MAM
Unlocking net worth with the potential of pre-IPO investments
Mumbai: Do you know that you can invest in some of the most promising and profitable companies in India before they go public? Yes, this is possible through Investing in pre-IPO shares.
Investing in companies before they go public can be a lucrative opportunity for investors who want to get an early stake in the next big thing. However, finding and accessing such pre-IPO deals can be challenging, especially for retail investors who lack the connections and resources like institutional investors.
What are pre-IPO investments?
Pre-IPO shares are shares of a company that are sold to investors before the company launches its initial public offering (IPO). Pre-IPO shares offer a unique opportunity to get early access to high-growth companies and potentially earn huge returns. Pre-IPO investments occur during a company’s growth phase before its stock becomes publicly available after an IPO.
Pre-IPO investments play a crucial role in unlocking net worth for savvy investors. Let’s explore how:
1. High return potential: Investing early in a company’s growth journey can yield substantial returns if the company’s value increases post-IPO.
2. Low allotments: Pre-IPO investments offer an opportunity to invest at a discounted valuation before shares become publicly available, avoiding the challenges of oversubscribed IPOs.
3. Invest in growth: Many companies choose to stay private for an extended period, and retail investors may miss out on the high-growth phase.
The following companies show the pre-IPO share price and the expected IPO price of some of the companies that offers, and the potential return on investment:
1. CDSL (Central Depository Services Ltd)
CDSL, a Mumbai-based central securities depository, had a PreIPO price of ₹60. After ~8 years, it’s now trading at ~₹1,800, boasting an impressive average annual return of ~400 per cent. A Rs. 10 lakh investment would now be worth around Rs. 3 crores.
2. Anandrathi
Anand Rathi Wealth Ltd, an Indian wealth solutions company, had a PreIPO price of ₹267. After 3.4 years, it’s now at ~₹4,000, with an average annual return of ~400 per cent. A Rs. 10 lakh investment would now be worth around Rs. 1.5 crores.
3. BSE (Bombay Stock Exchange)
BSE is an Indian stock exchange that allows investors to trade in stocks, equities, mutual funds, commodities, derivatives etc. BSE was available in the PreIPO at an investment price of ₹67. After the tenure of 7 years, the current price stands at ~ ₹2,860. The investment yielded an impressive average annual return of ~595 per cent. If an individual investor has invested Rs. 10 lakhs, the value is Rs. 4.2 Crs.
4. Nazara Technologies
Nazara Technologies, a Mumbai-based mobile gaming company and sports media platform, saw its PreIPO investment price at ₹225. After 1.5 years, the present price stands at ₹650, yielding an impressive average absolute return of ~130 per cent. For an individual investor who invested Rs. 10 lakhs, the invested value now stands at Rs. 28 lakhs.
5. Tata Technologies Ltd
Tata Technologies Ltd, an India-based global engineering services company offering product development and digital solutions, had a PreIPO investment price of ₹90. After approximately 3 years, the present price stands at ₹1,100, resulting in an impressive average absolute return of ~380 per cent. For an individual investor who invested Rs. 10 lakhs, the invested value now stands at Rs. 1.2 crores.
Hot Investments in Pre-IPO
1. Studds: Two-Wheeler Helmets and Lifestyle Accessories
Studds Accessories Ltd is a globally recognized brand specializing in manufacturing two-wheeler helmets and lifestyle accessories. With the strong operational fundamentals, the company has established a monopoly business, exporting to more than 40 countries.
Financial snapshot (in Rs. Crs)
The financials of studds accessories for the FY22-FY23 as: In FY22, Studds Accessories recorded a total revenue of ₹466 crore in FY22 and in FY23, this figure increased to ₹504 crore. The gross profit for Studds Accessories in FY22 stood at ₹201 crore and in FY23, it improved to ₹239 crore.
Studds Accessories achieved an EBITDA of ₹57 crore in FY22 and in FY23, this metric rose to ₹64 crore. The company’s Profit Before Tax was ₹40 crore. By FY23, it had increased to ₹47 crore. Studds Accessories’ net income in FY22 amounted to ₹29 crore. In FY23, the net income further improved to ₹34 crore.
The earnings per share for Studds Accessories were ₹15 in FY22. By FY23, this metric had risen to ₹17. These financials indicate positive growth trends for Studds Accessories during this period.
