Enterprise data forms a key part of the modern-day digital transformation strategy. However, when it comes to leveraging the value that data represents for businesses today, what happens after your data strategy has been set into action makes all the difference. This is true for businesses across all industries.
Gartner had predicted that by 2020, more than 86% of enterprises would be competing solely on customer experience (CX). However, digital businesses have already moved past that point. Now, nearly all business stakeholders, right from customers to employees, expect great experiences, similar to those provided by top tech companies, across all their digital touchpoints. In conjunction with the rising value of customer delight, this statistic serves as a key motivator for bridging the gap that keeps enterprises from delivering the degree of satisfaction they want to impart to their customers through their services.
However, no amount of human-backed effort will be able to bridge that gap simply because of the massive scope of enterprise data, and that is exactly where AI comes in. When it comes to turning your enterprise data into business value, leveraging AI in your digital or CX transformation strategy will determine the end results - 90% of companies achieve 15% higher revenues when they implement AI use cases over the course of their digital transformation initiatives. Enterprise data is usually siloed within enterprises, and bringing it together and sprinkling it with a liberal dose of AI can drive significant business value.
Here’s how you can do it too.
Redefining the potential of data with AI
AI is a key technology in the playing field of CX today - and great CX rests on company-wide orchestration excellence that comes from more than optimal process conduction powered by technology. For example, consider all those pieces of information about a customer who browsed through your product listing via a channel partner’s website and, three days after, purchased a couple of products through yet another channel and requested a service upgrade on one of them.
In the digital business environment, defined by skyrocketing expectations today, that customer would appreciate if the enterprise could connect the dots and use these behavioral insights to personalize the service rep’s interactions in line with the history of their relationship with the brand in question.
Moreover, these interactions represent a potential for building a relationship with the customer through offers on upsells and cross-sells and mending the relationship where things might have gone south. A high-value customer who received a faulty product after waiting for delivery for three days surely calls for higher levels of attention and, more importantly, the right actions than simply enquiring about delivery feedback. However, achieving such customer experience standards requires enterprises to deliver the next best actions at each point of decisioning for your sales and service reps.
AI helps you uncover those answers from your enterprise data through a robust data strategy. Here are three high-value use cases of AI that assist digital businesses in attaining the promised potential of their enterprise data today.
#1 Know your customer with a 360-degree customer view
To deliver interactions and journeys that make sense to the customer on a personal level, the customer’s digital touchpoints must be crunched through AI models. This will help determine what your customer’s channel preferences are, when they are willing to talk to your rep, what their satisfaction levels are, their transaction and interaction history with your business, and what they might be looking for next - in other words, a 360-degree view of every customer.
This requires marketing, sales, and service functions to be integrated under the umbrella of customer success. Your reps must be empowered with all the background on your customer through orchestration platforms that predict and determine the next best actions. This will help them keep your customer satisfied and coming back. As such, predictive insights must be delivered at the point of action at a granular level to power personalization for each customer rather than each segment.
#2 Maximize upsell and cross-sell
Delivering the next-best actions to your CX champions is one thing, but personalizing each customer’s journey is a different ballgame. Consider those loyal customers browsing through your renovated website, and are unable to find the item they used to purchase periodically. Not only should each channel deliver what the customer wants in an easily discoverable format, but also what they might buy. Banking journeys that leverage the milestone-oriented journeys of customers to predict their propensity to buy insurance products, loans, or invest in specific instruments deserve mention here, as much as the retail stores that place related products close to each other to maximize upsell and cross-sell.
In B2B scenarios, these offers and recommendations must be delivered on an account basis of service or sales interactions. Still, the core technology powering them remains the same - AI models churning data streams incessantly to predict what the customer is most inclined to buy at that moment.
#3 Efficient prioritization of interactions
Beyond all the right offers and meaningful interactions, efficient prioritization that focuses on the business value that each customer or account brings to your table is key to contextualizing the efforts of CX orchestration. For instance, a customer that brings repeat business to the enterprise may be frustrated about a service. This requires immediate attention not only because of the potential business that could be lost with their de-boarding, but also due to the negative publicity it might bring to the product in question.
Prioritizing based on customer satisfaction, their degree of influence across their social circles, and their lifetime value are just some of the few factors that can be leveraged to internalize the prioritization of interactions that your business conducts with the customers - through service reps or automatically. Moreover, this prioritization should be internalized within orchestration platforms, and high-priority interactions that deserve human attention must be delivered as tasks in the form of notifications to the most suited personnel for the task in question. All these propositions are made possible by AI models that work underneath the enterprise applications that orchestrate business interactions on a day-to-day basis.
With AI making significant leaps in the customer experience arena, digital business is now ripe for impactful transformation that is bound to bring significant value to the table. The times call for a reinvention of your CX transformation journey with artificial intelligence. This technology, and its use cases, which have demonstrated high RoI across all industries, are the key to squeezing the most out of your enterprise data as your data strategy goes from theory to practice in earnest.