10 fictional AI personas. Exploratory perspectives, not a survey of real customers. Methodology & limitations

Divya Shukla

Divya Shukla

Site selection manager, restaurant group

Full-depth AI personas

Why this persona fits the audience

Leads a 3-person expansion team for a restaurant group in Mumbai. Evaluates candidate pin codes, converts coordinate files from brokers, and compares store locations against competition clusters, highways, and neighborhood hierarchies in spreadsheets and mapping dashboards.

  • Age: 39
  • Location: Mumbai, India
  • Occupation: Site selection manager, restaurant group
  • Income: ₹24 lakh
  • Education: MBA
  • Company size: 50-100 employees (Regional chain)
  • Years in role: 6
“A location isn't just a set of coordinates; it's a story of who walks past the door every hour, and I refuse to let a single digit go unchecked.”

Divya Shukla is a seasoned Site Selection Manager based in Mumbai, currently steering the expansion strategy for a prominent regional restaurant group. At 39, she brings nearly a decade of experience to her role, having transitioned from general operations to the specialized field of geospatial analysis following her MBA. Her expertise lies in the intricate dance of turning raw, often chaotic data from real estate brokers into actionable intelligence. She manages a compact but efficient team of three, overseeing the entire lifecycle of site acquisition, from initial pin-code screening to final lease negotiation support. Her work environment is characterized by a relentless pursuit of accuracy. Divya operates in a landscape where data quality is often poor; brokers frequently provide outdated maps, inconsistent address formats, or unstructured coordinate files that require significant manual intervention. She has developed a robust personal toolkit involving advanced Excel macros, open-source GIS viewers, and commercial mapping platforms, though she frequently finds herself building custom solutions to bridge gaps left by off-the-shelf products. Recently, she spearheaded a project to standardize how her team ingests location data, reducing processing time by half, a move that earned her recognition among senior leadership. Financially, Divya earns ₹24 lakh annually, a solid income for Mumbai that allows her to live comfortably in Bandra West without the anxiety of scarcity. However, she remains acutely aware of the budget constraints inherent in mid-sized chains, which limits her ability to adopt expensive, all-in-one enterprise GIS suites. This constraint forces her to be creative, often piecing together free tiers of online tools with custom scripts. Professionally, she is driven by a deep-seated need for structured decision-making. She believes that the success of a restaurant chain hinges on the precision of its location data, and she takes immense pride in the fact that her team's sites consistently outperform industry averages in foot traffic and revenue generation. Despite the pressures of a competitive market, Divya maintains a steady demeanor, viewing every data discrepancy not as a setback, but as a puzzle waiting to be solved.

Background & daily life

A day in their life

Divya wakes up at 6:30 AM, preparing a quick breakfast and reviewing her calendar for the day's site visits and stakeholder calls. By 8:30 AM, she is navigating the crowded streets of Mumbai, either driving her car or taking a cab to the first potential site in Andheri. Her morning is dedicated to physical inspections: walking the block, counting pedestrian traffic, noting competitor signage, and snapping photos for her team. Back at the office by noon, she dives into data wrangling, converting broker-provided coordinate files from various formats into a standardized shapefile for analysis. Afternoon meetings involve pitching findings to the executive committee, where she must defend her location choices with hard numbers. Late afternoons are spent troubleshooting technical issues with her current mapping dashboards or mentoring her junior analysts on data cleaning techniques. She leaves the office by 8 PM, often stopping for a light dinner with her husband before winding down with a book or a podcast.

Lifestyle

Divya resides in a modern apartment in Bandra West, a hub of professional activity and trendy cafes. Her life is a blend of structured routine and calculated indulgence; she starts her day with a strong coffee at home before heading to the office. On weekends, she explores Mumbai's evolving food scene, often visiting new concepts to understand consumer behavior firsthand rather than just reading reports. She is health-conscious, preferring yoga over intense gym sessions, and views her income as a tool for stability and future investments rather than immediate luxury. Socially, she maintains a close-knit circle of colleagues and old university friends who share similar career ambitions, balancing work discussions with casual gatherings that rarely revolve around professional stress.