2. National Stock Exchange(NSE)
NSE is gearing up for its IPO. However, SEBI (Securities and Exchange Board of India) has set pre-conditions for NSE:
a. Strengthening technology infrastructure: NSE must ensure glitch-free operations for at least one year.
b. Enhancing corporate governance: NSE needs to improve its standards of corporate governance before filing for the IPO.
Financial snapshot (in Rs. Crs)
NSE recorded a total revenue of ₹8,652 crore and in FY22, this figure increased to ₹12,347 crore in FY23. NSE achieved an EBITDA of ₹6,944 crore in FY22 and in FY23, this metric rose to ₹10,121 crore. The company’s Profit Before Tax was ₹6,810 crore. By FY23, it had increased to ₹10,386 crore. NSE net income in FY22 amounted to ₹5,111 crore. In FY23, the net income further improved to ₹7,846 crore.
The earnings per share for NSE were ₹105 in FY22. By FY23, this metric had risen to ₹149. These financials indicate positive growth trends for Studds Accessories during this period.
Both Studds and NSE represent exciting investment opportunities in the pre-IPO space. Whether you’re eyeing innovative lifestyle accessories or the backbone of India’s stock market, these ventures are worth exploring!
However, investing in pre-IPO shares isn’t always easy, as it entails uncertainties and regulations. That’s why you need a reliable and trustworthy platform that can help you find, buy, and sell pre-IPO shares in India. So, whether you’re a seasoned investor or just dipping your toes into the investment waters, consider exploring the exciting world of pre-IPO stocks.
The article has been authored by Planify founder & CEO Rajesh Singla.
Digital
GUEST COLUMN: How AI is restructuring distributor and retailer motivation models
From incentives to intelligence, AI is redefining how brands engage channel partners
MUMBAI: Artificial intelligence is rapidly transforming how brands engage with their most critical yet often overlooked stakeholders: distributors, retailers, and last-mile influencers. For Abhinav Jain, co-founder and CEO of Almonds Ai, this shift marks a fundamental departure from traditional, transaction-led incentive models toward behaviour-driven, data-intelligent ecosystems. In this piece, Jain examines how AI is enabling brands to decode partner motivations, predict engagement patterns, and deliver personalised, scalable experiences—ultimately redefining channel relationships from transactional exchanges to long-term growth partnerships.
Across many sectors, there is increasing recognition that motivating those who bring products to market (distributors, retailers, last-mile influencers) poses a growing challenge.
Brands continue to invest significant marketing and digital resources to consumers, yet in many countries and the vast majority of emerging economies, these types of consumer-focused investment areas have had little impact on ultimate product delivery. Rather, it is still the case that traditional retail continues to make up most products sold.
So why is it that the systems built around motivating these channels have yet to evolve?
For decades, distributor and retailer engagement revolved around static schemes – quarterly targets, volume-based rewards, and occasional trade promotions. These programs were designed around transactions, not behaviour. The assumption was simple: if incentives increase, performance will follow.
Now, with the advent of artificial intelligence, the definition of performance is being challenged.
With the development of artificial intelligence, businesses can move beyond simply creating loyalty based on transactional-based models and toward models built on behaviours, the behaviours of channel partners that are intrinsic to their motivations in engaging with particular brands. As a result, the means by which businesses develop relationships within their distribution network are starting to evolve; thus, ultimately changing how brands interact with those within their distribution network.
Assessing engagement: Transitioning from transactional- to behavioural intelligence
Traditional loyalty systems refer to transactional activity (sales data). Although this data is valuable and important, it only provides a partial view of engagement across the channel partner.
For example, a retailer may have a high frequency of sales of a product, but their lack of engagement with the manufacturer would not reflect that they have true loyalty toward that brand. Conversely, a retailer who actively participates in training programmes, acts as brand advocates, and is engaged in learning with the supplier would exhibit more profound levels of loyalty but would have been invisible based on historical incentive programmes.
Artificial intelligence allows for the identification of behaviours that help to address this gap. Brands are able to use a variety of engagement data points, participate in learning programs, respond to communications, redeem behaviour and track platform use behaviour in order to identify motivation through behaviour.
McKinsey has stated that companies that leverage advanced analytics for their sales and distribution functions can achieve as much as a 15-20 per cent increase in productivity due to increased awareness of their behavioural trends throughout their networks.
This visibility of behavioural patterns within channel ecosystems can be transformational to brands as they can now view how partners engage on their path to purchasing products, instead of just measuring the sales revenue generated by those purchases.
