This article shows why data quality is fundamental for Customer Lifetime Value and Client Satisfaction and how Artificial Intelligence will achieve it.
Growing a business is often connected to the attraction of new customers and potential leads. However, there is evidence that building loyalty with 5% more customers brings a raised average profit per consumer of between 25% and 100%. The importance of customer service, thus, cannot be underestimated.
Experiential marketing is already a big part of business strategies. 83% of organizations that believe it's crucial to satisfy consumers also experience revenue growth. To deliver a great customer experience and service, it is essential to make intelligent data-driven decisions.
The cost of acquiring a new customer may be 6-7 times higher than the cost of retention. Customer Lifetime Value is a metric you should be looking after to keep track of a predicted revenue that a customer will generate throughout your relationship. The main point is that the higher the CLV, the more profit you produce.
How to increase CLV? Make the customer experience personalized and frictionless. If users are satisfied with the service, they are more likely to recommend it to their friends and vice versa. If the customer is unsatisfied with the product, he will spread the word. Net Promoter Score will help to measure the satisfaction and increase the CLV.
Customer success teams are just as important as the sales and marketing departments, so it is necessary to know how to improve their day-to-day operations and productivity levels.
When companies face a challenge with customer retention, they go and look for the best solutions in the market to fix it. One of the possible ways would be to hire more agents to deal with calls or messages to increase the productivity of the whole team.
Since 82% of B2B decision-makers assume that managers are unprepared for calls, another option could be to train staff before the interaction with clients.
Other resources suggest investing in a new customer service platform to facilitate operations is going to increase productivity, but there is still one element overlooked and that’s data quality.
CRMs and engagement systems have made customer success teams' jobs more efficient. However, with the appearance of more complex platforms, new challenges have arisen. With more data in these systems, the decision-making process becomes more sophisticated, putting the CLV at risk.
To make more intelligent data-driven decisions, it is essential to analyze the data and create accurate reports. According to Salesforce, 52% of companies still rely on excel spreadsheets to analyze contact center data. This statistic proves that much time is wasted on account research and data gathering rather than evaluating the insights and prioritizing the efforts. Some of the pains include:
Today's customer support is mainly about self-service, which is why many businesses have switched to proactive customer service operations.
For many years now, companies have been using reactive customer service, meaning that when a client has a problem, he or she fills out a form, calls, or sends a message to the support team to initiate a CS response.
A proactive approach in customer service means that the business addresses the customers first without consumers reaching out to the support team to resolve a problem.
Companies should make the first move to prevent any potential issues that a client could have and provide them with actionable insights. They should collect feedback, keep track of customer activity (reviews on social media, the reasons for calls, the objectives, and issues), and provide supporting material to act on inquiries.
According to Harvard Business Review, 81% of all customers try to resolve issues themselves before reaching out to the live support team. This is why having a decent knowledge base is critical for consumer satisfaction.
The support articles based on customer feedback and activity can help clients find a solution without calling or messaging the support agent. Plus, a knowledge base is helpful for the employees since it helps find the relevant solutions for each case faster and acts as a basis for machine learning techniques.
In the likely event a customer is unable to fix their problem with the knowledge base, the proactive approach behind the brand should be focusing on personalization for each account. Only 13% of customers believe that offers made by salespeople meet their needs.
Agents should consider the business need and objective of every client to increase the succession and satisfaction rates. This means gathering the updated and accurate information to provide a customized solution that fits the client's needs.
No account manager ever wants to receive bad feedback from their client and see the customer frustrated. Consumer dissatisfaction mainly comes from inaccurate information or irrelevant solutions, which circles back to data quality problems that each organization has.
Businesses report that bad data negatively affected 54% of their customer relationships. It happens because CS managers either spend too much time updating the information of contacts or researching the cases and gathering data to help them solve their specific problem.
Whether you are hiring new staff, training the new coming CS agents, or implementing a new CRM, you must take care of the data. After all, nobody canceled the GIGO concept (if the garbage is input, the output is the same).
It ensures the good quality of data to significantly improve customer success teams' productivity, saving resources and increasing the LTV of their customers.
Increased customer retention can be achieved in just a few clicks. The quick resolution to the customer support challenges in CRMs resides in the all-new AI co-pilots, just like Delpha.