Customer journeys are part engagement and part experience. When a brand designs a promotion to engage its customers, how those customers respond defines the experience. Key performance indicators (KPIs) can help organizations quantify these experiences.
Power BI Reporting can evaluate data points for a number of different customer KPIs, such as:
- Retention Rates
- Renewal Rates
- Lifetime Value
- Satisfaction Scores
- Upselling Rates
Turning these numbers into visuals and then displaying them on dashboards can bring information to life. This helps companies see what their customers experience.
Reporting tools bring data together in an easy-to-understand format. This means that companies can ensure customer success by analyzing the customer journey, personalizing engagement, and measuring customer health.
Analyzing the Customer Journey
A recent article in Harvard Business Review (HBR) suggests that businesses look at the underlying patterns of their customer journeys rather than using a stage-based model. For example, suppose an eCommerce merchant decides to look at the buying patterns of their best customers. They can look at the number of site visits, the products purchased, and the time between purchases.
Identifying the Yo-Yo Effect
Through data analysis, a merchant might find that their best customers rarely purchase during sales events and take longer to purchase items after a sales promotion. Why were customers not taking advantage of sales? With further analysis, the merchant may learn that their websites slowed down during sales promotions, resulting in customers abandoning their carts.
After turning data into visual charts, merchants can see the yo-yo effect of their sales promotions. Perhaps they learn that before a sales event, their top customers purchase at least every two weeks. After the event, their customers waited as long as a month before buying. According to the HBR article, customers dislike inconsistent journeys. In fact, inconsistent journeys rank only slightly higher than consistently poor experiences when looking at a list of the top most dissatisfying interactions.
Improving the Customer Journey
Obviously, in this example case study, the merchant needs to address their slow website, but they also need to address the buying experience of their top customers. They may decide to offer a pre-sale event 48 hours before the sale to provide their top customers with a more consistent experience. They established KPIs to monitor effectiveness.
After months of data collection, the merchant learns that their top shoppers continued to purchase at the same rate, even during sales events. Rather than relying on anecdotal information, this eCommerce merchant collected data at crucial touchpoints in their customer journeys. They used the resulting insights to create a consistently positive experience for clients with the highest lifetime value.
Personalizing Customer Engagement
An individualized buying experience is no longer an option. With 62% of consumers expecting a personalized buying experience, companies need a data-based strategy for customer engagement. They need to segment their customer base.
Once companies have data, they can separate their customers into groups. They can group them by age, demographic, and location. They can segment customers down to a granular level of detail, getting as specific as, “who purchased a blue shirt on June 3rd?” The possibilities are limitless.
However, not every data point makes for a profitable segment. Typical segments are arranged based on demographics, purchasing behavior, and customer preferences. These groupings make personalization possible. Personalization is crucial to business success, with 70% of consumers indicating that personalization impacts brand loyalty.
Segmentation can also identify missed opportunities. Grouping buyers by age could highlight where sales are weakest. Having that level of detailed data enables companies to develop marketing strategies to improve sales. It also provides an opportunity to solicit feedback to improve product offerings.
Individualizing customer engagement means understanding customer data. Although 77% of people are willing to share their data in exchange for more personalized service, they still expect companies to protect their information. They don’t want their details to create “creepy” engagements.
For example, assuming that everyone who purchases maternity clothes is an expecting mother could lead to awkward assumptions. Correlating purchase behavior with age can prevent sending mother-to-be offers to elderly buyers. While 91% of consumers are likely to engage brands with relevant messages, few appreciate generic or misaligned messaging.
Knowing how to use data effectively is central to successful customer engagement. Data overload can lead to missed opportunities. Finding the right reporting tools can help businesses sort through the available information to ensure their engagement is on target.
Measuring Customer Health
Customer health measures how likely a customer is to remain a customer. It provides an overall score based on information related to engagement, feedback, and service. This score provides a holistic view of customer success by consolidating multiple data points into a single value.
Interpreting Health Scores
Health scores are a risk assessment tool that quantifies the likelihood of retaining a customer. What the value is based on depends on the data used in calculating the health score. Identifying high-risk customers allows organizations to regain customer loyalty.
Let’s go back to the eCommerce example we looked at earlier in this article. Once the merchant had identified the website problem and offered their best customers a VIP sales event, the purchase rate returned to normal. However, this experience may have decreased customer loyalty. Determining customer health can identify the buyers who are most impacted by the yo-yo experience.
Some customers will be satisfied with the changes, while others may not be as quick to forget. By highlighting which buyers are most dissatisfied, the merchant can deliver personalized messages for better engagement. Monitoring health scores over time can identify which tactics worked best for regaining loyalty.
Customer success relies on data. It’s not something that just happens. Factors such as website visits, customer preferences, and product purchases can keep businesses informed on how they’re doing. However, data points do not provide insights unless they are viewed through reporting tools that can highlight relevant metrics and help visualize trends.
ChristianSteven’s PBRS consolidates data from across an enterprise to deliver insights on customer success, whether in reports or on a dashboard. Through its scheduling capabilities, PBRS ensures information is delivered automatically. Contact us to see how PBRS can help your business measure customer success.