A good CX can bring you closer to your brand advocates. How can data analytics help you deliver a seamless experience?
Customer experience is what connects your brand to your customers. It is a bridge between brands and their brand advocates that can be defined as the way a consumer perceives your brand. Every interaction your customer has with your brand has the potential to either weaken or strengthen the bond and having an optimized website or a good SDR is just the starting point for providing a positive customer experience.
Good CX involves building relationships by understanding what people want, need, and value. The complete experience includes pre-purchase associations with the brand (via marketing or awareness), the process of researching and making the purchase (either in-store or online), and post-purchase interactions (regarding service, repairs, extras, and more). The goal is to build meaningful connections between the brand and the customer.
Now that we know how customer experience affects our brand, let us understand how data analytics can help us optimize it.
What is data analytics for customer experience?
Analyzing data from customer interactions can give you a lot of valuable insights. You can get a clear idea of customer satisfaction, loyalty, and other metrics that reflect how your customers interact with your product.
You can also utilize data analytics to improve customer experience and overall improve customer satisfaction — thus increasing customer retention in the long term.
Importance of using data analytics for customer experience
Customer experience analytics is obligatory for companies that want to prioritize their customers. It lets companies understand their customers’ journeys, helping them to personalize experiences to meet individual tastes. By interpreting customer behavior, businesses can target their offerings better.
Also, customer experience analytics helps specify pain points in the customer journey. It motivates businesses to proactively resolve issues, resulting in higher customer satisfaction and less customer churn. Predictive analytics also plays a role in strategic planning by foretelling future customer behavior.
Customer experience analytics is a vital factor in driving customer loyalty, growing conversion rates, and enabling business growth.
Steps for analyzing customer data with customer experience analytics
Here’s the 5-step technique you can follow to get the best results of your customer experience analytics:
- Decide your goal
- Compile customer data
- Visualize collected data
- Select an analytics process
- Employ the insights
Let’s take a closer look at each of these measures below!
Decide your goal
Before you even begin to collect data or look at customer experience analytics, you must first extrapolate what you’re trying to identify. You must set SMART goals to ensure that you understand the data points that reflect customer needs and business goals.
Collect customer data
When analyzing customer experience data, you will typically consider two main types of feedback: direct and indirect.
Direct customer feedback – Direct customer feedback consists of metrics like:
- Net Promoter Score (NPS)
- Customer Satisfaction Score (CSAT)
- Customer Effort Score (CES)
- Voice of the Customer (VoC)
These are the CX analytics that most product marketers think about as they offer a direct understandings of customer behavior. Direct customer feedback could also comprise responses you receive on social media or comments from feedback surveys.
Indirect customer feedback – Rather than monitoring behavior, indirect customer feedback is influenced by customer behavior. This includes metrics like:
- Average Handle Time (AHT)
- Customer Lifetime Value (LTV)
- Average spend
- Customer churn rate
- Customer renewal rate
Whenever you calculate the LTV, you get an indirect look at how delighted customers are with your product (since they wouldn’t continue paying for a flawed solution, much less upgrade their subscription).
Other ways to accumulate indirect customer feedback include social listening, customer review monitoring, and analyzing voice chat transcripts.
These data points may not be as direct as NPS or CSAT scores, but they’ll help you drill down on the business outcomes that result from the customer experience.
Visualize collected data with different dashboards.
Once you have gathered data on customer satisfaction scores, lifetime value, and churn rates, then it is time to visualize everything using different dashboards.
Choose an analytics method and analyze customer data.
There are various data analytics solutions and procedures that you can use to filter through your customer analytics insights. Each process has pros and cons, so you must be acquainted with the options available to you.
A few different analytics processes to consider include:
- Descriptive analytics. Descriptive analytics uses real-time and historical data to spot trends and the relationships between certain metrics.
- Diagnostic analytics. Diagnostic analytics uses data to understand why certain events occurred, whether a rise in churn rates, a reduction in lifetime value, or other shifts in the makeup of your SaaS business.
- Predictive analytics. Predictive analytics uses models and algorithms to forecast future performance or the probability of certain outcomes.
- Prescriptive analytics. Prescriptive analytics uses data to figure out what the best course of action is and make decisions based on multiple factors.
Which one you go with will ultimately depend on the data you collect, which insights you expect to gather, and the business outcomes you are trying to achieve. For instance, predictive analytics is often adequate for businesses attempting to decrease risk or lower costs.
Use the insights to improve customer experience.
Finally, it is time to use your conclusions to improve the customer experience. Remember, collecting and analyzing data is only beneficial if you utilize those insights to make everlasting, favorable changes to your product.
Collecting customer journey analytics but never making changes to the onboarding process or customer engagement strategy would be a total waste of time. As such, you should proactively fix negative patterns you recognize and double down on the features that get new customers in the door.
Conclusion
CX is quintessential to sustaining customers, and various industries are placing importance on data analytics to better comprehend customer behavior, preferences, and needs. You can use this information to create better products and services. Data analytics can help you improve the customer experience by reducing friction, personalizing the journey, and adapting your marketing based on the needs of your users. So, if you thought data analytics was required only for those marketing campaigns, it is time to rethink your strategies!
