Transforming Retail with AI: Insights from Pressto’s Marketing Evolution
Akshay Matkar, Chief Growth Officer at Pressto, is on a mission to revolutionise the retail experience. In this interview, he shares his passion for leveraging AI and technology to create seamless and personalised journeys for customers. From implementing dynamic pricing strategies to building a data-driven culture, Akshay provides a unique perspective on the challenges and opportunities facing retailers today. He also discusses the critical role of ethical AI and the importance of human-centred innovation in driving sustainable growth.
Key Highlights from the Interview
- AI-Driven Innovations at Pressto: Pressto has implemented hyper-personalisation, marketing automation tools, chatbots, dynamic pricing, and an in-house analytics tool to enhance customer experience, operational efficiency, and strategic market expansion.
- Data Quality and Privacy as Foundations: Ensuring data accuracy, consistency, and security through robust governance frameworks, encryption, and privacy measures has been critical to building trust and enabling effective AI-driven decision-making.
- AI in Customer Engagement and Retention: AI-powered tools like predictive analytics, sentiment analysis, and chatbots have improved customer acquisition, engagement, and retention by offering personalised experiences and proactive service delivery.
- Strategic AI Adoption and Change Management: Addressing challenges such as resistance to change, biases, and scalability issues requires transparency, incremental implementation, and alignment of AI solutions with business objectives to deliver measurable results.
- Emerging Trends Shaping Retail: AI trends like hyper-personalisation, visual search, AR/VR, supply chain optimisation, and consumer behaviour analytics are transforming retail, offering opportunities to enhance customer experiences and operational outcomes.
“By focusing on data quality, managing biases, ensuring scalability, and fostering acceptance through change management, organisations can harness the full potential of AI.”
Could you start by sharing your professional journey, particularly your experience with marketing technology? How have you seen its implementation evolve across organisations throughout your career?
Akshay: I’ll start with an update and a disclaimer regarding Pressto. At Pressto, many initiatives are either in progress or recently implemented, so I don’t yet have long-term results to share. Before I joined, the brand operated using very traditional marketing methods. Since my arrival, we’ve introduced significant changes, but the results are still in their initial stages.
When we last spoke in 2020 or 2021—likely during the lockdown—the discussion was centred around AI-led personalisation. At that time, I was with Candere, leading the marketing, data team and brand communications. Back then, we were exploring personalisation, but since then, we have expanded into advanced AI use cases, including post-purchase recommendations, predictive analytics, and dynamic pricing. The journey evolved from being purely e-commerce to adopting an omnichannel approach.
In April 2024, Kalyan Jewellers, who were initially investors in Candere, fully acquired the company. With the acquisition came management changes, and after spending a significant amount of time at Candere, I decided it was the right moment to try something new. Retail had always been an area of interest for me, particularly its challenges in adopting technology. That curiosity led me to my current role at Pressto, which focuses on marketing, sales expansion, and analytics.
At Pressto, we are currently operating in Mumbai, Delhi, and Bangalore, with around 50 premium laundry service stores, and we plan to expand to another 20 locations within the next 12 months. Hyderabad is one of the next identified markets, and work is underway to launch there soon.
Our current focus is on digitisation, so we’ve initiated several changes. This includes implementing personalisation, hyper-personalised communication, marketing automation tools, chatbots, and the automation of our call centre. We’ve also begun running digital ad campaigns that rely heavily on AI for audience targeting and bid optimisation. Additionally, we’ve developed an in-house data analytics tool to identify potential markets based on a variety of parameters. This helps us benchmark current markets and identify high-potential areas for expansion.
Given our premium clientele, we focus on generating and analysing premium signals to refine our strategies. We’ve also started building detailed customer segments using models like the ‘Runners, Repeaters, and Strangers’ framework. Predictive customer analysis and dynamic pricing are next on our agenda. We’re working on a system to dynamically adjust pricing based on these factors.
The role is highly dynamic, blending e-commerce expertise with the nuances of the retail industry. While some aspects differ, such as the absence of a heavy focus on website traffic, 80% of the skills and strategies from my previous experience remain relevant and have been seamlessly carried forward.
