This publication is broken up into three sections:
TL;DR - For those wanting a quick take
Summary - For those wanting a bit more context and high level points
Article - Main body of work containing fully detailed article and explanations that you might want to consume over several readings
TL;DR
In an earlier 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 enable the move from Insight to Action to Impact through fast feedback loops.
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 main issues to consider when thinking about how to use data and analytics.
Focus on anchoring on customer goal attainment. So, ask 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 make 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.
Summary
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 enable the move from Insight to Action to Impact through 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 smartwatch 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 diagnostic 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, channels, and 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, ask 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 make 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.
Article
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.
Some things you can do to get you on the path to data-informed organisational and product development include the following:
Creating a Logic Model of how your organisation creates, delivers and captures value. Your logic model can represent the whole organisation or a small part of the organisation. Think of fractals.
Building a Value Driver Tree so you can see the key variables you can influence to generate the outputs and outcomes that you want; you may need to iterate design of your Value Driver Trees to approximately describe your organisation.
Incorporating Insight Analysis Process into your way of work to enable Insight to Action to Impact.
In this article I want to begin addressing some of the approaches you can take to developing actionable insights.
What analytical approaches are available that can serve marketing, sales, product management, operations and compliance?
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 or Device Apps etc.) and task (e.g., submitting a claim).
Examples of touchpoints in action e.g., smartphone device used in combination with a smartphone app, a desktop used in combination with a web-based application is another touchpoint or smart watch paired with a digital wallet for payments is another touchpoint etc.
As you can imagine the more devices and channels you offer, the increase in touch points is combinatorial and the potential paths taken by stakeholders can represent a complex network of interactions.
So how could you make sense of this touchpoint complexity?
Depending on your age and experience you have probably seen phrases like Management Information Systems (MIS), Business Intelligence (BI), Big Data, Data Analytics, Data & Analytics and Data Science being used in articles and videos. These terms have come and gone and come back again in some shape or form.
The common theme amongst these approaches is that they all require data that gets transformed to into a state to be useable within a specific business use case.
Some specific techniques in data warehousing, referencing Kimball (Ralph Kimball) and Inmon (Bill Inmon) approaches to dimensional modelling, still play a significant role in how we should consider architecting data warehouses to provide business friendly, valuable, and actionable data insights.
Modern approaches like streaming analytics require data streaming pipelines that enable use cases that are more dynamic and real-time in nature but have a lot of cost and complexity attached to them if not architected well.
What approaches have been developed to account for unque interface interaction context?
There are a couple analytical approaches to insight generation that have been developed to account for complexity of omnichannel or multichannel interactions between devices and channels?
Analytics Categories by Analysts and Vendors - The broad categories of analytical approaches taken within the enterprise as shared by some analysts and vendors include the following:
Forrester View: Web Analytics, Digital Experience Analytics and Product Analytics
Pointillist (now owned by Genesys) View: Customer Journey, Customer Data, Business Intelligence, Journey Mapping and Digital Experience Analytics
Machine Learning: Machine learning algorithms can be used to identify patterns in multichannel interactions. This can be used to identify the best strategies for optimizing customer experience which for example could lead to increasing conversions.
Predictive Analytics: Predictive analytics can be used to forecast customer behavior across channels and identify areas for improvement. This can help marketers optimize their strategies to increase conversions and/or revenue.
Text Mining: Text mining can be used to extract valuable insights from customer interactions across different channels. This can help to identify customer needs and preferences to better target them with personalized offers.
Agent-based Modeling: This approach uses computer simulations to model the interactions between individuals, organizations, and systems. It can be used to analyze complex omnichannel interactions by creating virtual environments that mimic the real-world interactions.
Network Analysis: Network analysis can be used to map out the relationships between different channels and devices and determine the impact of changes in one channel on the others, e.g., the in ability of an end user to complete a self-service action in the smartphone app and the need to break-out into a contact center for follow-up.
Multichannel - Multichannel analysis is a way to analyze customer behavior across multiple channels, including online and offline. By analyzing customer behavior across multiple channels, companies can gain insights into how customers interact with their brand, identify areas of opportunity and make more informed decisions. This type of analysis can help companies better understand their customers, personalize marketing messages, and optimize the customer experience across all channels. This type of analysis can help brands understand how customers interact with them and create better customer experiences.
Given the available analytics approaches what is a useful way to frame the nature of the modern analytics challenge?
The essence of the problem centers on how we need to consider the device and channel mix offered up and the types of questions we are asking.
The combination of devices and channels define touchpoints we have enabled for our customers. When you audit your organization’s touchpoints you will be surprised by the number of touchpoints that your organization has enabled, and the complexity created.
The challenges that businesses face today in terms of managing 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. You will need to contextualise any framework like Gartner’s DASOM framework into your organizational context.
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. It is essential to ensure that the experience is consistent, cohesive and coherent and that customers can easily transition between devices and channels.
Organisational maturity and stage of development - The types of analytics needed vary by stage and maturity of an organisation. The data and analytics set-up for an early stage start-up are qualitatively different from an established enterprise with many lines of business and product ranges.
Data & analytic categories - The categories of analytics used to manage use cases range from descriptive (i.e., dashboards and key business reports), to diagnostic (i.e. root cause analysis understanding why something happened), to predictive (i.e., statistical or machine learning models used in fraud detection, customer or employee churn prediction or weekly sales forecasting) and prescriptive (i.e., real-time or near-real time inference being served to end-users through recommendation engines) analytics.
Deployment into production - Most organisations struggle to deploy machine learning models into production. When you examine the reasons for this there are a couple of common factors ranging from organizational to personal, to inter-personal to political issues.
Interoperability and integration compatibility - As more devices become interconnected, it can be difficult to ensure that all devices are compatible with each other. This can lead to incompatibilities between devices and channels, which can disrupt the user experience. Devices and channels must be able to communicate and exchange data. This can be difficult to achieve due to differences in protocols and technologies used by different platforms.
Securing customer data - To manage interactions between devices and channels, customer data must be securely stored and managed. Security risks must be addressed, and appropriate measures taken to ensure customer data is not compromised.
Managing customer preferences - Customers have different preferences and expectations when it comes to how they interact with a company. It is essential to be able to track and manage customer preferences to provide a tailored experience that meets their needs.
Security - One of the biggest challenges with managing interactions between devices and channels is ensuring security. As devices become more interconnected, it becomes increasingly important to ensure that all data is secure, and that only authorized users can access the data.
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.
So how should you approach analytics?
The generation of insights from data can drive tangible business value and impact. Companies like Walmart, Amazon, Google, Meta and Spotify represent the power and potential of advanced uses of data and analytics in both the digital and physical world.
A range of issue areas need to be addressed within an organisational context covering the following themes when developing an approach to data & analytics: People & Culture, to Business Value and Outcomes, to Process and Operations and Technology and Tooling
The main issues to consider when thinking about how to use data and analytics.
Focus on anchoring on customer goal attainment. So, ask 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 make 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
Post Script
Before you go, please could you do the following?
Subscribe
Share
Survey
Star
If you got value from reading the article a star liking would be highly appreciated