Paid Search Management

How we generated $1.1M in direct pipeline with Paid Search in 6 months Here’s the exact Google Ads strategy we used with a $15K/month budget. Context: Company: Series A B2B SaaS Segment: Midmarket ACV: $20K+ Goal: Demo Requests Budget: $10-15K/month Channel: Paid Search Strategy: 1/ High-Intent Keywords Our primary focus was on bottom-of-funnel keywords. Campaign set-up: - Keywords: Category + "software" or "tool" - Match Types: Started with Exact Match, Phrase Match - Bidding: Manual CPC, Switched to tCPA with conversions - Landing Pages: Simple, direct “Book a Demo” CTAs, no distractions - Device Targeting: Desktop only Results: Low volume, High conversion to pipeline 2/ Generic Keywords We tested generic variants of high-intent keywords. It generated some demo requests but was not as efficient and cut most due to poor conversion rates. What worked: - Some keywords converted - We paused all broad terms that didn’t convert - Excluding irrelevant search terms consistently - Smart Bidding strategy improves performance What didn’t work: It drove more traffic, not SQLs. Intent matters more than volume in B2B. 3/ Competitor Campaigns We targeted competitor brand names and "alternative" modifiers. Campaign Setup: - Match Types: Exact Match, Phrase Match - Bidding: Manual CPC with higher CPCs to remain competitive. - Ad Copy: Highlighted differentiators, pricing advantages, and social proof. - Landing Pages: Comparison pages with clear CTA. Results: Higher CPL, highest return. 4/ Dynamic Search Ads (DSA) We ran DSA campaigns to expand targeting. Campaign Setup: - Landing pages: Homepage & key product pages. - Exclusions: Brand terms + irrelevant pages. - Bidding: Maximize conversions. Results: Found new high-intent keywords that we added to campaigns. 5/ Retargeting Since B2B deals don’t convert on the first visit, we retargeted high-intent visitors to bring them back. Campaign Setup: - Targeted visitors who visited the website. - Demand Gen and YouTube Ads - Feature / Benefit, Capabilities, Product explainers Primary goal: Brand presence and nurturing. Reporting: - HubSpot CRM integration → Imported lead & deal data. - UTM tracking → Traced pipeline back to specific campaigns. - Google Data Studio Dashboard → Full-funnel tracking (Lead -> CW) Results: - $1.1M in direct pipeline in 6 months - Scaled from 0 to over 20 demos per month - Generated 3.55 ROAS Summary: We focused on high-intent search and competitor campaigns, testing MOFU terms but cutting those that didn’t convert. DSA campaigns helped uncover additional high-performing keywords while retargeting nurtured, engaged visitors. As conversion data increased, a shift in bidding strategies improved performance. --- If you’re a marketer in B2B SaaS, spending around $15k+/month, and need help with a Google Ads strategy Book a time, and let's chat about how we can grow your pipeline. https://lnkd.in/edUWuUfN #b2bsaas #paidads #googleads

I am running paid search for a B2B SaaS company where Google Ads is reporting ZERO landing page conversions, yet we are continuing to invest in the channel. Why? Because I have reverse engineered the website conversion paths of qualified opportunities that began with paid search, and while the buyer journey STARTS with paid search, it ENDS a week later on the demo request page or main product page. Here's a recent conversion path (real-world example): Day 1: Lands on paid search ad -- hits a landing page. Navigates to main site to check out a few more organic resources pages. Day 2: Goes back to the homepage and from there interacts with 8 other pages. Day 3: Goes to case studies, integrations and a few other blogs. Day 4: Goes back to main /products/ page and interacts with the chat widget. Says: "I want a human." Sales rep takes over the chat and books a demo request right there after answering a few basic questions. 23 digital website interactions in 4 days. Only 1 interaction came from paid search, but it was the most important -- brand discovery during a solution seeking moment. The demo request happened four days and 22 touch points later. What's the takeaway? If you're in B2B but you have the mindset of a direct response marketer, you are destined to fail. Demo requests are less likely to happen on B2B landing pages, so optimize them for helpfulness and engagement, not eCommerce cart check outs. You're selling expensive software, not basketball sneakers. #marketing

