful features that allow nesting data columns within columns, to provide a more efficient way of handling the complex relationships that bigdata represents today. The end result is exceptional performance, flexibility and scalability for analyzing very large datasets (You can read more about BigQuery Capacitor in » Inside Capacitor, BigQuery’s next-generation columnar storage format.)
Advantages for Marketers
Simply put, BigQuery becomes an insight enabler to help marketers optimize faster than ever before. It supports rapid data exploration to answer questions, discover hidden trends, and uncover new insights.
BigQuery also supports integration with external platforms and other Google tools such as machine learning, predictive digital marketing, and Google Analytics 360.
When combined with the premium Google Analytics 360 product, the platform provides an enterprise-level solution to support data-driven marketing. This combination can unlock new data insights:
- Hit-level data – a more accurate and complete way of connecting the dots in the customer journey, across sessions, desktop and mobile devices.
- A deeper understanding of the customer – enabled by more rich data through extended custom dimensions, metrics and calculations. Using the Google Tag Manager data layer, attributes from sources such as Saleforce or D&B can be integrated in real time. Or batch updates can be made using marketing automation campaign data from platforms like Eloqua.
For marketers, this means a single view of the digital customer journey is now possible. That view has the potential of unlocking an understanding of how the Omni-channel journey spans devices, mediums, and channels – from the first visit through conversion.
The Ability to “Rewrite history”
You’ve probably heard the phrase hindsight is 20-20. Frequently marketers are challenged with the discovery of new insights, which end up generating even more questions. Those changes may arise out of the need to adjust the criteria, for example, that is used to define analytics goals. But with standard goal and funnel definitions, changes typically will only get picked up in future data. This greatly slows down the optimization process.
Using Google BigQuery, custom funnels and goal definitions can be applied retroactively, and with the flexibility of more advanced criteria. This allows the analyst to refine goal definitions, to correct broken historical conversion rates, or apply machine learning tools.
With the ability to make these changes to historical data, marketers enjoy greater agility to gain insights more quickly, and are empowered to optimize faster.
Simplifying Data – In The Cloud
Large-scale websites present a much greater challenge when it comes to analytics. The sheer volume of data is overwhelming and could easily take an analyst months, or even years to sift through using conventional tools. But BigQuery allows you to store and analyze petabytes of data. (one petabyte by the way, is 1000 TB or 1,000,000 GB)
For one recent client, the website had over 50 million visitors annually. This traffic resulted in 100’s of millions records from web activity across all of the interactions generated by site visitors.
One of the goals for this company was to analyze the digital journey and gain key insights to drive web development efforts. Path analysis could provide very valuable insights to help define personas, and identify web content that was most valuable to the conversion process.
However, the website contained tens of thousands of webpages, and were represented in the data as raw URLs. Future development plans included enhancements to standardize these pages for easier analysis. But for the current analysis task, this presented huge challenges in light of the high traffic volume.
Nevertheless, using BigQuery scripting, business rules were created to standardize the pages into less than a couple of dozen categories. The business logic and updates were executed in the Cloud and completed within minutes. This eliminated the need to attempt to download any portion of this massive dataset, which would not have been practical given the sheer magnitude of the data.
Using Insights To Define Buyer Personas
The Google BigQuery Export schema lists nearly 300 different data columns as a part of the dataset. Using statistical tools, dimensional reduction could be performed to identify the most important features and then more efficiently analyze the dataset.
By applying K-Means clustering techniques, groups of visitors were identified who exhibited similar behaviors. Descriptive differences were then provided to be used as input to the persona definition process.
Some of the key feature differences between groups included:
- Days to convert
- Digital journey “first visit” details
- The role of channels throughout the customer journey (organic, social, paid, E-mail)
- Page category fingerprint (page categories typically used by converters)
- Page engagement metrics ( e.g. scroll events )
- The influence of specialized content near conversion points (video and downloads)
- Cross-device patterns (first visit, mid-journey visits, conversion visit)
Without the power of a platform like BigQuery, this analysis would not have been possible. Additionally, the project was able to be completed within weeks versus many months using traditional tools.
The above only scratches the surface of the foundation that the BigQuery platform provides to support future AI and Machine Learning.
For more background on this project, download the Customer Journey Mapping case study or contact Scott Pete to discuss your specific application.