Synthetic Businesses: Can Gen AI Mirror Worlds Accelerate Sales?

Published June 3, 2024

1

Demo Brand

I was watching the Google Cloud Next ’24 keynote video, and noticed that many of the demos had a common company name: “Cymbal.” I first thought it was a real company they were talking about, but it soon became apparent that Cymbal was a demo brand that Google had created to showcase all their capabilities. The idea of a “demo brand” became even more real when I had gone for a meeting with a prospect in the financial services industry. As I was explaining what the Netcore platform could do, I realised that some of the demos I was showing were not resonating because they were from eCommerce. As I reflected after the meeting, I realised that what we needed to do was to create replicas of businesses mirroring the enterprises we were meeting. This would help us make the conversation much more targeted, demonstrate our knowledge of the industry, and speed up closure. In other words, we needed a “Cymbal” of our own.

I found this explainer from Google: “Cymbal is a fictitious, multi-industry holding company that provides a rich foundation for telling stories about Cloud. It has subsidiaries operating in key focus areas for Cloud (Retail, FSI, HCLS) and is facing many of the critical business challenges that our customers face and which Google Cloud is best positioned to solve. Seems too good to be true? You’re right. Cymbal’s structure, story, fact base, and brand identity were carefully designed as a storytelling engine for Google Cloud.”

Google has created multiple such ‘companies’:

Google explains further: “We created the Cymbal brand to make storytelling more consistent across Google Cloud. But as we embarked on this journey, we realized these datasets could be useful to our customers and community more broadly. The contents of these datasets are synthetic, and provided to industry practitioners for the purpose of product discovery, testing, and evaluation.”

The phrase which came to me as I was reading this was “Synthetic Business” – a business created with not just synthetic datasets but much more: synthetic “live” customers undertaking actions (journeys, searches, transactions, actions on push messages, and more) to create a “mirror world” as a virtual playground and lab.

In the past few months, the word “synthetic” has come to the forefront in two contexts: synthetic biology and synthetic data.

2

Synthetic Biology

I wrote a short essay a few months ago: Synthetic Biology: The Technology of Life. As a NIH site explains: “Synthetic biology is a field of science that involves redesigning organisms for useful purposes by engineering them to have new abilities. Synthetic biology researchers and companies around the world are harnessing the power of nature to solve problems in medicine, manufacturing and agriculture.”  BIO adds: “It aims at the (re-)design and fabrication of biological components and systems that do not already exist in the natural world. Synthetic biology combines chemical synthesis of DNA with growing knowledge of genomics to enable researchers to quickly manufacture catalogued DNA sequences and assemble them into new genomes.”

More from Britannica: “[It is a] field of research in which the main objective is to create fully operational biological systems from the smallest constituent parts possible, including DNA, proteins, and other organic molecules. Synthetic biology incorporates many different scientific techniques and approaches. The synthetic systems created may be used to generate products ranging from ethanol and drugs to complete synthetic organisms such as complex bacteria that can digest and neutralize toxic chemicals. Ideally, these customized synthetic biological systems and organisms would be much safer and less complicated than approaches based on the manipulation of naturally occurring biological entities. Synthetic systems and organisms would essentially operate like biological “factories” or “computers.””

Michael Elowitz said in an interview: “I think there’s something about the physics style of reasoning, thinking, and problem solving. Physicists value reducing a problem to its essence and imagining the simplest example of a question and the simplest way to go after it. They also value quantitative approaches. If you think about the initial synthetic gene circuits, that’s exactly what they were. They’re literally the simplest, non-trivial circuits anyone could imagine.  But in biology, we continuously discover that biological systems are far more complicated than we realize….[B]iology is a kind of never-ending process of uncovering and unpacking all this complexity. The synthetic approach—building to understand—is really just saying that scientists should start with the simplest possible circuits and see what they can do and build up from there. I think that’s a style that’s appealing to physicists, and certainly was appealing to me. But the two approaches—synthetic biology and ‘real’ biology—are complementary. We don’t want a world with only one.”