We inherited a Google Ads account burning £4K/month with zero attribution. Here's what we found. A B2B technology company came to us, spending £4,000 per month on Google Ads. They had been running the same campaigns for 14 months. When we asked what pipeline those campaigns had generated, the answer was: we don't know. Here is what the audit uncovered: 1. 62% of spend was going to broad match keywords that attracted unqualified traffic 2. Landing pages had no clear call to action for enterprise buyers 3. The same ad copy was shown to every visitor regardless of company size, industry, or buying stage 4. No remarketing sequences for accounts that showed initial interest 5. Zero integration between Google Ads data and their sales pipeline The total spend over 14 months: £56,000. The attributable pipeline from that spend: £0 confirmed. Not because Google Ads does not work for B2B. It does. But only when it is built into a system that targets the right accounts and tracks the right outcomes. The ABM Paid Media Restructure (what we built in 30 days): 1. Replaced broad keywords with intent-based search terms mapped to their target account list 2. Built account-specific landing pages with messaging aligned to each stakeholder's priorities 3. Created remarketing sequences triggered by account engagement signals, not just page visits 4. Integrated Google Ads conversion data directly into CRM pipeline stages 5. Set up weekly pipeline attribution reports so every pound of spend was accountable The lesson is consistent across every account I audit. Paid media in B2B is not a lead generation tool. It is a pipeline acceleration tool. And it only works when it is connected to named accounts, personalised messaging, and closed-loop attribution. If your Google Ads or LinkedIn Ads spend cannot be traced to specific pipeline, you have an attribution problem before you have a performance problem. DM me "PAID" and I will run a 15-minute review of your paid media setup and tell you exactly where the leaks are. --------------------------------------------------------------------------- Who am I I'm Lukas, founder of LDS Digital. What I do I help businesses build steady lead and revenue systems. What LDS Digital does We turn interest into real enquiries and booked calls using account-based marketing and AI automation. Who we help B2B operators who want growth without guesswork. The outcome A clearer pipeline, better lead quality, and more predictable revenue. Why this works This approach works because it focuses on fundamentals, clean execution, and systems that keep performing over time. If this resonates, feel free to DM me.

Scaling a six-figure Paid Search budget by 20% month-over-month while maintaining efficiency isn’t easy. Here’s how we tackled this on a project for VEED.IO: 📊 𝗪𝗲 𝘀𝘄𝗶𝘁𝗰𝗵𝗲𝗱 𝘁𝗼 𝗥𝗢𝗔𝗦, 𝗮 𝗯𝗲𝘁𝘁𝗲𝗿 𝗳𝗶𝘁 𝗳𝗼𝗿 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 For subscription products like VEED, not all users are equally valuable—churn rates, retention, and ARPU vary widely. CAC alone doesn’t account for these differences, so we moved to ROAS reporting. This allowed us to measure campaign performance with a focus on long-term value, rather than just upfront conversions. 🤝 𝗪𝗲 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝘀𝗽𝗲𝗻𝗱, 𝘂𝘀𝗲𝗿 𝗮𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗱𝗮𝘁𝗮 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗨𝗧𝗠𝘀 To make ROAS reporting possible, we connected Google Ads spend data with VEED’s first-party payment (Stripe) and attribution models (Google Analytics). This integration allowed us to connect recurring revenue to specific campaigns, ad groups, and even keywords, providing a clearer picture of performance. 🐭 𝗪𝗲 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗥𝗢𝗔𝗦 𝗮𝘁 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗹𝗲𝘃𝗲𝗹𝘀 𝗮𝗻𝗱 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗲𝗱 𝗸𝗲𝘆𝘄𝗼𝗿𝗱-𝗹𝗲𝘃𝗲𝗹 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Our granular reporting revealed (expectedly) that ROAS varied significantly across campaigns, markets, and use cases. At the keyword level, we saw the most variation—some features and search terms drove much higher value than others. 🔎 𝗪𝗲 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗲𝗱 𝘄𝗵𝗶𝗰𝗵 𝗸𝗲𝘆𝘄𝗼𝗿𝗱𝘀 𝗮𝗻𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗱𝗲𝘀𝗲𝗿𝘃𝗲𝗱 𝗺𝗼𝗿𝗲 𝗯𝘂𝗱𝗴𝗲𝘁 Using ROAS data, we pinpointed keywords and use cases with the fastest payback period and highest ROAS, ensuring they received a larger share of the budget. Similarly, we identified lower-performing strategies that were better candidates for reduced spend, enabling more efficient allocation overall. 📦 𝗪𝗲 𝗿𝗲𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗲𝗱 𝘁𝗵𝗲 𝗰𝗮𝗺𝗽𝗮𝗶𝗴𝗻 𝘁𝗮𝘅𝗼𝗻𝗼𝗺𝘆 𝘁𝗼 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 VEED’s original campaign structure was organized by market at the Campaign level and by features at the Ad group level. We flipped this model, creating campaigns based on use cases (e.g., “Add Subtitles”) and countries within them. This shift improved controlled budget allocation to the most valuable features. 💯 𝗢𝘂𝗿 𝘁𝗲𝘀𝘁 𝗰𝗮𝗺𝗽𝗮𝗶𝗴𝗻 𝘁𝗮𝘅𝗼𝗻𝗼𝗺𝘆 𝗱𝗿𝗼𝘃𝗲 𝗯𝗶𝗴 𝘄𝗶𝗻𝘀 Our ROAS insights led to a revamped campaign structure, which directly impacted VEED results: • 20% increase in subscriptions • 12% higher ARPU • Flat CAC despite increased spend This project is an A+ example of of how deep marketing analysis can (and should) drive actionable changes to campaigns. I’ll be sharing the full article article in the comments for those interested in reading about the set-up more in-depth. 👇