Could you share a brief overview of Pressto, including the current customer profile and the key business focus areas?
Akshay: Pressto is an international brand originally from Spain, operating independently in different regions. Pressto India was launched in 2008 in Mumbai and focuses on providing premium laundry services.
In the laundry sector, there are typically three models:
- Home Washing: Done manually or using household machines.
- Local Dhobis: Traditional neighbourhood laundry services.
- Factory Model: Warehousing operations where clothes are picked up, cleaned centrally, and delivered back.
Pressto India follows a distinct cluster model, offering personalised services through different types of stores:
- Main Stores: Equipped with dry-cleaning and advanced machinery for full-service cleaning.
- Junior Stores: Handles cleaning tasks under supervision.
- Service Stores: Focus on delivery, collection, and pressing to perfection.
Additionally, Pressto offers premium care for leather shoes and bags, catering to clients who require specialised cleaning and maintenance.
For added convenience, Pressto operates a “Pressto at Doorstep” service. Dedicated Pressto vehicles collect laundry directly from customers’ homes or societies and deliver it back once cleaned. This ensures a seamless and hassle-free experience.
All stores are company-owned, with no franchise operations, ensuring consistent quality and adherence to operational protocols. This combination of high-quality care, a cluster model, and customer convenience sets us apart in the premium laundry service market.
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How has AI influenced growth strategies in Pressto or other organisations you’ve worked with? In what ways has AI been integrated into expansion and growth initiatives?
Akshay: From my experience, AI has had a significant impact on both customer acquisition and retention strategies. My primary exposure to AI has been through its applications in personalisation, chatbots, predictive analytics, ad optimisation, and customer sentiment analysis.
1. Personalisation
AI has revolutionised personalisation, especially in marketing communications. Today, it’s not just about addressing customers by name; true personalisation involves understanding customer intent and delivering tailored experiences. For instance:
- In my previous role at Candere and now at Pressto, personalisation in communications has significantly improved customer engagement by cutting through the noise of crowded, generic messaging.
- This approach helps differentiate brands, particularly in competitive markets where multiple players chase the same customer.
2. Chatbots and Virtual Assistants
On the customer acquisition side, chatbots and virtual assistants are instrumental in:
- Narrowing down customer searches to match their specific needs.
- Handling generic queries efficiently, freeing up human agents for complex issues.
On the retention side, chatbots reduce dependency on agents by managing routine interactions and escalating only when necessary, saving time and resources.
3. Predictive Analytics
Predictive analytics plays a pivotal role in both acquisition and retention:
- Customer Behaviour Analysis: Helps identify high-value customer cohorts, churn risks, and behavioural patterns to guide strategic focus.
- Churn Prediction: AI predicts potential customer churn, allowing proactive engagement to retain them, which is critical as retention impacts the bottom line more than acquisition.
4. Ad Optimisation
AI enhances ad performance by optimising targeting and maximising ROI. While managing ad bids is relatively straightforward, achieving precise targeting and performance scaling requires intelligent algorithms to ensure the right balance between cost and effectiveness.
5. Dynamic Pricing
At Pressto, we are working on implementing dynamic pricing to create a win-win for both customers and the brand:
- AI analyses multiple factors, such as market conditions, customer behaviour, and inflation, to compute optimal pricing.
- This approach ensures fairness, balancing the brand’s need to offset costs with the customer’s expectation for value.
6. Sentiment Analysis
Sentiment analysis through metrics like CSAT (Customer Satisfaction Score) and NPS (Net Promoter Score) is vital:
- These lead indicators provide real-time insights into customer sentiment, allowing brands to take proactive actions before issues escalate.
- Monitoring CSAT and NPS daily helps identify trends and areas of concern, supporting a more responsive and customer-centric approach.
In summary, AI has been instrumental in achieving business goals by improving personalisation, streamlining processes, and enhancing customer insights. These tools have proven invaluable in both my previous and current roles, driving measurable success in acquisition, retention, and overall customer experience.
