Digital Intelligence To Drive Repeat Customers And Prevent Churn
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Enterprises that use a customer journey management approach are able to not only visualize and measure churn, but also understand the root cause and use that information to orchestrate optimal experiences. Sophisticated journey management software includes journey analytics capabilities, which help you improve your ability to identify at-risk customers and thereby reduce customer churn. By gaining a data-driven understanding of customer preferences and the best ways to reduce friction in particular situations, companies can more easily identify and prioritize opportunities for improvement.
Customer journey analytics can pinpoint the drivers of customer satisfaction in a way that traditional analytics cannot. By understanding and quantifying what matters most to your customers, you can consistently provide a superior customer experience and measure their impact on customer churn and other key quantitative metrics like online sales, repeat purchase rate, and others.
Implementing customer intelligence for your business needs precision of technical skills, domain know-how and resources. Large volumes of internal and external digital data are optimized to show results by focusing resources on the highest-value customers. When your data is dispersed in different formats, segregating what is useful is labor-intensive and time-consuming. Data has to be filtered and cleansed prior to a fair analysis of actionable insights.
As reported by Bain & Company to Bloomberg, more than two-thirds of Fortune 1000 companies use Net Promoter Score to evaluate the likelihood of customers churning. The evaluation determines the need for proactive activities to prevent customer churn by targeting the unhappy customer and addressing any negative issues before they abandon ship or worse, damage your brand with negative word-of-mouth. According to consultant Estaban Kolsky, 11% of customer churn can be avoided if the business simply reached out to the customer.
Close the loop on your campaigns by establishing a clear line of sight between your marketing initiatives and the results they deliver. AppCard uses artificial intelligence and smart data capture to help you generate persuasive customer relationship marketing communications that drive results. If you're not launching marketing campaigns based on customer identities and purchases, your program is nothing more than a discount system. AppCard makes your data more actionable by delivering personalized messages to your customers on a 1:1 basis that increase customer satisfaction and earn true loyalty.
The perfect addition to any churn rate dashboard, RPR is a retention metric that provides a clear indication of the percentage of customers that have bought goods or services from your businesses more than once. A powerful means of quantifying customer loyalty, analyzing your RPR allows you to accurately assess the performance and impact of your various consumer retention initiatives. Using the RPR, you can see which segments of your customers are making the most repeat purchases, tweaking your marketing campaigns and messaging to inspire customer loyalty across a wider cross-section of your audience.
If repeat customers are churning, you should focus your CX strategy on customer interactions. Look for upselling and cross-selling openings. You might also think about loyalty programs and incentives to keep valuable customers in the fold.
Most businesses spend a significant sum of money on acquiring new customers but very less focus goes into ensuring that customers continue to make repeat purchases. Some experts even suggest that more focus needs to go into higher customer retention and lower churn rate as a business grows.
Existing studies show that various thinking AI can be used for this purpose. Examples include targeting customers using a combination of statistical and data-mining techniques (Drew et al. 2001), screening and targeting cancer outreach marketing using machine learning and causal forests (Chen et al. 2020), optimizing promotion targeting for new customers using various machine learning methods (Simester et al. 2020), identifying the best targets for proactive churn programs from field experimental data using machine learning techniques (Ascarza 2018), and profiling digital consumers for targeting using online browsing data (Neumann et al. 2019). 2b1af7f3a8