3

Synthetic Data

Synthetic data has come to the fore in the context of data needed to train AI systems (including LLMs). Wikipedia explains: “Synthetic data is information that is artificially generated rather than produced by real-world events. Typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models. Data generated by a computer simulation can be seen as synthetic data. This encompasses most applications of physical modeling, such as music synthesizers or flight simulators. The output of such systems approximates the real thing, but is fully algorithmically generated. Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data.”

IBM Research writes: “Data is the raw material fueling much of today’s progress in artificial intelligence, producing fresh insights, new discoveries, and decisions backed by more evidence. Data is now so essential to the modern economy that demand for real, high-quality data has grown exponentially. At the same time, stricter data privacy rules and ever larger AI models have made gathering and labeling real data increasingly difficult or impractical. Synthetic data is computer-generated information for testing and training AI models that has become indispensible in our data-driven era. It’s cheap to produce, comes automatically labeled, and sidesteps many of the logistical, ethical, and privacy issues that come with training deep learning models on real-world examples.”

From a recent paper entitled “Best Practices and Lessons Learned on Synthetic Data for Language Models”: “Synthetic data has emerged as a promising solution to address these challenges. Synthetic data refers to artificially generated data that mimics the characteristics and patterns of real-world data, but is created through algorithms, generative models, or even simulations, rather than being directly created by humans. By leveraging synthetic data, we can not only overcome the limitations of real-world data but also unlock the potential to develop more robust, reliable, and fair AI models. One of the many benefits of synthetic data is that it can be generated at scale, providing an abundant supply of training and testing data for AI models. This is particularly valuable in domains where real-world data is scarce or difficult to obtain (e.g., weather data covering all conditions). Second, synthetic data can be tailored to specific requirements, such as ensuring a balanced representation of different classes by introducing controlled variations (e.g., up-weighting low-resource languages in multilingual language learning). This level of control over data characteristics can improve model performance and generalization. Third, synthetic data can help mitigate privacy concerns by creating anonymized or de-identified datasets that do not contain sensitive personal information. This is crucial in domains such as healthcare, where patient privacy is of utmost importance.”

From a recent Wall Street Journal story which identified “data shortage” as a frontier research problem in AI: “Pablo Villalobos, who studies artificial intelligence for research institute Epoch, estimated that GPT-4 was trained on as many as 12 trillion tokens. Based on a computer-science principle called the Chinchilla scaling laws, an AI system like GPT-5 would need 60 trillion to 100 trillion tokens of data if researchers continued to follow the current growth trajectory, Villalobos and other researchers have estimated. Harnessing all the high-quality language and image data available could still leave a shortfall of 10 trillion to 20 trillion tokens or more, Villalobos said. And it isn’t clear how to bridge that gap. Two years ago, Villalobos and his colleagues wrote that there was a 50% chance that the demand for high-quality data would outstrip supply by mid-2024 and a 90% chance that it would happen by 2026. They have since become a bit more optimistic, and plan to update their estimate to 2028.”

4

Usage

I asked ChatGPT on which industries are using synthetic data.

This concept is particularly valuable in sectors where simulation and modeling can significantly enhance understanding, training, development, and testing processes. Here are some examples:

1. Automotive Industry:

1. • Autonomous Vehicle Testing: Companies like Waymo and Tesla use synthetic data to simulate various driving conditions, helping to train their autonomous driving algorithms without the need for millions of miles of actual road testing. This includes generating data for unlikely but possible scenarios that might not be frequently encountered in real driving.

2. Healthcare:

1. • Medical Imaging: Synthetic data is used to create realistic, anonymized medical images for training AI systems. This is crucial in areas where patient privacy is a concern, and real patient data may not be extensively available.
   • Drug Discovery and Development: Synthetic biology applications allow researchers to design organisms and biological systems, facilitating faster and potentially more cost-effective drug development processes.

3. Finance:

1. • Fraud Detection Training: Financial institutions use synthetic transaction data to train machine learning models to detect fraudulent activity. Real transaction data can be sensitive and limited, so synthetic data provides a plentiful and secure alternative.
   • Risk Management: Synthetic data scenarios help in modeling various financial risk scenarios (like credit risk and market changes) enabling better preparedness and response strategies.