With consumer data coming from multiple data lakes, how has AI helped you manage and integrate this information? Has it contributed to achieving a 360-degree view of the customer to support pricing strategies, customer retention, and personalised messaging?
Akshay: Before leveraging AI for decision-making, ensuring the quality of data is paramount. AI’s effectiveness heavily depends on the accuracy and consistency of the data it processes. At Pressto, we encountered significant challenges with data quality when we began. Issues such as incomplete, inaccurate, and inconsistent data were prevalent. It was evident that expecting meaningful insights from AI without first addressing these issues would be futile—much like aiming for a fitter body without maintaining a proper diet.
Our first step was to prioritise data quality improvements. By focusing on cleansing and standardising our data, we laid a strong foundation for any AI models we intend to build. This ensures that the insights derived are reliable and actionable.
Equally critical is the protection of customer data. As a brand, our customers are our most valuable asset, and safeguarding their information is a top priority. To address privacy concerns:
- Privacy Policies: We have implemented robust privacy policies to ensure customer data is protected and not misused.
- Encryption and Role-Based Access: We’ve started encrypting sensitive data and implementing role-based access controls to restrict unauthorised access.
- Third-Party Vendor Compliance: Contracts with third-party applications include strict clauses to prevent data misuse and ensure compliance with our privacy standards.
To further strengthen our approach, we are developing a data governance framework. This includes:
- Regular audits for data usage and modeling processes, similar to the audits conducted in other areas of the business.
- Continuous monitoring and revision of data handling practices to ensure compliance with regulations and internal policies.
These efforts in data quality, privacy, and governance form the cornerstone of our strategy. They not only enable AI to function effectively but also ensure that any decisions or models derived from our data are ethical, accurate, and aligned with customer expectations.
By taking these foundational steps, we aim to build a robust system where AI can truly deliver value while maintaining the trust and confidence of our customers.
What challenges have you encountered when implementing AI use cases, particularly regarding adoption within the organisation? What guardrails would you recommend to mitigate risks and address common issues during an organisation’s AI journey?
Akshay: Implementing AI solutions comes with its fair share of challenges, particularly when it comes to gaining internal acceptance and ensuring smooth integration. Here are some of the key challenges I’ve encountered, along with strategies to address them.
Data Quality and Availability
One of the most fundamental challenges is ensuring that the data fed into AI models is accurate, complete, and consistent. Poor data quality often leads to subpar model performance and unreliable outputs. When I joined Pressto, we encountered issues like inconsistent and incomplete data, which required immediate attention.
To address this, we focused on implementing robust data governance practices. Regular audits and data augmentation techniques were introduced to ensure the integrity of the data. These steps not only improved data quality but also created a solid foundation for building effective AI models.
Bias and Resistance to Change
Bias and resistance to AI-driven outcomes are common challenges in AI adoption. Many individuals, especially those accustomed to traditional methods like manual data analysis on Excel, develop their own patterns and references over time. When AI-generated results deviate from these established norms, scepticism often arises.
Resistance to change is another hurdle, particularly when AI outputs do not align with preconceived expectations. For instance, if an AI system delivers results that differ from long-standing human-derived conclusions, it can lead to doubts about the system’s reliability.
To mitigate these issues, transparency and education are crucial. Explaining how AI models work and validating their outputs against historical data can help build trust. Training programs and stakeholder involvement throughout the implementation process are also essential to foster acceptance and understanding.
Scalability and Integration
While simple use cases for AI are often easier to implement and gain acceptance for, scaling to more complex applications presents significant challenges. Advanced use cases may require substantial computational resources, modular system architectures, and cloud infrastructure.
To address this, we took an incremental approach. Starting with smaller, manageable projects allowed us to build confidence and demonstrate value before tackling more resource-intensive implementations. Ensuring that the necessary infrastructure and resources are in place before scaling is another critical factor.
Change Management
Another major challenge is resistance to change within the organisation. This often stems from a lack of understanding of AI systems or disappointment when AI outcomes don’t immediately meet expectations.
Overcoming this requires a combination of clear communication and continuous engagement with stakeholders. Highlighting tangible benefits and demonstrating measurable improvements can help build momentum and trust in the AI system.
