Zenken Sales × ChatGPT Enterprise (Global Niche Top Division)

Hello — Okada from Zenken’s AI division here.

This is the third installment in our case-study series following our press release on April 24, 2025 — “Zenken cuts external outsourcing costs by ¥50M/year through company-wide ChatGPT Enterprise rollout.” Today we’re sharing the results from the sales department (Global Niche Top division).

If this is useful, please share it.

Sales department case studies

Case 1: Faster list building and proposal preparation

Before: Building target lists and prep work for proposals required combing through product pages and doing manual market research, which ate significant time. Proposal preparation tended to be person-dependent. With so much time going into information gathering, there was less left for actual strategy.

After: Using ChatGPT, summarizing product pages and auto-extracting strengths and target customers became possible, sharply speeding up proposal prep. Market research and competitive analysis became more efficient, lifting the quality of proposals. With less time spent on information gathering, more time could go to strategic work. List-building productivity rose 1.5x, freeing up bandwidth for strategy. Time spent designing the structure of a proposal dropped to about one-third, broadening the depth and breadth of thinking and producing higher-quality proposals and sharper questions for clients.

Case 2: Faster email drafting and more outreach per day

Before: Writing sales emails took time, capping daily outreach. Reps had to manually review service pages and craft target-specific messaging, and message quality was inconsistent.

After: Just by pasting a URL into ChatGPT, reps can pull product strengths and target customers automatically. Email-drafting time dropped sharply and outreach volume rose. Daily outreach went from 25 emails to 40 — about 10 more per day — without sacrificing quality, leading to more meeting opportunities. Time spent on email replies and script creation dropped to one-third, and the cold-start “0 to 1” problem of staring at a blank email shrank.

Case 3: Better meeting prep and discovery

Before: Pre-call discovery prep and customer understanding took a lot of time, blocking real depth in the meeting itself. Reps were stuck doing surface-level checks like “look at the homepage” and “check SEO state.”

After: Reps can now pull historical ad activity, news, and industry trends instantly, going into meetings with much sharper discovery. “You really did your homework” is a comment they hear more often, building trust faster. Better understanding of the client’s products leads to more pointed questions. Productivity in market research went up, allowing question-asking that’s grounded in industry understanding and hypothesis. Better pre-meeting customer understanding lifted proposal accuracy. Meeting-minutes drafting and post-call communication became smoother too — lifting work efficiency dramatically.

Case 4: Overseas market research and stronger international proposals

Before: Gathering and analyzing overseas market information took massive time. Competitive research and market-trend awareness were thin. The English-language barrier made information gathering inefficient.

After: English-language information can be gathered quickly, with related data organized and analyzed. Competitive research and market-trend understanding became fast, sharply accelerating decision-making for international expansion. Proposals can be produced that account for each target market’s culture and competitive environment, leading to more compelling presentations. Research time on overseas competitive bids and competitor analysis dropped 50% with ChatGPT support, freeing up more time for analysis itself.

Case 5: Less person-dependence, stronger team output

Before: Quality and outcomes depended heavily on individual reps’ skill and experience, leading to inconsistent proposal quality. Internal rules and information were scattered, and finding the right reference took time.

After: With GPTs embedded into workflows and templated, output quality stays consistent while the floor lifts across the team. Anyone can produce work above a baseline level, helping with knowledge sharing and reducing person-dependence. Time on rote work dropped, freeing time for individual development. Internal rules and information can also be consolidated into GPTs, so any team member can pull what they need instantly via dialogue with the AI — a major productivity gain.

Quantitative impact on workload

Task	Before (hrs/mo)	After (hrs/mo)	Saved	Reduction
Market and competitive research	80	20	60	75%
Sales-email drafting and replies	60	30	30	50%
Proposal materials and prep	60	20	40	67%
Discovery prep and notes	30	10	20	67%
List building and customer information	30	15	15	50%
Meeting minutes and reports	20	5	15	75%
GAS / formula authoring	10	2	8	80%

Summary of the time-savings impact

• Average reduction: 62%
• Where the savings came from: automating information gathering and analysis, faster writing, automating repetitive work
• Biggest reduction: GAS / formula authoring (80% saved)
• Smallest reduction: email handling and routine writing (about 30–50% saved)

Perceived productivity gain

• “5x”: 4.5%
• “3x”: 9.1%
• “2x”: 27.3%
• “1.5x (about 33% time saved)”: 54.5%
• “No noticeable change”: 4.5%

※ 95.5% of respondents felt productivity gains. Daily action counts rose by about 30–50%, which translated into more sales activities and more meetings. Faster market research and proposal prep mean reps can run more sales activities — but workload has also crept up alongside the higher activity level.

Established usage patterns

Sales activity

• Faster understanding of target companies’ strengths and products
• Better, more efficient email drafting
• Automated industry and market analysis for proposals
• More efficient pre-call discovery prep
• Faster, more accurate cannibalization checks

Information gathering and analysis

• Automated market and competitive research
• Industry-trend awareness and insight
• Customer-persona analysis and pain-point identification
• Faster information summarization for decision-making

Document and communication improvement

• Faster, higher-quality meeting-minutes drafting
• Email-tone editing and optimization
• Faster internal reports and forms
• More efficient multilingual handling

Technical use

• GAS / macro generation and optimization
• Formula authoring
• Data-analysis automation
• Faster system development and improvements

Thinking partner / sparring

• Idea generation
• Pre-conversation organizing of thoughts before talking with managers and peers
• Faster hypothesis generation and validation
• Better decision-making process