4. Gaming and Virtual Reality:

1. • Game Development: Synthetic data assists in creating realistic environments and AI behaviors. By generating varied data on virtual interactions, developers can train game AI to respond more realistically to player actions.
   • VR Simulations: Virtual reality platforms use synthetic environments for training and entertainment, simulating everything from complex surgical procedures for medical training to virtual travel experiences for consumer entertainment.

5. Retail and E-commerce:

1. • Customer Behavior Modeling: Synthetic data is used to simulate customer shopping behaviors and preferences, helping retailers to optimize their stock, layout, and marketing strategies without compromising customer privacy.
   • Supply Chain Management: Synthetic data models can predict various supply chain disruptions and their potential impacts, allowing companies to test different response strategies.

6. Education and Training:

1. • Professional Training Simulations: Synthetic businesses are created within educational environments to help students learn business management, marketing, and decision-making skills in a risk-free setting.

7. Cybersecurity:

1. • Threat Simulation and Response Training: Synthetic data generates various network conditions and cyberattack scenarios, enabling better training of security professionals and development of more robust cybersecurity systems.

I then asked ChatGPT which industries are most likely to use synthetic data in the near future.

1. Telecommunications:

1. • Network Optimization and Planning: Synthetic data can simulate network conditions under various loads and stress scenarios to optimize network operations without affecting actual service.
   • Customer Experience Modeling: Telecommunications companies can use synthetic data to model different customer interaction scenarios with new services or to improve existing customer service processes.

2. Insurance:

1. • Risk Assessment and Pricing: Synthetic data can help model various risk scenarios more comprehensively, helping insurers to set prices more accurately and develop new insurance products.
   • Claim Processing Automation: Synthetic datasets can train AI models to automate and optimize claim processing, reducing time and errors associated with manual handling.

3. Manufacturing:

1. • Supply Chain and Process Simulations: Manufacturers can use synthetic data to model production processes and supply chain logistics to identify potential issues and improve efficiency.
   • Product Development: Synthetic data can simulate stress tests and other product testing scenarios, which are crucial in product design and quality assurance.

4. Real Estate and Urban Planning:

1. • Market Analysis: Synthetic data can simulate various market conditions to help real estate companies make better investment decisions.
   • City Planning and Infrastructure Development: Urban planners can use synthetic data to model traffic patterns, utility usage, and other urban dynamics to improve city planning and infrastructure projects.

5. Energy and Utilities:

1. • Grid Management: Synthetic data can help simulate different scenarios for electricity demand and supply, aiding in grid management and disaster response planning.
   • Renewable Energy Integration: By simulating various environmental and usage scenarios, synthetic data helps in planning the integration of renewable energy sources into the existing grid.

6. Transportation and Logistics:

1. • Route Optimization: Companies can use synthetic data to model different traffic conditions and optimize delivery routes and times.
   • Fleet Management: Synthetic data can simulate fleet operations under various conditions to improve logistics and reduce costs.

7. Entertainment and Media:

1. • Audience Analysis: Media companies can use synthetic data to model viewer preferences and behaviors, helping in content creation and targeted advertising.
   • Interactive Media Development: For developers of interactive media, such as video games and virtual reality, synthetic data can be used to test user interactions and enhance user experience designs.

8. Legal and Regulatory Compliance:

1. • Compliance Training and Testing: Synthetic data can simulate various legal scenarios for training professionals in compliance-related fields.
   • Case Management Systems: In the legal industry, synthetic data can be used to test and optimize case management systems without compromising the confidentiality of actual case data.

These industries can benefit significantly from synthetic data by improving their predictive analytics, reducing reliance on sensitive real-world data, and enhancing their testing and development processes. The trend toward digital transformation and AI-driven decision-making will likely drive increased adoption of synthetic data across these fields.

5

Digital Marketing – 1

Javier Martin discusses the use of synthetic data in digital marketing: “Marketers and brand strategists are simply spoiled for choice as digital marketing continues to innovate, with new trends emerging every few months. Reaching the right audience at the right time with relevant content and at the right cost remains a huge challenge for brands. To answer the question, “Where should I invest my next dollar?”, data is the brand’s best friend. But not all brands or campaigns have lots of data. Digital marketing is an empirical discipline. Eventually, they will have small or medium-sized datasets to study for a specific problem. It is difficult to forecast campaign success when we only have data for one year, because this approach did not exist two years ago. In this context, synthetic data can be a new tool for extracting value from existing data and addressing new challenges.”

