IBM Software / SaaS Global

IBM Global Sales Incentives

Customer Service Automation · Enterprise Knowledge Search

IBM Global Sales Incentives needed to improve how it handled seller incentive inquiries because its chatbot still escalated too many questions and manual analyst reviews were taking far longer than target service levels.

28% of inquiries went into hold status
326,525 questions received from January to September 2023
82.5% improvement in time to resolve an inquiry
92% and 93% answer rates in 2021 and 2022

Reported impact

Business outcome

GSI sharply reduced inquiry-resolution time, increased the percentage of questions answered automatically, and gave sellers a faster and more personalized support experience.

  • Customer Experience
  • Productivity Improvement

Solution

The IBM Global Sales Incentives team combined IBM watsonx Assistant and IBM Process Mining to redesign how seller incentive questions are answered and escalated. AskIncentives already served as the digital front door for questions about incentives, commissions, sales plays, and sales targets, but unresolved issues still required analysts to manually review inquiries. To fix that, GSI partnered with digital strategy teams to mine the bot's conversation history and the inquiry-tracking tool, revealing excessive hold states, poor root-cause categorization, and slow analyst assignment patterns. Those insights were used to expand the bot's knowledge base, consolidate categories, improve ticket routing, and add personalized profile data such as territory, quota, last payment, and achievement at the start of an interaction. The resulting design links conversational support, process intelligence, and workflow optimization so more routine questions can be answered immediately and analysts can focus on the genuinely complex cases.

Data flow

Seller questions enter AskIncentives through watsonx Assistant, bot and inquiry-tracking logs are mined by IBM Process Mining, and the resulting insights feed knowledge-base expansion, category cleanup, and improved analyst-routing logic.

Solution architecture

2 components · 2 layers
  1. Compute
    • IBM Process Mining Analyzes bot and inquiry-workflow history to identify bottlenecks and recommend changes to routing and content.
  2. Serving
    • IBM watsonx Assistant Runs the AskIncentives chatbot that answers seller questions and delivers personalized incentive information.

Architecture clues

  • Personalized seller profile data is injected into the interaction at the start of the conversation.
  • The next phase extends the pattern toward more complex answers with watsonx and Watson Discovery.
  • The redesign mined both bot history logs and inquiry-workflow data rather than tuning the chatbot in isolation.

Evidence from the source

"28% of inquiries went into hold status"
"82.5% improvement in the time required to resolve a query"
"the AskIncentives chatbot answered 96% of the 326,525 questions it received"
"the bot receives tens of thousands of questions"
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. IBM

    IBM Global Sales Incentives customer story

    First-party
    https://www.ibm.com/case-studies/ibm-gsi-watsonx
    Source type
    Vendor-published customer story
    Published
    May 14, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.