Ethical and Legal Compliance
Fortunately, I have not encountered significant ethical or legal compliance challenges in AI implementation so far. However, this doesn’t diminish the importance of proactively adhering to data privacy and security standards. At Pressto, we prioritise ethical data handling, even in the absence of strict enforcement of regulations like GDPR in India. Measures like encryption, role-based access controls, and robust privacy policies have been implemented to protect sensitive information.
The journey of AI adoption requires a thoughtful approach to address these challenges effectively. By focusing on data quality, managing biases, ensuring scalability, and fostering acceptance through change management, organisations can harness the full potential of AI. Building trust, maintaining transparency, and adhering to ethical practices are vital steps in creating sustainable AI solutions that drive meaningful business impact.
What emerging AI-driven trends do you foresee shaping the retail industry over the next 3-5 years?
Akshay: Emerging trends in AI are poised to transform the retail industry, and several areas stand out as particularly impactful:
- Hyper-Personalisation: Hyper-personalisation is a significant trend and one of my areas of interest. However, achieving it is complex and requires deep insights into customer behaviour. Successfully delivering tailored experiences can greatly enhance customer loyalty and engagement.
- Visual Search: While not directly related to my current domain, visual search was a critical use case during my time at Candere, where we managed a catalogue of over 10,000 products. Visual search technology simplifies the customer journey by allowing users to find exactly what they’re looking for, making it invaluable for e-commerce platforms with extensive inventories.
- Supply Chain Optimisation: Optimising the supply chain is another key area, particularly in managing inventory, forecasting demand, and controlling costs. Efficient supply chain management directly impacts the bottom line by reducing operational inefficiencies and improving profitability.
- Sentiment Analysis: Customer sentiment has become a vital factor for brands. Decisions are increasingly driven by customer reviews, making sentiment analysis essential for tracking metrics like Customer Acquisition Cost (CAC), Customer Satisfaction (CSAT), and Net Promoter Score (NPS). This helps brands adapt strategies based on real-time customer feedback.
- AR and VR: While my experience with augmented and virtual reality is limited, these technologies are gaining traction across industries, particularly in enhancing customer engagement and creating immersive shopping experiences.
- Enhanced Customer Support: Customer support continues to be a critical area for all industries. AI enhances efficiency and outcomes, enabling quicker resolution of customer queries and a more seamless support experience.
- Consumer Behaviour Analytics: Understanding consumer behaviour remains a personal favourite area of focus. AI allows for deep analysis of consumer segmentation, movement within segments, and behavioural trends. These insights help design effective marketing strategies, forecast demand, and refine data-driven approaches to meet evolving customer needs.
In summary, these trends—ranging from hyper-personalisation to supply chain optimisation and consumer analytics—highlight how AI is set to reshape the retail landscape, enhancing efficiency, customer engagement, and overall business performance.
As we create this guide, what are your expectations from the report? What key insights or takeaways would you find most valuable for someone in your position?
Akshay: I’m particularly interested in exploring the AI evolution roadmap in the report. It would be valuable to include insights on how AI progresses through its stages, such as Perception AI, Generative AI, Cognitive AI, and eventually Physical AI. Understanding this evolution is essential for readers to identify how AI can best align with their specific industries and use cases.
The report could provide an overview of each stage, detailing their capabilities, applications, and potential impact. It would also be helpful to highlight the relevance of these AI stages across various industries—for example, how retail might benefit more from Generative AI, while manufacturing might find greater value in Physical AI. Matching business challenges and customer segments with the appropriate AI technologies can guide organisations in making informed decisions.
Additionally, the report should emphasise the importance of strategic decision-making when adopting AI. With AI becoming a buzzword, it’s tempting for businesses to adopt it purely because it’s trending. However, organisations must carefully assess whether the implementation aligns with their objectives and will deliver measurable value rather than consuming time, money, and resources without tangible outcomes.
By providing a clear roadmap and practical guidance, the report can empower businesses to navigate the complexities of AI adoption effectively, ensuring their efforts are purposeful and result-oriented.
