unknown

Marketing Personalization · Sales Automation

A large e-commerce platform wanted more of its small and medium-sized sellers to buy targeted advertising, but its self-service ad portal was not educating merchants well enough, sellers were spending little to nothing on ads, and the company lacked the AI-assisted tooling and operational support needed to scale ad performance.

more than 30% year-over-year growth in ad spending

Reported impact

Business outcome

The retailer's advertising business gained stronger merchant adoption, more effective seller outreach and a higher-growth revenue engine, with AI and automation helping both the company and its SMB sellers improve campaign performance and monetization.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Accenture helped the unnamed e-commerce company rebuild its advertising operation as a blend of AI-enabled tooling and human campaign support. The team upgraded the self-service ad portal so merchants could understand and use advertising workflows more easily, then added real-time optimization capabilities driven by data, AI and generative AI. Sellers receive targeted ad suggestions designed to improve return on ad spend, while AI-generated post-call summaries and live campaign insights support the teams helping merchants improve performance. Accenture also deployed One CRM and OneGlass so account teams could manage conversations, activities and follow-ups in one place and automate personalized emails, campaign recommendations and WhatsApp messages at scale. Around that stack, the company built strategists, media managers, analysts and support agents, creating an operating model where AI guides outreach and optimization while humans coach sellers on the best next action.

Data flow

Seller activity and campaign data feed the portal, CRM and AI services, which generate advertising recommendations, post-call summaries and campaign insights. Account teams then use One CRM and OneGlass to follow up through email, recommendations and WhatsApp messages while merchants act on the optimized advertising guidance.

Solution architecture

4 components · 3 layers
  1. Compute
    • AI and gen AI initiatives Generate post-call summaries, campaign insights and targeted recommendations for sellers and support teams.
  2. Serving
    • self-service ad portal Lets SMB sellers manage and learn advertising workflows through a redesigned user experience.
  3. Orchestration
    • One CRM Coordinates account conversations, activities and follow-ups for the advertising team.
    • OneGlass Supports account-management workflows and centralized seller engagement tracking.

Architecture clues

  • Generative AI produces post-call summaries and real-time campaign insights.
  • One CRM and OneGlass are the coordination layer for seller engagement.
  • The delivery model includes campaign strategists, media managers, data analysts and support agents.
  • The solution mixes a self-service advertising portal with AI-assisted agent workflows rather than relying on self-service alone.

Evidence from the source

"AI and gen AI initiatives offer post-call summaries and real-time campaign insights."
"Together, Accenture and the company created the technical infrastructure, tools and a team necessary to bring its advertising business to new heights."
"We also deployed One CRM and OneGlass solutions to help the ad team seamlessly manage conversations, activities and follow-ups with the accounts."
"more than 30% year-over-year growth in ad spending"
"the new solution taps into the power of data, AI and gen AI to provide real-time, customizable strategies for advertising optimization and seller interactions"
Transparency Evidence & methodology 90/100 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 value90/100
  • 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 impact15/25
    1 metric 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
    5 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. Accenture

    unknown customer story

    First-party
    https://www.accenture.com/en/case-studies/software-platforms/building-better-business-with-data-ai
    Source type
    Vendor-published customer story
    Published
    Aug 16, 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

  • Published on source page Aug 16, 2024 · Accenture · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.