𝙄𝙙𝙚𝙖 #𝟮𝟬: 𝙈𝙚𝙖𝙨𝙪𝙧𝙚 𝙬𝙝𝙖𝙩 𝙢𝙖𝙩𝙩𝙚𝙧𝙨: 𝙩𝙝𝙚 𝙩𝙧𝙤𝙪𝙗𝙡𝙚 𝙬𝙞𝙩𝙝 𝙍𝙊𝘼𝙎 A decade ago, I worked with a US retail business that was trying to optimise its paid search spend of around $30m. The team was focused on return-on-ad spend = revenue/ad spend (ROAS) as its primary performance metric.  The CEO wanted to understand whether the spend was efficient and how it could be scaled.  The core issue was the team was 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 at the same level as it was 𝗺𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 – a self-fulfilling loop of sub-optimisation.  Underlying this were two interconnected challenges:  • 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻. Performance was being managed based on a single blended ROAS.  The high-performing keywords were offsetting low-performing keywords.  • 𝗦𝘂𝗿𝗿𝗼𝗴𝗮𝘁𝗶𝗼𝗻. Optimizing ROAS is never the business objective. When ROAS becomes the only objective, teams stop chasing 𝘱𝘳𝘰𝘧𝘪𝘵 and start chasing 𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘺. Result: They cut high-intent growth traffic (harder to convert) and doubled down on brand searches (traffic they would have likely captured anyway).    The core diagnosis was a simple decile analysis: keywords were sorted from best to worst performing (based on ROAS) and then grouped into 10 bins of each spend (see example below). This simple descriptive insight was a critical enabler: it doesn’t tell you what to do but it allows you to frame hypotheses and know where to focus.  The key insights: • The top decile (Google “navigation tax”): These are high-volume brand terms. The key question here is about incrementality. Would we have captured this traffic anyway? • The efficient torso: These keywords are performing well. The strategy here is scaling – can we spend more? • The long inefficient tail: What is driving the poor performance here? Low intent keywords, poor landing page experience, price or availability?  𝗧𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝘀𝘆𝘀𝘁𝗲𝗺, 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗱𝗲𝘁𝗮𝗶𝗹. As we dug into keyword-level performance, we found a range of issues and opportunities: • Geo: Performance dropped the further the visitor was from a physical store.  • Product: ROAS plummeted when there was low stock or uncompetitive prices. • Device: High-intent searches on desktop vs. browsing behavior on mobile. • Seasonality: keywords that only became efficient at specific times of year. Key takeaways 1. Deaverage. Analyse at the most granular level where it’s possible to take action.   Deciles are a simple but powerful analytical tool to understand the distribution of performance. 2. Track a portfolio of metrics: e.g., ROAS, mROAS (marginal ROAS), and iROAS (incremental ROAS) – and use as guardrails, not targets.   3. Implement countermeasures: If you measure efficiency (ROAS), you must pair it with a volume constraint (e.g., profit $). Marketing dashboards are great but 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 ≠ 𝗶𝗻𝘀𝗶𝗴𝗵𝘁. 

