3 Business Drivers to Deliver Significant ROI from AI Search Personalization

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Even before ChatGPT and large language models (LLMs) leaped to the front of emerging technologies, I advised CIOs, CDOs, IT leaders, and product managers to prioritize AI search as a digital transformation force multiplier. I wrote another article on how AI search capabilities can deliver superior customer experiences, create growth opportunities, improve employee productivity, and reduce technical debt.

Consider LLM’s impact on customers and employees as an accelerant, and I believe ChatGPT will energize the consumerization of natural language search. ChatGPT and other Generative AI platforms raise the bar on what customers expect on websites and employees in their workflow tools.

But there’s a gap between AI-driven expectations and today’s customer and employee experiences. Employees will be even more frustrated when they have to search multiple platforms, and customers will abandon carts and drop subscriptions when the tiny keyword search box can’t help answer their questions. People now expect AI search capabilities, including natural language querying, smart snippets, recommendations, and personalized experiences, to answer their questions quickly and accurately.

While there are many business drivers to improve search experiences, those drivers have to show financial returns to land on the top of a CIO’s investment priorities or a CDO’s product roadmap. The challenge, like many digital transformation enabling platforms, is that AI search is a key ingredient to many business drivers, but it requires collaboration and culture change to realize the business and financial impacts.

As a Digital Trailblazer, I seek to quantify the business drivers with KPIs, connect these KPIs to financial measures, and rally the organization on the transformations required to deliver targeted outcomes.   

Here are three business drivers for AI search personalization and how to develop a program to deliver financial impacts and ROI.

1. Increase in lead conversion, purchases, and customer loyalty

Every customer-facing experience should have clear goals of how successful interactions lead to revenue. Ecommerce and media websites seek to maximize purchases and subscriptions, while many B2B sites use website engagements as the start of lead generation funnels.

What these sites and mobile applications have in common is that they all seek to pique visitor curiosity and drive product interest because it often requires several visits and interactions to convert prospects into paying and loyal customers.

Product managers should implement A/B testing to demonstrate how AI search impacts customer journeys and targeted KPIs. Modernized search experiences simplify integrations with common SaaS, CRM, CMS, and other platforms, so starting with a small experiment is easy. Here’s an approach for executing a multi-month AI search pilot that can help boost today’s customer engagement metrics.

• Design a pilot that’s at least 2-3 times longer than your typical conversion duration, so if a customer journey is typically one month, then aim for a three-month or longer pilot. 
• Establish an agile sprint cadence to support at least twelve experiments. For a 3-month pilot, that’s a weekly cadence.
• Aim to make one change and no more than 2-3 changes per cadence. You could change the A/B rules, add more content sources, change the UI, or add new AI search capabilities. 
• Track the conversions and compare results from people experiencing AI search versus those using your legacy search capabilities.

Many legacy customer experiences were developed using search engines with hard-coded heuristics that require ongoing tuning by software developers. AI search engines regularly outperform these approaches with ML models that connect customer information and visitor behavior data with targeted business outcomes. The A/B experiments establish a data-driven approach to prioritizing the transition and help garner support for the transformation.

2. Improve customer satisfaction with self-service and AI-enabled service agents 

AI search capabilities can also drive improved customer satisfaction scores (CSat) by enabling the “back of the house” with more comprehensive information, machine learning capabilities, and better tools.

Need inspiration or benchmarks? Check out how Salesforce achieved a 90%+ self-service success rate and what steps athenahealth took to improve their support agent’s resolution by 75%.

Organizations can improve their CSat metrics by 20% or more by replacing rule-based chatbots with an AI-enabled agent-assisted service that can access customer data and comprehensive product information.

When you create customer self-service capabilities or enable AI service agents, create customer segments that utilize these capabilities and track their CSat. This ROI calculator for service and support can help forecast a financial impact. KPIs such as faster resolution times, fewer escalations, and improved onboarding can all improve CSat while reducing costs.

3. Accelerate employee onboarding, learning, and engagement

Three employee onboarding KPIs are highly correlated: employee satisfaction (ESat), time to productivity, and employee performance. To improve these metrics, employers commonly make investments in onboarding programs and training, but at a high creation and delivery cost.

And since digital transformation drives changing priorities and frequent workflow changes, smart employers believe that onboarding and time to productivity are not one-time objectives. Digital Trailblazers know transformation is a core organization competency, so the easier and faster they can support learning, the more likely they can accelerate delivering business impacts.

But learning is not just about training and offering certifications. 

In the 2022 workplace and learning development trends, 68% report having under $3,000 per employee for training, and 40% of employees want formal training less than twice a year. Why? According to the report, employees claim they are trained in compliance (70%) and soft skills (51%), but these areas aren’t the employees’ top priorities. Their top requests for making learning more effective include delivering more guidance relevant to their jobs (38%) and getting access to up-to-date content (31%).

Employees are telling you what they need to become successful, and it comes down to having self-service, job-relevant learning based on accurate, comprehensive information. That won’t work with a person-curated intranet or searching a half dozen tools only to find the information in them is outdated. 

The Digital Workplace is about having centralized information accessible directly in an employee’s workflow tools. Can a sales professional search across the enterprise from their Salesforce dashboard? Can people in customer support, IT services, or legal search ServiceNow for comprehensive information about your company’s products and related services?

Ask the right questions in your ESat surveys about self-service learning, and you can measure the impacts on employees. You can also correlate an employee’s utilization of AI search in the digital workplace with their time to productivity and performance measures.

To summarize, Digital Trailblazers have three key areas where AI search personalization can deliver ROI:

• Growth in customer experiences that drive leads and sales 
• Improved CSat that also lowers customer support costs
• Increased ESat with faster and higher productivity

LLMs will accelerate people’s expectations around search experiences. You can be a laggard risking disruption or be a Digital Trailblazer and use pilots to measure results and deliver ROI.

This post is brought to you by Coveo.

The views and opinions expressed herein are those of the author and do not necessarily represent the views and opinions of Coveo.