Asad ur Rehman lists use cases of synthetic data in marketing:

1. Customer Segmentation: Using synthetic data to test and refine segmentation strategies, ensuring more targeted marketing without compromising real customer data.
2. Ad Performance Simulation: Predicting the effectiveness of advertising campaigns on synthetic populations before real-world deployment, saving costs, and optimising strategies.
3. Personalisation Enhancement: Training algorithms on synthetic data to personalise marketing content, improving customer experiences while safeguarding privacy.
4. Market Analysis: Generating synthetic data reflecting new market segments or demographics, providing insights when real data is scarce or unavailable.
5. Product Concept Testing: Simulating market responses to new products using synthetic data, aiding in strategy formulation and risk mitigation.
6. Sales Forecasting: Utilizing synthetic data for more accurate sales predictions, especially in scenarios with limited historical data.
7. Content Optimization: Testing content variations on synthetic audiences to determine what resonates before publishing to actual consumers.

Romain Warlop writes: “Marketers can use synthetic data to run future what-if scenarios to forecast events on marketing before they actually happen. For instance, using internal data together with open data, external studies, and business knowledge, marketers can create a synthetic data set of customer behavior on the market that replicates historical KPIs and mimics plausible scenarios in order to test the result of a marketing strategy in different configurations. By testing algorithms on synthetic data sets, marketers have more control over their marketing strategies and can see if their plans are working at scale. For example, if marketers develop an algorithm to detect counterfeit products from images, they can test the accuracy of their model before using it with their actual data…Before expanding to new audience segments, marketers can also use synthetic data that mimics the behavior of this new segment and apply their own forecasting algorithms to help them determine the relevancy of the segment. Synthetic data, despite being artificially created, mimics the real world, making it an invaluable resource for marketers grappling with data scarcity in light of the depreciation of third-party cookies.”

Tom Roach discusses various startups using AI for marketing. “One startup I spoke to…uses large panels of synthetic consumers to generate market research, brand health reports, segmentation studies, pricing and category entry-point analysis – technical, arduous work that would normally take weeks, but that can now be done overnight. They say it’s already at 90% accuracy versus the equivalent analysis using real humans analysing real human data. Essentially it’s able to get a fix on any brand’s ‘share of model’ from a number of dimensions.

6

Digital Marketing – 2

From Redpoint: “Consider a fictitious travel company that wants to market a certain vacation package – say a budget weekend getaway to Atlantic City. To market to the right audience, it needs to know which customers would be most likely to purchase the package. If it’s a newly created offer, the company can’t go to the well with existing customers. Again, it might test the offer on a small percentage of existing customers – taking the chance of creating friction for those customers who find the offer irrelevant. Instead, it purchases an anonymized dataset that includes thousands of customers who have visited the same or a similar destination – a dataset of synthetic customers created from real customers (anonymized), or algorithms that can generate variants of real customers. It trains its model on this anonymized dataset to determine which customers will buy the vacation package. Providing real-life synthetic data, particularly in industries with a need for security and privacy, effectively accelerates the training of models to apply them more quickly to real-life scenarios.”

Nathan Eddy writes: ““Synthetic data could have far-reaching implications across the organization,” said Mike Froggatt, senior director analyst in the Gartner marketing practice, who explained there are three main opportunities around synthetic data for brands. The first is the expansion of privacy-safe customer data models for targeting, the second is data-driven creative capabilities, which include automation for video, audio and display ads, and third is the increased availability of AI-driven models for activities like customer service, product development and training. He said organizations looking to leverage synthetic data require a central data platform, whether a simple database or specialized technology like a customer data platform (CDP), to house both sample data and the synthetic data created via generative AI tools…Among the other tools he recommends are generative AI tools to create the synthetic data and future scenario and modeling software, which can include anything from virtual worlds to digital twins of customers/products.”

