The customer journey is made up of experiences and related emotions.
Mapping it helps us ask important questions such as:
- What steps lead to positive emotions? To greater lifetime value?
- What experiences lead someone to shop at a competitor?
Think about the customer journey map
The map, along with customer feedback, might answer the question about positive experiences. Lifetime value can be calculated by looking at findings from the data warehouse. However, the question about leaving us and moving to a competitor is more nuanced, and demonstrates the value of linking the customer journey to analytics.
Using machine learning, causes of behavior can be determined and monitored over time. The analytic models are built from patterns determined by an interplay of dimensions that are measured throughout the journey, including which steps were skipped by a customer, which steps were repeated, how much time it took from start to finish of a process, and how much was spent.
Business statistics, such as how many, how recently, and for what profit, can be gathered at each step of the journey. These are tactical measurements that show how well the business is executing a plan.
With big data, there is more information about customers along their journey, with more steps measured, and with data coming in faster.
When data science is applied to a customer journey map, we can begin to assess cause and effect. Making a plan to address causes is strategic. Machine learning reveals the most important patterns and levers leading to observed behaviors. These information levers can influence both strategy and messaging, making customer communications and advertising more effective.
A few thoughts on campaign messaging
- With no analytics, everyone receives the same message.
- With demographic segmentation, it’s possible to target what people look like – how old they are, where they live, or their gender.
- When customer behavior and predictive analytics are added, the targeted message can reach small groups that look and behave the same way.
- Adding machine learning and big data allows personal messaging. It’s the holy grail of marketing: the right message to the right person at the right time.
With big data, there is more information about customers along their journey, with more steps measured, and with data coming in faster. This means that a more nuanced view of the customer is possible through analytics. Communicating with individually identified customers, which increases our likelihood of success, is available when we apply data science to the customer journey.
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