AT A GLANCE
- Customer Experience Management uplifts customer loyalty, encourages additional purchases, and increases customer referrals, boosting incremental revenues.
- Customer Experience Analytics is collecting and assessing customer data that enable an organization to understand its customers better.
- Thanks to data analytics, you spot trends and predict outcomes, strengthen the customer relationship, thus helping your company perform at high levels.
Customer Experience Management (CEM) is the collection of processes you use to track, manage, and design your customers’ interaction with your company.
Implementing CEM improves business performance in almost every industry: It uplifts customer loyalty, encourages additional purchases, and increases customer referrals, boosting incremental revenues.
Customers generally go through purchase and ownership stages:
- They define their need,
- research alternative solutions,
- decide on goods or services,
- complete the purchase,
- activate and use the product or service,
- request service,
- make referrals,
- and purchase additional items.
Those sequential stages generate a customer journey and could spread across various touchpoints like a branch, call center, website, and mobile application.
Customers may prefer different touchpoints while interacting with your organization. For instance, one might request information at the branch, while the other could request service through your mobile application.
Moreover, different customer personas go through different journey paths. For example, upon purchase of a service, an elderly customer could immediately request service, whereas a tech-savvy customer could figure things out by himself/herself.
IMPROVE CX BY USING CUSTOMER EXPERIENCE ANALYTICS
Customer Experience Analytics allows us to examine all journey stages for all personas at all touchpoints. It helps us identify the gaps between actual customer behaviour and the designed customer experience. It also reveals the missing information so that we can track customers throughout their journey.
We use two types of data to perform a customer experience analysis: transaction data and description data.
Transaction data defines an event (the change as a result of a transaction.) We usually describe those with verbs.
When analyzing transaction data, we focus on transaction time (time, date, sequence number etc.), transaction definition (transaction type, category, subcategory etc.), and identity (customer, account, contract, service request etc.)
Events in Customer Experience Analytics represent transactions. A transaction sequence under a specific identity refers to a path. By processing all transactions for all identities, we discover possible transaction sequences and alternative journey paths.
Events and journey paths include an identity value and identity description so that we can label them.
Adding description data does not change -but specifies a journey path. For example, we could generate a young customer’s journey path if we have access to his/her demographic information.
ANALYZING A PARTICULAR CUSTOMER PROCESS, OR A CUSTOMER GROUP
We can also perform Customer Experience Analytics to focus on a particular customer process. We accomplish that by identifying transactions related to the customer journey or the specific customer process.
Likewise, we can analyze the experience of a specific customer group by utilizing the identity description. Separating journey paths helps us compare customer groups in terms of formal and distinctive events and journey paths.
Customer groups can also be analyzed based on critical journey paths. Those include the most frequent journeys, paths that end up with high customer satisfaction or events that processes a service complaint.
Customer Experience Analytics provides a holistic view of the customer journey or process. It helps us identify all possible journey paths based on actual customer behaviour, exposing the deviations from the designed customer journey.
Accordingly, it addresses improvement areas for Customer Experience Management. Thanks to data analytics, you can spot trends and predict outcomes, strengthen the customer relationship, thus helping your company perform at high levels.