The Value of Moving AI and Analytics to the Front Office

Side effects. Usually, deemed negative, right? While typically undesirable, side effects can produce positive benefits as well. For instance, off-label drug prescribing whereby physicians prescribe a medication for something other than the FDA-approved use because it’s been found to be beneficial in treating other medical issues too. Well, the same concept can be applied to analytics.

There is a lot of discussion currently around the adverse side effects of AI and analytics—hallucinations, bias, and outright deception, among other things. But organizations have used various forms of AI and descriptive and predictive analytics in the back office for decades to do things like model propensity, combat churn, and plan and optimize processes more effectively. Well, a side effect of this is that we are now seeing advanced analytics and AI being brought into the front office to power things like higher degrees of personalization in customer engagement.

This shift began a few years back with AI techniques like natural language processing, sentiment analysis, and text analytics being brought in to bolster front-end channels like chatbots. Now AI techniques are slowly making their way into all things marketing and customer engagement—copy, imagery, email, and advertising creation. As this happens, organizations are seeing a  digital shift occur—with a somewhat natural decline in traditional channels like direct mail, call center, and in-store.

So what technologies are moving into the front office from an AI and analytics perspective to enhance personalization and ultimately CX?

The most popular are these:

• Text analytics. Allows organizations to understand consumer text to infer consumer preference and emotion.
• Natural language processing. Integrating this technology into chatbots, call centers, IVRs, and other voice-based consumer digital interaction tools allows organizations to understand and respond without human interaction.
• Sentiment analysis. Allows for the understanding of sentiment or emotion from both a voice and text perspective.
• Visual and voice recognition. These technologies aid in improving the customer experience from a point of sale/kiosk/ATM perspective.
• Visual and voice biometrics. Used more for fraud and risk applications, these technologies indirectly improve the customer experience by reducing identity theft issues.
• Real-time decisioning. Enables activities such as next best actions, real-time agent advisory, and real-time offer delivery.
• Beacons and geofencing. These allow organizations (with permission) to understand where a customer resides in a physical location like a retail store or petrol station based on a mobile device and subsequently deliver offers and messages to that device.
• AI-based optimization and customer routing. These technologies (i.e., customer journey optimization) will be used to improve outcomes (conversions, LTV, etc.) by using analytics to guide customers to end conversion events versus pushing them down a brand-based predefined path.

Several technologies that you might expect to see on the above list are not present purposefully. These include customer segmentation, fulfillment automation, workforce scheduling, PII redaction, virtual onboarding, and generative AI—because, in my mind, these play more of a behind-the-scenes customer experience and personalization role than they do a front-facing role during a customer interaction.

Positive Effects of AI and Analytics

Other than the obvious benefit of creating a more contextually relevant customer experience, what are the organizational positive effects of bringing AI and analytics more into the front office? 

Speed. With many AI and analytics technologies comes automation. Automating processes from a customer engagement perspective allows campaigns, content, messages, and interactions to get to market faster. Additionally, technologies like AI-powered chatbots can provide a speed of response to allow customers to self-serve when it comes to problem-solving, even if in the middle of the night. Consumer demand and expected time to resolution of customer service issues are only continuing to increase, so it’s important that the speed of enabling technologies increase as well.

Scale. One of the long-standing issues organizations have had is scaling to meet customer demand. Whether it’s a contact center, an online ticket portal, or a service or support site, being able to process numerous engagements or interactions concurrently has been a challenge. Technologies like customer routing and enterprise decisioning, which rely on AI and analytics, allow for personalized customer experiences to be delivered at scale across engagement channels. We often see that these technologies are then ported beyond marketing, service, and support to other departments in the organization.

Productivity. Given current economic conditions, many organizations are looking at how they can be just as if not more effective with current head count levels. AI and analytics technologies increase productivity levels at organizations by doing the following things:

• Empower customers to self-serve and not rely on an employee.
• Empower marketers to automate processes, setting them to run once.
• Deliver personalized content and engagement recommendations to the front line.
• Give marketers the ability to focus on strategic and not tactical operations.
• Collect and analyze data automatically to provide seamless journey interactions.
• Use data to model propensities to purchase, churn, and engage.
• Decrease employee burnout through reduced workloads.

Productivity is perhaps the most important side effect of bringing AI and analytics into the customer engagement realm.

Operationalization. Bringing AI and analytics from the back office to frontline customer experience and personalization initiatives is the true proof that organizations can collect relevant customer data, apply insight to that data, and then use that data via engagement to improve the business. By operationalizing this data via engagement action, the longer-term benefits and competitive differentiations of AI and analytics are realized, which include improved business metrics around revenue, profit, margin, loyalty, trust, and customer lifetime value.

There is no doubt that AI and analytics will continue to make their way more and more into customer engagement practices. While the outcome, in my opinion, will be overwhelmingly positive from both a customer and brand perspective, there will be certain situations that will question organizational ethics and oversight.  It’s imperative for brands to have a “responsible marketing” style framework in place, one that is focused on educating both employees and consumers on how your organization is using customer data, technologies, and other customer engagement resources in a responsible manner.  Transparent education will result in the ability for brands to limit their liability as we enter this exciting world of AI and analytics for customer experience personalization.

Jonathan Moran is head of MarTech solutions marketing at SAS, covering global product marketing activities with a focus on customer experience and marketing technologies. Prior to SAS, Moran gained more than 20 years of marketing and analytics industry experience at both Earnix and the Teradata Corporation in pre-sales, consulting, and marketing roles. Over the span of his career, Moran has had the opportunity to not only architect, develop, demonstrate, and implement analytical software solutions, but also to work on-site with Fortune 500 customers across industries, helping them solve unique digital marketing analytics business issues.