Posted on: 26 10 2022

Customer experience modelling—best practises with data

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I’ve been running Customer Experience Journey Mapping workshops for eight years now. I have noticed that sometimes the conclusions reached are based on the opinions of the loudest person in the workshop and on various stereotypes. Fortunately, data can help to overcome these erroneous conclusions.

A company’s staff may have a very narrow understanding of what is happening in its customer buying process. How, then, can you improve the customer experience if decisions are driven by a misconception of the buyer journey, or stereotypical buyer personas?  By relying on them, a company often continues with “business as usual” and generates no new insights. That’s one way that companies fall behind their competitors.

There are many different methods for modelling the customer experience. I use the Stanford Design School model to run our Customer Experience Journey Mapping workshops.

It helps me to break down the customer journey into small pieces and reveal all the possible nodes and bottlenecks. This makes it easy for sales and marketing teams participating in the workshops to identify the key issues affecting the customer experience and systematically make improvements.

Are you ready for the moment of truth?

The success of a CXJM workshop is very much dependent on careful groundwork. Both Luxid experts and client company staff are involved in the preparation phase. The multidisciplinary team will jointly review the client company’s data and use it to prepare the basis for the CXJM.

The actual workshop always starts by modelling the customer journey. The modelling takes into account the customer’s emotional reactions and other issues that are relevant to the experience at different stages of the journey.

Once the client’s journey has been modelled, one or more moments of truth are selected along the way and examined in more detail: what happened before the moment of truth? What is the emotional impact of the moment of truth? What needs to be done to make that emotion more positive?

In this workshop, a company’s sales, marketing and possibly also product development experts and business management work together on a common issue. This gives the team a clear picture not only of the customer experience, but also of its impact on the business. The workshop also highlights everyone’s roles and responsibilities in improving the customer experience and creates a basis for better collaboration.

CXJM workshops make it much easier for companies to market their businesses and help them to realise that they should be focusing on customer-centric marketing rather than product-centric marketing.

The traditional CXJM workshop remains a sensible and cost-effective first step tool for modelling and developing the customer experience.

Using data to boost modelling

As I mentioned earlier, the stereotypical buyer persona and associated buyer journey is rarely accurate. At worst, believing in it can have a negative impact on your business. It is therefore important that the modelling of the customer journey and experience also make use of existing company data. This information is used to verify and corroborate the different stages of the journey, touchpoints, reactions, content and interactions. Real-time measurement allows for rapid responses to problems.

Data, machine learning and artificial intelligence (AI) are already playing an important role in customer experience modelling. Their importance is growing. While it is true that they make it easier to define customer personas and enable hyper-targeted and segmented marketing, machine intelligence does not understand human emotions. This is precisely why customer experience modelling requires human input, teamwork and emotional intelligence—exactly what the CXJM workshop offers.

The steps of a modern CXJM workshop

  1. Preparatory work based on company data
  2. Workshop with hypotheses and modelling, emotional intelligence
  3. Hyper-targeted and segmented marketing
  4. Measurement and optimisation, iteration based on data
  5. Implementing best practices in operations
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