MAM
ML & AI to play a key role for marketers in the third connected age: Rishad Tobaccowala
MUMBAI: The world is now entering the third connected age and it is going to have a huge impact on various areas of advertising and marketing. Shifts are happening including data connecting to data, which is machine learning. Artificial Intelligence is fuelling everything including marketing, advertising services, and biotech.
Advisor, speaker, and educator Rishad Tobaccowala who is also an author spoke at the MMA India seminar called ‘Impact India: The Future of Modern Marketing’ recently. While addressing at the event, he noted that the first shift going on in the third connected age is machine learning. The second shift going on in the third connected age besides machine learning is that there are much faster ways of connecting like Jio which has had a massive impact. “In the next few years, India will move faster and faster to 5G. It is very resilient and one can download a high definition movie in seven seconds or less. It will open up more things,” added Tobaccowala during his talks at the event.
He also highlighted that mobility will not just be about the mobile device. It could be things like glasses, and headphones because the third shift will be about new ways of connecting. He gives the example of ‘Voice’ and this will continue as devices become smaller and more powerful. Also, augmented reality technology will become important. Virtual reality will also happen though it is in its early days. It will have a big impact on areas like gaming which is already bigger than movies and gambling. The fourth shift will be new trust currencies emerging and blockchain will be the key factor. This third connected age will build on the first and second connected ages and will change the way the world of marketing exists.
He noted that 1993 was the first connected age with the world wide web where people connected to interact. That gave birth to businesses like search and e-commerce and to companies like Google and Amazon. In 2007 there was the second connected age which was about being connected all the time and it gave rise to mobile, and social media. Today, most traffic in the US comes from mobile and unlikely from the desktop. These three ages build on each other and they will not replace each other.
He noted that Audience, Brands, Content, Data and Enterprise are now significantly shifting. Every marketer recognises that they do not just send messages to audiences. Increasingly, customers are becoming active rather than passive.
He also spoke about the move towards re-aggregated rather than segmented marketing. In the latter, you take large audiences and make them smaller audiences like TV, newspapers including others. In the former, you are able to thrive in the digital age. You start with an audience of one and re-aggregate him/her through things like decision engines.
He mentioned three trends that will grow when one talks about brands. The first is that brands will increasingly become experiences. That is important as a great experience will result in people speaking about it and becoming loyal. Second, the brand’s purpose will become important. What does a company stand for? The third new trend is branding as employees. In the future, no company will survive without its employees and suppliers. “The most important advocate for brands even more than happy customers is happy employees,” he added.
In terms of content, he noted that it is impossible to put content into a bucket. He gave the example of seeing an ad on Tiktok while travelling in a car resulting in him buying a product at a store. Is it ATL or BTL? Is it offline or online? Is it mobile e-commerce or social? Is it analogue or digital? It was all those. Content is morphing and so is marketing. Also, new content creators are changing pretty dramatically.
He mentions the fact that more views and interactions are happening for Instagram, and Youtube creators than those who watch the Superbowl. New kinds of content will emerge. In terms of data, he said marketers must recognise that data alone is not the way brands are built. It is about how you extract meaning from data, and how you tell stories utilising data. The big mistake people make is thinking that data by itself is the differentiator. Connecting data to other things in a company is what will make the difference. Less than half a dozen companies have special data. So, it is how you use, and leverage data to tell stories. That is important because human beings choose with their hearts to make purchases. Then they use data to justify what they just did. That is what happens 7 out of 10 times.
The future is about data-driven storytelling, not data-driven marketing. Marketers cannot be only driven by data numbers otherwise they will be out of a job, he warned.
He stressed that marketing is about stories & insights and not just about data and algorithms. Pure numbers are not the answer.
Enterprise, he said, is rethinking the way an organisation is set up for the future. The future, he cautions cannot fit into the containers of the past. The best marketing companies, he said, revolve around people speaking up freely and challenging each other. That is how ideas are born. Also, the best companies constantly reinvent themselves.
So, how does one do this? First, constantly learn, he said and added that if marketers do not then they will fake it and will become irrelevant as individuals, marketers and businesspersons. Marketers must set aside an hour a day to learn new things.
Also, marketers must build a case for the exact opposite of what they believe. That will strengthen an argument. Third, learn new technologies by doing things. “Learn, do, build the case for the opposite” he concluded.
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.