From The Drum: “The most valuable application of synthetic data is probably in the training of machine learning (ML) models. These models require vast quantities of data in order to be able to make accurate predictions about the real world. The impressive verbal fluency of ChatGPT, for example, stems from the fact that its underlying large language model (LLM), GPT-4, has been trained with an enormous amount of text-based content from the internet. But it takes time and money for human beings to gather and label (that is, organize according to a particular set of rules) data that’s been collected from the real world. Synthetic data, on the other hand, is both inexpensive to produce and automatically pre-labeled, which means it can easily be fed into an ML model without jeopardizing that model’s functionality. To use an automotive analogy, it’s as if a car manufacturer suddenly developed a means of producing huge amounts of cheap but perfectly usable refined oil using only a computer. Another benefit of synthetic data is that it can be used to create a more robust dataset, accounting for fringe or anomalous events which might not be reflected in real-world data.”

Y2S Consulting writes (among other things) about Agent-Based Modeling: “This technique uses computational models to simulate the actions and interactions of autonomous agents (e.g., individuals, and organizations) to assess their effects on the system as a whole. It’s particularly useful for generating synthetic data in complex systems where individual behaviors and interactions drive the overall dynamics, such as in economics, social sciences, and traffic systems.” More: “Some brands have started to use synthetic data to model customer behavior, especially in sectors like e-commerce and retail, where understanding customer preferences and predicting future behaviors can significantly impact sales and customer satisfaction.”

7

A Better Demo

Now that we have a good idea about synthetic data and its applications across marketing and other disciplines, let’s focus on a narrow problem: how can it be harnessed for doing better demos to speed up the sales process? This is where we need to move from synthetic data to creating a “synthetic business.”

Syntho.ai captures the problem statement well:

Cymbal, Google’s demo brand, was created by Clay Stein. “The Problem: At Google, Demos are valuable sales and marketing tools. The issue is they are time consuming to create. The current landscape of demos is scattered, producing duplicative assets with divided formats and conventions- using resources inefficiently and leaving Google vulnerable to legal and compliance issues. The Opportunity: Create a toolkit for content creation that instantly ramps up demo builds without the need to worry about legal, compliance, domain registration or design standards allowing teams to build beautiful experiences faster in a cohesive manner.”

Let’s imagine how I could have done my meeting with the financial services company differently if I had a “Cymbal”-equivalent in my arsenal.

• Begin with a preamble about the fictitious business and summarising the key numbers.
• Move to the backend view – showcasing the Netcore Martech platform and the various views and actions possible.
• Show info for a single customer and the journey of that customer.
• Invite the marketer to take up the persona of an actual customer, do actions on a mock website/app (or via an email or WhatsApp message) and show in real-time what happens at the backend, along with the likely next actions that will be triggered to help close a transaction.
• In fact, a view showcasing how a new customer will be moved through to transaction, compressed within minutes, would make for a very powerful demo. The starting point could be a new lead who has not yet transacted. Based on that, an interaction flow leading to a closure could be simulated.
• At the same time, dashboards could show what other ‘customers’ ae doing in real-time on the demo platform.
• As part of the demo, a “Co-Marketer” could then take inputs for campaigns and show outcomes in seconds.

The idea is to create a “mirror world” that brings to life the operations that a marketing team would do. It is like being in an aircraft simulator and flying or landing a plane. It is an experience one never forgets. Gen AI’s ability to “generate” synthetic data to embody multiple personas “digital twins” with a large (synthetic) customer model can show in minutes what hours of talk and multiple feature comparison sheets would never be able to do.

This creation of a synthetic business thus becomes the key to accelerate the sales process. All the individual technology elements are at hand to build a demo experience that creates awe and accelerates the path towards winning the brand’s business.

Previously, I have written about the third-generation of B2C Martech platforms comprising Co-Marketer, Digital Twin, Mirror World, and Large Customer Model. All of these come into place once the deal is done. I think a fifth component needs to be added: a “Synthetic Business” which helps in getting the deal done. This is where martech vendors can harness the power of Gen AI for themselves to accelerate their sales process.

8

The Details

I prompted ChatGPT: “I run a martech company. I want to use the ideas discussed to help build Synthetic Businesses to improve my demos. How can I do that? Provide a step-by-step plan.” Here is the response.

