Customer experience (CX) has come a long way in a short time. Most people hadn’t even considered CX as a way to enhance customer engagement, differentiate their companies or leverage customer journey maps to improve their CX.
Now we see many companies using customer journey data as a guiding factor in customer acquisition, engagement, and retention strategies. Companies sought to optimize touchpoints in individual channels, like customer service, however, most companies did not realize the importance of seeing the customer journey as a whole.
Now, companies have been considering the entire customer journey, from initial contact, purchases, multi-channel touchpoints, pain points, and other relevant information—as the foundation for the engagement. The omnichannel environment enables companies to begin to construct a complete, detailed and data-driven view of each customer to inform seamless, consistent, and more profitable customer interactions.
We work towards constructing an end-to-end journey not only from the view of what a company sees but most importantly from how a customer perceives it. After all, it’s their journey and if it’s not right, the company pays for it.
“Whenever your customer experiences pain, it costs you money.”
The Need for Artificial Intelligence (AI)
There is a lot of data surrounding the customer. There is marketing/campaign data, behavioral data, sales data, financial data, customer service data, product usage data, and not the least of which is the customer journey data. How is a company supposed to put all of this data into context, filter it, and find meaningful insights that help improve overall CX?
The answer is actually fairly simple – first, focus on the customer journey and prioritize what needs to improve in the customer journey that actually affects your financial results the most. This might be fixing touchpoints that reduce the churn rate if that affects financial growth most. Or it might be solving a problem with messaging the product to increase customer acquisition. Or for some, it might be a packaging or pricing issue that both increases acquisition and reduces churn.
Typically, there are about 150+ touchpoints in a customer’s journey across 8 broad stages (Aware> Interest> Decide> Buy> Use> Support> Complain> Renew/Leave). Out of which nearly 12% are pain points and 8% are Moments of Truth (MoTs). If we do a quick math, that’s about 20 pain points and 10+ MoTs. We would want to focus on fixing these pain points that affect the customer most intensely and are impacting financial outcomes the most. That’s quite a lot to work upon and overwhelming if everything becomes a priority at the same time. We need a smart and data-driven way to solve two major problems. This is exactly why we use AI to address these challenges.
- To make sure we have discovered all the possible and relevant touchpoints across the customer journey
- To prioritize the Pain Points
- To evaluate the financial impact of these touchpoints
Within the realm of AI, Machine learning and Natural Language Processing (NLP) are the most utilized techniques. To solve above-mentioned problems, we use these technologies regularly and intensely on behalf of our customers.
We leverage customer and stakeholder interviews, analyze various business processes, customer survey feedback, as well as data inputs like product usage patterns, customer support and issue resolution data, revenue, retention, and cost data. We use analytics, NLP, text analytics, and data mining to ensure all relevant touchpoints are analyzed in the journey. Afterward, we use a data-driven approach to prioritize the pain points and highlight the touchpoints that are impacting both the customer experience and the company’s financial results.
With the use of artificial intelligence, massive amounts of data from multiple sources all come together to form insightful conclusions and thoughtful actions that will enhance every aspect of customer engagement.