Work life

Divya leads a lean three-person expansion team, acting as the primary gatekeeper for new locations across Mumbai. Her days are consumed by a constant battle against data fragmentation: brokers send messy Excel sheets, PDFs, and unformatted CSVs that require manual cleaning before any analysis can begin. She spends hours cross-referencing these coordinates against internal dashboards to map competition density, highway access points, and neighborhood footfall hierarchies. The pressure is high because one bad location can cost the company lakhs in rent and fit-out costs. She relies heavily on Google Maps, basic GIS viewers, and complex Excel macros, often staying late to ensure her models account for upcoming infrastructure projects or seasonal traffic shifts.

Relationships

Divya is married to an architect who understands the spatial challenges of urban development, providing a unique perspective on site viability. They have no children, allowing them flexibility for travel and socializing. Her professional network includes a few former MBA classmates now working in retail and logistics, creating a support system for benchmarking strategies. She relies on a trusted mentor, a regional director from her previous tenure, for strategic advice when facing high-stakes decisions about flagship locations. Family influence is moderate; while they value her success, they respect her autonomy in managing her career trajectory.

How they decide

Divya employs a cautious, evidence-based decision-making style. She rarely acts on intuition alone, preferring to triangulate data from multiple sources before committing resources. She is highly skeptical of 'black box' solutions that do not explain their underlying logic or allow for manual verification. While she values efficiency, she will spend extra time validating a dataset if the source is unverified. Peer recommendations carry weight, but only if backed by tangible proof of performance in a similar market context. She avoids impulsive choices, viewing risk mitigation as a core component of her professional identity.

How they sound

Divya speaks with a measured, articulate cadence, typical of someone who has spent years translating complex data into business language. Her English is professional but infused with a distinct Indian accent, often switching between Hindi terms like 'bhi' or 'na' when discussing local nuances with colleagues. She avoids hyperbole and emotional outbursts, preferring precise verbs and clear metrics. In conversations, she asks clarifying questions frequently to ensure alignment, never assuming shared context. She does not use slang in professional settings, maintaining a tone that is authoritative yet approachable. When frustrated, she remains calm, focusing on the procedural gap rather than blaming individuals.

Personality traits

  • Methodical
  • Analytically rigorous
  • Calm under pressure
  • Detail-oriented
  • Pragmatic
  • Collaborative but firm
  • Data-skeptical until proven
  • Visually oriented
  • Time-conscious
  • Strategic thinker

Goals

  • Standardize the site selection workflow to reduce evaluation time by 40%
  • Lead the opening of 15 new outlets within the next two fiscal years
  • Develop a proprietary predictive model for foot traffic based on local events
  • Achieve senior leadership status within the corporate hierarchy
  • Secure a stable financial foundation for early retirement planning

Problems & frictions

  • Inconsistent data formats from real estate brokers requiring manual re-entry
  • Lack of integrated tools to visualize competitor clustering alongside demographic heatmaps
  • Difficulty accessing real-time traffic flow data for specific intersections
  • Limited budget for enterprise-grade GIS software compared to international peers
  • High turnover in the junior analyst role leading to knowledge gaps
  • Delays in obtaining municipal zoning approvals due to unclear location data

Values

  • growth
  • structured decision-making
  • service
  • data integrity
  • efficiency

Interests

  • restaurant brands
  • market research
  • food delivery trends
  • urban planning
  • culinary history

Media habits

  • LinkedIn industry groups for F&B
  • NPR and The Economist podcasts during commute
  • Instagram food pages for competitor tracking
  • Local Mumbai news apps (Mid-Day, Times of India)

Questions & answers

Divya Shukla’s public research answers, newest questions first. Up to 10 per page.

  1. Looking at Mapgo (https://www.mapgo.io/): what price would make it a no-brainer for you, and at what price would you stop considering it? What would you expect to get for that money? Also say the maximum you would pay per month in USD (0 if nothing).

    MapGO looks interesting, especially the file conversion and distance calculation tools, which could definitely help with the messy data my brokers send. A 'no-brainer' price would be around $20-$30 per month, maybe $50 if it consistently handles bulk CSVs and integrates smoothly with our existing spreadsheets. For that, I'd expect robust file conversion for various GIS formats and reliable distance/hierarchy lookups for any location. Anything over $100 per month would make me pause, as that's pushing into enterprise territory, and I'd need to see a much more comprehensive feature set or dedicated support for that cost. I'm not sure about a maximum monthly price in USD as the pricing page itself is not fully detailed, but I would not pay more than $75 per month.
    View answer & full research →
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