Unique Paths to Understanding Customer Journeys

Tuesday December 13th, 2016

When it comes time to write my quarterly blog, I reflect back upon the focus of my work and interactions with our customers. Usually this leads to a new lens for which to discuss the potential for data and analytics to help organisations achieve business outcomes. What is interesting to me is to realise that the topic of Understanding Customer Journeys continues to be the most talked about topic and so I have decided to explore it further.

At Teradata, we frame customer journey analytic capabilities in terms of Connected Data, Connected Analytics and Connected Interactions as illustrated in the following diagram:
monica-woolmer-unique-paths-1

  • Connected Data – provide marketing insights from all types of data (transactions and interactions)
  • Connected Analytics – provide marketing the ability to see and understand potential and actual outcomes
  • Connected Interactions – provide marketing with the ability to manage a consistent customer experience across all channels

So where to start? Well that depends on the capabilities your organisation already has in place. In some cases there will be a need to fill a gap, such as enhancing the ability to capture more granular customer events. In others the requirement may be to connect data from multiple disparate data silos. Most organisations have an ability to interact with their customers across different channels. Are these interactions coordinated? Is there a need to move to more real-time interactions?

Perhaps not too surprising in all of my recent conversations a key topic is the role analytics play in understanding the customer journey. Many common customer analytics utilised today include segmentation, profitability and scoring/propensity models. The critical Connected Analytics capabilities required to understand and optimise the Customer Journey are highlighted in the diagram below.

monica-woolmer-unique-paths-2
The Connected Analytics capabilities include:

  • Real time analytics to drive decision making into frontline channels utilising a combination of business rules and machine learning
  • Discovery analytics to understand the Customer Experience and drive more strategic decisions
    o Customer Path analytics to understand customer journeys, where they are failing, and improve them (Examples include Path to purchase, Path to churn, Path to complaint, Path to profitability, Path to fraud)
    o Text analytics to understand the topics that customers are talking about, their sentiment and intentions
    o Predictive modelling, utilising multi-genre analytics (combining multiple analytic methods) to improve Predictive Models( Examples include improving churn prediction, propensity to buy etc.) through the development of behavioural models
    o Affinity analysis to identify product recommendations, identify influencers etc.
    o Marketing attribution to better understand the exact impact of different media, enabling smarter decisions to be made about Paid Media spend
  • Actionable Analytics are analytics are those that directly drive marketing decisioning, including Machine Learning to continually optimise your campaign targeting
  • Dashboards and Reports help you to monitor your business performance, and determine where changes need to be made.

Which analytics are applied is determined by the business requirements. It is expected that the portfolio of analytics utilised will grow over time.

Just as each of your customer’s journeys is unique your organisation’s path to improving the understanding of customer journeys will also be unique. The key to moving quickly is to identify a path from where you are today to the business outcomes you want to achieve.

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