Step 1: Define the Objective

Start by clearly defining what you aim to achieve with your synthetic business demos. Objectives may include demonstrating specific product features, simulating customer journeys, or showcasing the impact of your technology on different industries.

Step 2: Develop Synthetic Business Profiles

• 1 Identify Industries: Choose industries relevant to your target clients where your solutions could have a significant impact.
• 2 Create Demo Brands: Develop detailed profiles for each synthetic business, including industry, size, market challenges, and goals. These should mirror the characteristics and challenges of your potential clients’ industries.
• 3 Develop Scenarios: Craft scenarios that these synthetic businesses might encounter, which your martech solutions can address (e.g., customer segmentation, campaign management).

Step 3: Generate Synthetic Data

• 1 Data Types: Identify the types of data (transactional, behavioral, demographic) needed to create realistic customer interactions.
• 2 Data Generation: Use generative AI and data simulation tools to create synthetic data sets that reflect realistic customer behaviors and interactions.
• 3 Ensure Compliance: Ensure that your synthetic data adheres to relevant data protection regulations to avoid legal and ethical issues.

Step 4: Integrate with Your Martech Solutions

• 1 Customization: Adapt your platform to handle synthetic data, ensuring it can demonstrate the functionalities as if interacting with real data.
• 2 Demonstration Scripts: Develop scripts for demos that highlight how your platform can analyze and utilize this data in real-time.

Step 5: Build Interactive Demo Interfaces

• 1 User-Friendly Interfaces: Design interactive interfaces that allow potential clients to engage with the synthetic business, such as adjusting campaign parameters and viewing marketing analytics.
• 2 Real-Time Interaction: Enable real-time data manipulation and visualization to showcase the dynamic capabilities of your solutions.

Step 6: Training and Sales Team Preparation

• 1 Educate Your Team: Train your sales and demo teams on the intricacies of each synthetic business and the corresponding data sets.
• 2 Role-Playing Drills: Conduct role-playing sessions to prepare the team to handle various client questions and scenarios effectively during the demos.

Step 7: Pilot Testing and Feedback

• 1 Internal Testing: Before going live, run internal tests of the demos to ensure everything operates smoothly.
• 2 Collect Feedback: Gather feedback from a small group of trusted clients or external consultants and make necessary adjustments.

Step 8: Launch and Iterate

• 1 Official Launch: Introduce the synthetic business demos in your sales pitches and at industry events.
• 2 Continuous Improvement: Regularly update the synthetic scenarios and data based on new industry trends and feedback to keep the demos relevant and effective.

Step 9: Monitor and Report

• 1 Performance Tracking: Monitor how these demos impact the sales cycle and client engagement.
• 2 Reporting: Regularly report on the effectiveness of demos and integrate insights into broader business strategies.

Step 10: Scale and Diversify

• 1 Expand Offerings: As you perfect your approach, consider creating additional synthetic businesses for other sectors or regions.
• 2 Diversification: Explore further applications of synthetic data in other aspects of your business, such as product development or customer service.

9

AIs: Summary and Enhancements

ChatGPT

Summary

The idea of Synthetic Businesses involves creating detailed, fictitious company profiles (referred to as “demo brands” like Google’s “Cymbal”) to facilitate more effective sales demonstrations and engagements. These synthetic businesses utilize comprehensive datasets and create scenarios featuring “live” customers and their interactions. This concept has been highlighted as beneficial in areas like marketing, where it allows for tailored, risk-free simulations and precise testing of marketing strategies without compromising real customer data. The approach combines elements from synthetic biology and synthetic data to innovate within digital marketing and beyond, emphasizing the generation of synthetic customer data to improve decision-making processes in marketing, training of AI systems, and more.

Enhancements

1. Enhanced Transparency: Clear guidelines and disclosures about the use of synthetic data can help in maintaining ethical standards and trust with stakeholders.
2. Cross-industry Validation: To improve the accuracy and reliability of synthetic data, it would be beneficial to incorporate cross-industry insights and validations that could help in creating more robust and universally applicable synthetic models.
3. Integration with Real-time Data: Combining synthetic data with real-time data analytics could provide a more dynamic and accurate tool for scenario planning and decision-making.
4. Development of Standardized Tools: Creating standardized, easy-to-use platforms or tools for generating and managing synthetic businesses could simplify the process for companies and ensure consistency and compliance across industries.
5. Focus on Customizable Elements: Allowing businesses to customize their synthetic models to reflect specific demographic, geographic, or psychographic variables could enhance the practicality and applicability of synthetic businesses in diverse market conditions.

