Digital interactions and the platforms that come with them have been constantly evolving over the last 20 years. The same applies to the tools used by digital professionals, which are becoming increasingly advanced. This has encouraged companies to create multiple specialized teams, which has inevitably led to siloes. 

It's important for companies to introduce a unified data model approach across their organization to bridge together data from multiple touchpoints. By ensuring that their analytics are at the heart of their digital product & marketing strategies, companies can ensure that it is fully aligned with their business needs.  

Piano's leading-edge platform provides a tailored data model that covers the entirety of a business's objectives as well as those of the different company stakeholders. It gives the entire organization access to a reliable 360° view of customer data, enabling teams to create immediate value, improving their speed to market and ability to react to trends faster than their competitors.

Today’s analytics challenges

In the early days of the web, there was simply the internet user who went online via a desktop, and, hey presto – straightforward analytics. Zooming up to 2021, the customer journey has become far more sophisticated, involving multiple touchpoints that can be physical, digital or both.  

In terms of analytics, this is compounded by the fact that each specific technology has traditionally had its own set of tools, from web analytics to app analytics, not forgetting voice analytics, chatbot analytics, etc. The silo effect also applies to digital professionals who have tended to focus and work on each specific technology, with little or no interaction. 

The same goes for the vocabulary used in analytics — page_views can be equivalent to screen_loads, app_open, message_sent, action_sent, etc. depending on the technology. And translating all these events into tangible steps in the customer/user journey such as conversion can be challenging to say the least. 

The convoluted nature of modern analytics can also create roadblocks when trying to effectively monetize your business and optimize costs/service levels. Even meeting simple KPIs can be a challenge, such as understanding how content consumption has evolved over time, the weekly average share of subscriptions between digital and physical or purchasing patterns across all your brands. 

Relying on a single, unified data model is therefore essential if you want to accurately measure how your content was consumed, understand and improve your site’s performance and use data to increase conversions, retention rate, repeat visits and content recommendations.

Implementing user-centric analytics 

There are a multitude of ways that companies can organize their data. However, it’s critical to have a central analytics strategy regardless of the governance model applied within the company — and ideally a team that can build value from the core of business, then offer it to the rest of the organization.  

Becoming user-centric is challenging due the range of platforms and devices, the need for specific teams and analytics tools, as well as the difficulty of centralizing the structure of the data. 

Creating a unified ‘hybrid’ digital analytics architecture 

Today’s analytics market can be boiled down to three approaches, each with their advantages and drawbacks: 

Marketing analytics 

These are designed to meet specific digital marketing issues such traffic acquisition, monetization and include numerous metrics and specific analyses. They provide useful Out of the Box (OOTB) & marketing-specific reports but can quickly reach their limits when it comes to analyzing very specific company concepts. They also aren’t flexible enough for more advanced users – you can’t go the extra mile.

Product analytics

These solutions have a high degree of flexibility, allowing them to measure interactions that are very specific to the development of a product or service — a strong selling point for Product Managers and Product Owners. Their drawback is that the flexibility can make them complex when it comes to having reliable and exhaustive data, as very few analyses are ready to use. This means they fall short in terms of data democratization and the ability to spread data across the organization. 

In-house analytics

Tools developed in-house can offer end-to-end customization capabilities while offering configurable computing power. But beyond the exceptional skills required to successfully complete in-house projects, there are numerous functional trade-offs such as development costs, risks, technical debt and maintenance. The Total Cost of Ownership can therefore skyrocket over time in the race to stay ahead of the competition. 

To ensure that both your analytics and your business are user-centric, it’s vital combine the best elements of these approaches.  

Firstly, you need to be able to effectively log events and their properties, i.e. understand exactly what actions the user has taken such as page loads and conversions. Then it’s necessary to make sure your data is being ingested through a structured and consistent pipeline, from collection, to cleaning, storage and activation. Finally, it’s essential to have users at the centre, avoiding on overflow of basic events that need to be cleaned up.

Aligning the terminology 

A major part of streamlining your analytics across all touchpoints is to align the naming of all the relevant actions. This could mean renaming all your events as page_loads or simply interactions. This also applies to properties which often have different denominations that describe the same thing, e.g. visitor_id vs. user_id. The key is to move as quickly as possible to understanding if the user has converted or not and why. 

Storing the events  

By using separate tools for your different types of analytics, e.g. web and app, you’re multiplying the number of tables on which the data is stored. This has serious ramifications in terms of the effective use of time, accuracy of the results and sharing the analysis with the relevant parties. By storing all your data in a single model, you link everything together and drastically simplify the analysis of the customer journey. 

Simplifying the data pipeline 

User-centricity means effective data collection, storage and activation. Firstly, the data needs to be collected across all user touchpoints. Next, it needs to be stored cleanly and neatly. It can then be used to provide decision-ready reporting to users across your organization, or by the data science team to build recommendation engines. 

Setting up a unified data model 

By mapping out the KPIs, you can align your teams around the same analytics objectives. It’s then necessary to design your data model by identifying the main events and associated properties that you’ll need to measure. Next is gathering interest from key stakeholders and demonstrating how they can leverage value from the data. 

Finally, there is making sure you have the optimal digital analytics tools to adapt to your strategy. This means choosing a solution that enables you to sustain the effectiveness of your data model over the long term.

How a data model strategy will boost your business value 

With its leading-edge platform, Piano makes it simple to meet your KPIs. To discover how your content consumption has evolved over the last year, we allow you to track the entirety of the cross-device user journey with a single tool, use the same vocabulary throughout the analysis and perform the same calculations. 

With our predictive analysis, you can carry out advanced anomaly prediction powered by machine learning. This allows you to monitor any drop in traffic based on your normal rates of consumption and lets you set up automatic alerts to notify the relevant sales teams. They can then put in place the relevant retention steps to reduce user churn. 

We also ensure that you implement effective data governance, which is not only about quality but having adequate data privacy protection. Piano is committed to protecting user privacy in full compliance with global regulations. Our platform categorizes the purpose for the collected data, as well as the legal basis for the collection. This involves clearly listing user consent/opt-in in relation to the specific country regulation involved, which in turn minimizes the legal risk for companies and ensures that collection is ethical and in line with the respect for user rights. 

The critical importance of data democratization is ensuring that decision-ready data is accessible to everyone in the organization. Piano’s dashboarding and automatic reporting tools are integrated as standard elements in the data model and are fully turnkey from the outset.

Learn more about our new analytics offering by visiting this overview page.