Predicting motivations, not just measuring performance
Possibly, the largest contribution of Artificial Intelligence (AI) to helping brands engage with partners via channel ecosystems is its ability to predict future engagement versus simply measuring past performance.
Traditionally, brands only realised that a partner was disengaged (not likely to purchase products) once their sales performance had already declined. By then, the brand would have to use significant amounts of incentives or aggressive promotional activities to recovery their partner’s engagement level.
AI models can help organisations to detect early signs that a partner is becoming disengaged, such as declining participation in learning modules, declining interaction via the platform, or slower reward redemption rates. These indicators can help organisations to proactively engage with their partners before their sales performance begins to decline.
The practical application of AI and predictive analytics gives brands the ability to re-engage with their partners prior to their sales performance declines. For example, instead of developing and implementing broad-reaching incentive programs that provide a “one size fits all” incentive to all partners in an ecosystem, brands are able to develop targeted, engaging re-engagement programmes. This is how personalisation can be done on a large scale, such as across global distribution and retail networks.
The vast majority of distributor and retailer channels have thousands, if not millions, of individual channel partners. Historically, providing personalisation to such a large number of businesses has not been feasible.
However, with the advent of AI, personalisation at scale is becoming a reality.
Brands can now create tailored engagement journeys for all their partners, based on their partner profiles, through some combination of machine learning models and behavioural segmentation. For example, high-performing distributors might receive higher levels of leadership-based recognition and greater incentives to continue to grow. Emerging retailers, on the other hand, might be supported with training, onboarding rewards, and measurable performance milestones.
The shift towards personalisation of partner engagement echoes the direction that consumer marketing is already moving towards.
According to Salesforce’s report, over 70 per cent of customers expect personalisation in the way that brands engage with them. As such, there is a growing expectation for B2B ecosystems to have these same types of expectations from their channel partners.
Gamification and continuous engagement
AI is also radically changing how brands will engage with their channel partners through the use of gamification.
Many traditional incentive-based contests and leaderboards would spark temporary engagement among their participants, but they struggled to sustain engagement over time. With the use of AI, gamification mechanics are evolving dynamically based on historical and evolving participation patterns by their channel partners.
Challenges, rewards, and recognition structures can be modified continuously in order to sustain engagement with all of a brand’s partner segments. This will provide a greater opportunity to move away from episodic campaigns towards ongoing, continuous engagement experiences.
When channel partners receive motivation as part of their daily business activities through recognition, learning, and tracking their performance, long-term loyalty will be achieved.
Aligning motivation to broader impact
There is a growing trend within the channel ecosystem to integrate sustainability and socially responsible behaviours into the channel partner programmes of brands.
Increasingly, brands are motivating their partners to use sustainable practices in their operations, participate in sustainable practices like sustainability-related knowledge programmes, or promote products that are in line with their sustainability objectives.
Brands can use AI to monitor and measure these types of behaviours and incorporate them into their incentive frameworks so that brands can align their commercial objectives with broader social and environmental outcomes.
A shift in the way brands view their channel partners
AI is having the most significant impact on the way that brands are now viewing their channel partners, as it relates to the underlying philosophy of those fundamental relationships.
For the past several decades, many brands have viewed their channel partners as intermediaries in the supply chain. More and more brands are now beginning to view their channel partners as key ‘partners-in-growth,’ and their actions can have a direct impact on market performance.
In fact, all the channel ecosystems are using behavioural engagement platforms to design new models that reward not just transactional behaviour, but also create continuous engagement journeys for their partners, where their partners can receive recognition for their participation, learning, and continued engagement, thereby reinforcing long-term loyalty to the brand.
The future: Intelligent channel ecosystems
As we consider what the next phase of channel engagement may look like, many believe that it will be based on intelligent ecosystems, using AI to continuously monitor and adjust the engagement strategies used to engage their channel partners, in real time and based on the behaviours of those partners.
For brands operating in complex distribution networks, the ability to perform well will be determined both by whether products are available to their customers, as well as by the enthusiasm, expertise, and loyalty shown from each channel partner that represents the brand each and every day that they are working on behalf of the brand.
While AI clearly does not eliminate the human aspect of a brand’s relationship with its channel partners, it does allow brands to better understand and nurture that relationship.
In markets where the last mile will determine whether a sale is made, how one leverages the intelligence gained by using AI will ultimately be the difference between gaining a new, sustainable competitive advantage versus losing one.
