A Recipe for Data & Analytics

In a previous article called Data, Data, Data, I argued that to operationalise metrics you need to build Insight Analysis into your way of work by developing a process and structure to be able to move from Insight to Action to Impact to enable fast feedback loops.

The analytics landscape has evolved significantly. The tooling and approaches involved (i.e., non-exhaustive list) range from web, to product, to digital experience, to customer journey, to marketing and operational analytics.

The choice of what analytical approach to use will really depend upon the touchpoints you have enabled for the various stakeholders interacting with your organisation and the types of questions you are trying to answer.

Touchpoints are a combination of device (e.g., smartphone, laptop, desktop, kiosk, tablet, IOT device etc.), channel (e.g., Voice Call, USSD, SMS, Instant Messaging like WhatsApp, LINE, or Messenger, Social Media like LinkedIn or Facebook, Media Platforms like Youtube, Tik Tok or Instagram, Email Clients like Outlook or Gmail, Browsers like Chrome, Safari or Edge, Native Smartphone Apps etc.) and task (e.g., submitting a claim, view statement of transactions and check status of query).

Examples of touchpoints in action e.g., smartphone device used in combination with a smartphone app or or a smart watch paired with a digital wallet for payments is another touchpoint etc.

The challenges that businesses face today in terms of managing data from a multichannel world that is still far from truly being omnichannel are the following:

• Coherent data and analytics strategy – Developing a data strategy is rooted around defining a data vision, strategy, definition of objectives and specifying key success measures.
• Ensuring a seamless customer experience across multiple devices and channels: Customers expect a unified experience when engaging with a company regardless of the device or channel they use.
• Organisational maturity and stage of development - The types of analytics needed vary by stage and maturity of an organisation.
• Data & analytic categories - The categories of analytics used to manage use cases range from descriptive through to predictive and prescriptive analytics.
• Deployment into production - Most organisations struggle to deploy machine learning models into production.
• Interoperability and integration compatibility: As more devices become interconnected, it can be difficult to ensure that all devices are compatible with each other.
• Securing customer data: To manage interactions between devices and channels, customer data must be securely stored and managed.
• Managing customer preferences: Customers have different preferences and expectations when it comes to how they interact with a company.
• Security: One of the biggest challenges with managing interactions between devices and channels is ensuring security.
• Data Integrity: As data is shared between devices and channels, it is important to ensure that the data is accurate and consistent across all channels.
• Scalability: As more devices and channels become interconnected, it can be difficult to scale the system to accommodate increased usage and data traffic.

A range of issue areas need to be addressed within an organisational context covering the following themes when developing an approach to data & analytics.

The main issues to consider when thinking about how to use data and analytics

Focus on anchoring on customer goal attainment. So, asking the following questions?

• Are there unmet needs?
• How important are these unmet needs?
• How does your offering or solution speak to these needs?
• Can you measure and instrument your products and services to track how well you meet these unmet needs?

Define your organisation’s touchpoint taxonomy. So making sure you gain an understanding of the following

• Understanding the device and channel mix your organisation enables
• Developing a well-considered touchpoint taxonomy that accounts for the various interaction and experience layers for your stakeholders: moving from UI Layer through to User Journey or Task Level Layer through to overall Customer Experience Layer.
• Enable the data and analytics tooling to be able to generate insights and to create a shared understanding of what is happening at key touchpoints.

In future articles I plan to do a deep-dive on the following:

• Web analytics
• Product analytics
• Digital experience analytics
• Customer journey analytics