B2B SaaS paid search has changed significantly in the past 15 years. Here's how I approach paid search in 2025: 1. Restructure the Account for Ease of Use and Clarity Group keywords by clearly named topics (campaigns) & subtopics (ad groups) and segment campaigns by behavior (brand, brand alternatives, competitors, non-brand top of funnel, and non-brand solutions). Advertisers need to know immediately the strategy behind the account structure and what is working and why. 2. Move All Keyword Match Types to Exact Google is an efficient thief, and it takes about four months of vigilant daily search term review and adding negatives. Add relevant exact match keywords as well. 3. Address Landing Page Performance My current preferred landing page format is a lengthy multiple call to action (long-form multi-CTA) landing page that offers a free trial as the main call to action and a relevant white paper as a secondary call to action. Landing page should clearly identify benefits of both the free trial and asset (separately) as well as build credibility and trust through customer logos and testimonials/before and afters. 4. Market to Your ICP Lead quality is important but lead scoring can be misleading. Review paid search leads against named accounts and total relevant market. Pause campaigns driving only vanity metrics. Review top performing assets to determine if they are actually helping to sell your product. Put your marketing hat on and determine if you are building demand for your audience. 5. Test Automation Only after clean search term reports, landing page and asset testing, and consistent quality lead and pipeline generation, start testing automation. All automation should include bid caps and portfolio bidding should only be used on similar campaign types with close pipeline to spend ratios. 6. Review In-Market Retargeting Efforts Search isn't going to win any deals on its own. Review performance of paid social and programmatic retargeting efforts. Make sure you are engaging accounts that have shown intent on search with brand building ads. Stay top of mind on "cheap" active networks like Meta, X, and Reddit.

I had a monthly ad budget of well over $1M and was allocating between 66% - 75% of it to Google Ads We had an incredible average cost per lead on the account and our agency said we were still losing impression share due to budget, so we could add even MORE money to the channel without seeing diminishing returns…so we did 5+ years now removed from that role, if there was one thing I wish I could go back and tell my younger self then, it would be this: ➡️ You're wasting spend by determining success as a low cost per lead and not tracking past those leads to see if they turn into pipeline or revenue Most B2B companies spend money on paid search in 4 buckets: 👉 Brand keywords (ex. Drift) 👉 High-intent non-brand keywords (ex. chatbot software) 👉 Low-intent non-brand keywords (ex. chatbot) 👉 Competitor keywords (ex. Intercom) Since paid search is an easily attributable channel with it often leading to a direct response, you can use tracking scripts + UTMs to capture data that led up to the conversion You then run a report filtering for all leads that came through paid search and see which lifecycle stages they progressed to As I mentioned earlier, the biggest mistake that younger me made was stopping at the data inside Google Ads and only focusing on the cost per lead But true success means looking at how they do or don't progress into meaningful business opportunities for you, aka pipeline and revenue creation And now as someone managing a monthly budget significantly less than the one I had in my early days - what I would give to go back + help younger me out to make some real business impact - - - - - - - - - - - If this is something you can relate to, I wrote about it this past weekend. I'll also drop the template to run your own analysis below. Happy nerding out 🤓

