Analytics and machine learning can help us understand customer needs: Seema Agarwal, Manthan

Analyzing customer needs and ensuring fashion trends are in sync with today’s fashion-centric generation is the need of the hour, says Seema Agarwal, VP-Products, Manthan.

Today, fashion is not just about wearing the right clothes. Everyone wants to know the latest trends in fashion, and organizations are leveraging technology to inform their customers about this. To this end, Manthan, a leader in providing analytics solutions to high-end fashion retailers such as Future Group and Crocs and luxury brands such as Zara and Swarovski, is instrumental in bringing technology to fashion.  

Here’s what Seema Agarwal, Vice President-Products, Manthan, has to tell about the use of technologies such as big data, analytics, and IoT when tailoring to the needs of today’s tech savvy and fashion crazy market.

What is big data’s contribution in better retail planning and execution?

For better decision making in retail planning and execution, retailers need more accuracy on the business context, more granularity in terms of data and insights, and the ability to respond closer to or in real time. Making effective decisions in today’s context of customer-centric retailing and omni-channel customer behaviour requires processing of massive amounts of data generated within and outside the organization, from various structured and unstructured sources. This is where big data technologies provide practical solutions that deliver performance and economies at scale.

With predictive and prescriptive analytics running on big data platforms, retailers could make new products fly off shelves, create the most competitive pricing models, run the most effective real-time contextual personalization campaigns while ensuring the best customer experience possible.

Understanding customer behaviour and its impact on shopping decisions is a relatively underutilized science, mainly due to the inability to gather and process more data points with traditional data management systems. Today’s omni-channel shopper leaves more than 30 times the digital data footprint of a traditional store shopper. With the increasing penetration of IoT in shopping, this figure is expected to dramatically increase. Only a combination of big data, machine learning and predictive & prescriptive analytics can tap into the real potential of how organizations understand and respond effectively to customer needs today.

In addition, big data can be used to predict emerging or changing fashion trends, and understand their impact on retail demand right down to the regional or store level. Social media and search are great sources to predict the performance of new styles and designs right from when they are launched in a fashion show to well before they are launched in the retail store. This information helps businesses optimize their production and supply chain to maximize revenues and minimize markdowns.

What is the role of digital marketing and social media analytics in the fashion industry?

The seemingly never-ending bounty of social data that consumers churn out every waking second can help manufacturers and retailers get a handle on how consumer needs and preferences evolve. By leveraging the right kind of monitoring and analytics, retailers get an extremely valuable opportunity to stay ahead of fashion trend curves. This not only enhances shopper expectations but also helps fashion retailers to have control over inventory by influencing demand through promotional, pricing and supply chain strategies.

We have observed growth in social media analytics and business intelligence through digital marketing. Digital marketing analytics expenses increased by 60 percent in 2015 as branding and advertising businesses boomed. Likewise, we think social media and online advertising on mobile will continue to grow as integration of offline and online customer experience is on the rise. This has increased consumer brands’ ability to digitally influence customers and the digitally empowered customer's ability to influence brand image and value.

How are fashion retailers viewing the adoption of mobility in the use of analytical technology in-store, at location, or even customer facing apps?

Analytical mobile apps are finding quick adoption as a tool that significantly raises the retailer’s ability to engage customers and deliver superiors experiences, especially in-store. Analytics-powered clienteling apps help store sales managers pull up customer information such as past purchases and preferences, frequently browsed styles, products and product attributes, and product recommendations based on behavioural segmentation to make engagement relevant and powerful. Store managers can now tailor their store assortment, replenish fast-moving inventory, reallocate store staff, and adjust store visual merchandising, among others, in collaboration with their respective HQ staff using an analytics-powered store mobile workbench.

How is IoT making an entrance in fashion retailing?

IoT in fashion is quite nascent with the majority of usage in the sporting and specialty fashion segments. With apparel becoming an extension of the digital self, its role in managing information related to fitness, entertainment and personal productivity is a major area of growth. Other areas like outdoor sports and specialty apparel are seeing a rapid growth in IoT applications that provide environmental information such as temperature, pressure, and altitude through embedded sensors. Consumers have started responding to performance feedback and analytics from wearable fitness devices to see if that’ll help them lead an active and healthy lifestyle. By using beacons and real-time analytics, retailers can create innovative engagement campaigns to further enhance the lifestyle choices that their consumers make, boosting loyalty as a result. In the in-store retail environment, personal shopping assistants and tools like intelligent mirrors and VR technologies are helping customers with their shopping decisions.

Is predictive analytics determining the future trends of style?

Predictive analytics and big data are playing a big role in identifying the future trends in the fashion industry. Analytics solutions help in aggregating fashion trend and sales information from a wide variety of sources around the globe—from retail sites and social media to designer runway reports and blogs covering trends—and then makes it accessible in real time for menswear, womenswear, children’s apparel, accessories, and beauty. In addition to the ability to combine both internal and external data sources, users now have access to more context for their data, which ultimately leads to more insights and better decisions.