Claude

Summary

The main idea proposed is the creation of “Synthetic Businesses” – artificial companies or mirror worlds that can be used to demonstrate marketing technology platforms and solutions in a more compelling and relatable way for potential customers. The author proposes using generative AI to create synthetic customer data, behaviors, journeys, and interactions that mimic real-world scenarios specific to the industry or business of the prospect. This would allow for more targeted, contextualized demos that showcase the platform’s capabilities and value proposition in a realistic, experiential manner. The key benefits outlined are:

1. Targeted and industry-specific demos tailored to the prospect’s business context.
2. Ability to showcase end-to-end customer journeys and platform use cases in a compressed timeframe.
3. Real-time simulation of customer actions and corresponding platform responses.
4. Intuitive, interactive experiences akin to flight simulators for better engagement.
5. Accelerated sales process by demonstrating value proposition powerfully.

Enhancements

1. Develop a robust methodology or framework for rapidly creating Synthetic Businesses for different industries, leveraging generative AI, industry knowledge bases, and customer data (if available).
2. Explore the use of digital twins, agent-based modeling, and simulation techniques to create dynamic, interconnected Synthetic Business environments.
3. Incorporate interactive elements like conversational AI or virtual reality for an even more immersive demo experience.
4. Establish clear guidelines and safeguards around the use of synthetic data to ensure privacy, security, and ethical compliance.
5. Continuously evolve and update Synthetic Businesses based on customer feedback, market dynamics, and advancements in generative AI capabilities.
6. Explore potential use cases beyond sales demos, such as employee training, product development, or strategic decision-making simulations.

Moreover

The specific application of creating comprehensive “Synthetic Businesses” as interactive demo environments for sales seems to be a novel concept not widely implemented yet. Most current use cases focus on specific aspects like customer modeling, product testing, or AI training rather than constructing full-fledged artificial business replicas.

As generative AI capabilities continue to advance, we may see more industries exploring the idea of Synthetic Businesses for sales demos and simulations in the near future. However, as of now, there are no prominent examples of industries already doing this at scale.

Gemini

Summary

You propose creating “Synthetic Businesses” – virtual companies with realistic customer data and behavior – to improve B2B sales demos. These “mirror worlds” would allow salespeople to showcase their products in a more engaging and targeted way, accelerating the sales process.

Strengths

• Highly engaging demos: Synthetic businesses could provide a more interactive and realistic experience compared to traditional demos.
• Faster sales cycles: By showcasing real-world use cases within a familiar context, salespeople could close deals quicker.
• Privacy-compliant: Synthetic data avoids privacy concerns associated with real customer information.

Weaknesses

• Development complexity: Building and maintaining synthetic businesses with realistic customer behavior could be technically challenging and resource-intensive.
• Data bias: The algorithms used to generate synthetic data could introduce bias, leading to inaccurate representations of real-world scenarios.
• Ethical considerations: There might be ethical concerns surrounding the use of entirely fabricated businesses, especially if not clearly communicated to potential customers.

Enhancements

• Focus on specific industries:Developing industry-specific synthetic businesses could provide a more tailored and relevant experience for potential customers.
• Transparency: Clearly communicate the use of synthetic data to build trust with potential customers.
• Integrate with existing tools: Develop a way to integrate synthetic businesses with existing sales tools and platforms for a seamless workflow.
• Start small: Instead of full-fledged companies, consider creating smaller, targeted scenarios that address specific customer pain points.

10

Future

I gave this prompt to ChatGPT: “I want you to imagine the future. I am a Martech platforms vendor (like Netcore). I am giving a demo to a B2C eCommerce company using the “Synthetic Business” demo platform. Write a story detailing how the demo is done and the interaction with the B2C marketer who asks questions in between to gain a better understand. Also show a happy ending with an accelerated deal closure.”