Diminishing returns in paid marketing channels. Why it is crucial and what can you do about it? Diminishing returns effects are a vital behaviour for every marketer to account for when making investment decisions. But it is often spoken about as an unavoidable truth, when you hit diminishing returns on a channel you need to divest into a different channel. The theory is sound, everyone can see that when you increase budget too far in one channel performance declines. This remains true almost irrespective of which measurement method you use (in platform, modelled, uplift tests etc.) But the conclusion that "I need to reduce budget and spend elsewhere" isn't the only option to counter diminishing returns effects. Paid Search shows some of the steepest diminishing returns curves of any paid marketing channel, but 𝘆𝗼𝘂 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝘃𝗲 𝗮 𝗹𝗼𝘁 𝗺𝗼𝗿𝗲 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝗴𝗿𝗮𝗱𝗶𝗲𝗻𝘁 𝗼𝗳 𝘁𝗵𝗮𝘁 𝗰𝘂𝗿𝘃𝗲 𝘁𝗵𝗮𝗻 𝗺𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗲𝘅𝗽𝗲𝗰𝘁. The default position in budget optimisation is "I need to avoid diminishing returns" when actually the approach should be "𝗛𝗼𝘄 𝗰𝗮𝗻 𝗜 𝗰𝗵𝗮𝗻𝗴𝗲 𝘁𝗵𝗲 𝗱𝗶𝗺𝗶𝗻𝗶𝘀𝗵𝗶𝗻𝗴 𝗿𝗲𝘁𝘂𝗿𝗻𝘀 𝗰𝘂𝗿𝘃𝗲" We model channel performance including factors called "channel synergies". How what we do on one channel impacts our ability to activate on another. One of my favourite examples of this is 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝗦𝗲𝗮𝗿𝗰𝗵 𝗰𝗮𝗺𝗽𝗮𝗶𝗴𝗻𝘀. It is a relationship every experienced marketer knows, but is usually ignored when it comes to measuring impacts of channels. When brands run YouTube campaigns, the diminishing returns curve on search activity flattens. We gain the ability to spend more on search more effectively. 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝘁𝗹𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗮𝗻𝗴𝗲 𝗼𝗳 𝟮𝟬%-𝟯𝟬% 𝗯𝗲𝘁𝘁𝗲𝗿 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝘁𝗵𝗮𝗻 𝘄𝗲 𝘄𝗼𝘂𝗹𝗱 𝗲𝘅𝗽𝗲𝗰𝘁 𝗮𝘁 𝗵𝗶𝗴𝗵𝗲𝗿 𝘀𝗽𝗲𝗻𝗱 𝗹𝗲𝘃𝗲𝗹𝘀. That shouldn't surprise anyone, more people know (and maybe even care) about the brand, so the performance in bottom of funnel channels is better. Marketing theory has understood these impacts for years, but it very rarely makes it to the day to day decisions being made in budget and campaign optimisation. Suddenly the answer isn't "search has hit diminishing returns so we need to spend elsewhere" but "𝘄𝗵𝗲𝗻 𝘄𝗲 𝘀𝗽𝗲𝗻𝗱 𝗲𝗹𝘀𝗲𝘄𝗵𝗲𝗿𝗲 𝘄𝗲 𝗮𝗹𝗹𝗼𝘄 𝗼𝘂𝗿𝘀𝗲𝗹𝘃𝗲𝘀 𝘁𝗼 𝗶𝗻𝘃𝗲𝘀𝘁 𝗯𝗲𝘁𝘁𝗲𝗿 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘀𝗲𝗮𝗿𝗰𝗵". The horridly steep diminishing returns curves which used to hold us back start to turn in our favour and we can increase spend without having the trade-off in efficiency. And we start to have optimisation data which agrees with the marketing theory.

You’re using the wrong AI for Paid Search. And it’s quietly burning budget. Most teams plug Generative AI into everything keywords, ads, bids, reports then wonder why performance stalls. The problem isn’t AI. It’s misalignment between the job and the intelligence. Here’s the Paid Search–ready framework for 2026 👇 The AI Stack (and where each one actually wins in Paid Search) 1️⃣ Machine Learning → Predict outcomes Use it for: • Conversion probability • LTV forecasting • Demand curves & seasonality • Budget pacing Paid Search example: Predict which queries are likely to convert before you raise bids. No ML foundation = reactive bidding. 2️⃣ Neural Networks → Recognize patterns Use it for: • Query intent clustering • Creative fatigue detection • Anomaly detection in CPC/CPA Paid Search example: Spot early signals of performance decay across thousands of queries— before ROAS drops. 3️⃣ Generative AI → Create & synthesize Use it for: • Ad copy variants • Keyword expansion ideas • Search term summaries • Landing page drafts Paid Search example: Generate 50 ad variants fast but never let GenAI decide bids or budgets alone. 4️⃣ AI Agents → Execute tasks Use it for: • Search term mining → negatives • Ad testing workflows • Reporting + insights distribution Paid Search example: An agent that pulls weekly queries, flags waste, updates negatives, and logs changes automatically. 5️⃣ Agentic AI → Run operations Use it for: • Cross-campaign budget reallocation • Bid strategy coordination across brands/regions • Always-on optimization with guardrails Paid Search example: Agents that shift spend across campaigns based on marginal ROAS with human-defined constraints. The mistake most teams make They start at the top. GenAI everywhere. No prediction layer. No pattern detection. No control. That leads to: ❌ Blind automation ❌ Overbidding on noisy intent ❌ Budget leaks that scale fast The rule for 2026: - Prediction before automation. - Recognition before creation. - Control before autonomy. How winning Paid Search teams are building AI stacks They stack capabilities, bottom-up: ML → NN → GenAI → Agents → Agentic Ops One layer at a time. One problem at a time. Start here this quarter: 1. Pick one Paid Search bottleneck (waste, scaling, reporting, creative fatigue) 2. Map it to the correct AI layer 3. Automate only after you can predict + measure impact AI doesn’t replace Paid Search strategy. It exposes whether you actually have one. 💬 What’s the one Paid Search problem you want AI to solve this quarter? 📌 Save this for your next AI or media planning review 🔁 Repost to help your team stop scaling the wrong intelligence ➕ Follow Sandeep Gulati🎯 for AI × Paid Search frameworks built for ROAS, not hype IC: Aditya Sharma