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