Scene: A sleek conference room in the heart of a bustling city. Sunlight streams through the windows, casting a warm glow over the polished table where Michael, a seasoned sales executive from a leading martech platform vendor, prepares for a pivotal demo. Across from him sits Sarah, the Chief Marketing Officer of a prominent B2C eCommerce company, eager to see the latest in marketing technology.

Michael begins the demo with a confident smile, “Welcome, Sarah! Today, I’m excited to show you something that isn’t just revolutionary—it’s evolutionary. Let’s dive into our Synthetic Business platform, designed specifically for scenarios like yours.”

As he speaks, the large screen behind him lights up, displaying the dashboard of a synthetic eCommerce company named ‘Trendify’—a fictional yet meticulously detailed replica of a thriving online fashion retailer. “Trendify is our demo brand, created to demonstrate our platform’s capabilities in real-time and in a context that’s relevant to you.”

Sarah, intrigued, leans forward. “So, this Trendify… It’s all generated? The data, the transactions?”

“Exactly,” Michael responds. “Every bit of data you see is synthetic but reflects real-world complexities. Let’s start with customer segmentation.” He clicks into a segment dashboard showing various customer profiles. “These profiles are built from synthetic data, ensuring privacy and allowing us to model behaviors and preferences without ethical concerns.”

Sarah nods appreciatively, “Can you show me how you’d launch a targeted campaign for a new product line?”

“Of course,” Michael says as he navigates to the campaign module. He selects a segment of customers identified as ‘High Fashion Enthusiasts’ and sets up a campaign for a new line of eco-friendly apparel. The platform’s AI suggests optimal times for email and social media engagement, projected reach, and conversion rates—all powered by synthetic data.

Sarah questions, “How accurate are these projections?”

“They’re based on the aggregated behaviors from our synthetic model, which mirrors diverse real customer interactions,” Michael explains. “We continuously update and refine our AI models to enhance accuracy.”

Impressed, Sarah wonders about personalization capabilities. “Could we customize offers for individual customers based on their past interactions?”

“Absolutely,” Michael says, showcasing a dynamic personalization engine. “Our platform can trigger unique offers based on customer activity, and because we’re using synthetic data, we can simulate several outcomes before going live.”

He demonstrates by selecting a synthetic customer, simulating their journey through Trendify’s website, and displaying how personalized messages and offers appear in real-time. “This helps in understanding and perfecting engagement strategies without risking brand reputation or customer experience.”

Sarah, now deeply engaged, asks about integrating her existing tools with this platform. Michael assures her of seamless integration capabilities with popular CRM, ERP, and analytics tools, enhancing her current infrastructure without disruptions.

“As a CMO, you might also be interested in our analytics suite,” Michael transitions, displaying dashboards enriched with insights on customer lifetime value, churn rates, and ROI of marketing campaigns. “These insights allow you to make data-driven decisions swiftly and confidently.”

Seeing the comprehensive capabilities, Sarah seems convinced but has one last concern, “Implementation can be a nightmare. What’s the reality of integrating this into our systems?”

“We understand that concern very well,” Michael sympathizes. “That’s why we have a dedicated team to ensure a smooth transition. We assist with everything from data migration to staff training and support. Plus, our platform is designed to be as intuitive as possible, minimizing the learning curve.”

Sarah, now reassured and excited about the potential, reflects on the power of the demo. “Seeing this in action, tailored so closely to what we do, makes all the difference. It’s like watching a day in the life of our own company, but enhanced.”

“That’s exactly what we aim for,” Michael concludes with a smile. “Should we discuss the next steps?”

As the demo wraps up, Sarah is not just satisfied; she’s impressed. She recognizes the immediate value this platform could bring to her company. Discussions move quickly to procurement, and within a few weeks, the deal is signed. Implementation goes smoothly with the martech vendor’s robust support system.

Three months later, Sarah reviews the quarterly performance, noting significant improvements in customer engagement and conversion rates. She sends Michael a grateful email: “Your demo wasn’t just a presentation; it was a promise well kept. We’ve seen remarkable results already. Looking forward to more innovations from your team.”

Michael, reading the email, feels a deep sense of fulfillment. The Synthetic Business demo didn’t just close a deal; it started a partnership, one that would grow and evolve with each company’s success.