Surbhi Juneja, director for strategy and growth, WebEngage talks to IndiaRetailing about growing retail marketing scenario in the country
New Delhi: Surbhi Juneja, director for strategy and growth, WebEngage talks to IndiaRetailing about the evolving retail marketing scenario in India. She talks about how marketers are increasingly using artificial intelligence and machine learning to boost sales at stores and on online channels. She stressed on the need for the unification of data that, otherwise, existed in silos of brick-and-mortar stores and online marketplaces to effectively drive up sales for the retailers. She said the lack of data unification in offline and online channels generally leads to poor customer experiences.
How retailers are increasingly using digital marketing as a tool and how it is evolving for retailers?
A gradual shift was observed globally, but the fast-paced acceleration came mostly during the post-pandemic era. This quantum jump in digital marketing has led to a difference in customer lifetime value (CLV) when a consumer is engaging only on one channel (online/offline) vs. when a consumer is engaging on both online and offline channels. It is observed that customers who engage in both online and offline purchases, turning into multichannel or omnichannel customers, have a CLV that is approximately 2.5 times higher than those who only purchase through a single channel, either online or offline. This highlights the significant impact of offering an omnichannel experience on increasing CLV, the importance of which was realised during the Covid-19 pandemic and led to the quantum jump in digital marketing in the present-day marketplace for both online and offline retailers.
Is it prompting more and more retailers to adopt omnichannel or multichannel modes?
The increasing shift to omnichannel was observed as Covid-19 led to the emergence of two distinct types of brands. Firstly, there were offline brands that realized the importance of going digital for survival during the pandemic. They adapted quickly to the digital landscape after realizing its significance. Secondly, some brands were born during Covid-19 and initially focused on digital marketplaces, especially direct-to-consumer (D2C) channels. However, as the pandemic subsided, they started exploring the offline world.
We at WebEngage have seen a shift in its clientele. Brands that were traditionally offline are now seeking our assistance to navigate the digital and offline realms simultaneously. These brands are adapting to the evolving consumer journey, acknowledging that it is no longer a linear path to purchase.
With this ongoing shift, what are some of WebEngage’s marketing strategies used in the retail space and the solutions that you provide to retailers?
In the world of retail marketing, as many brands adopt the omnichannel approach, one of the most effective marketing strategies used by us is data unification. Instead of treating users holistically, the brands often operate in separate silos for online and offline customers, leading to missed opportunities and fragmented user experiences. To address this, brands need to acknowledge that users exist in both worlds – online and offline – and work towards integrating their strategies. Combining data from websites, apps, and offline sources opens up numerous possibilities for campaigns and use cases.
This lack of data integration also results in disjointed user experiences. For instance, a customer adds an item to their online cart and receives follow-up emails and messages for cart abandonment, the brand being unaware that the same evening they made an offline purchase. To address these challenges, WebEngage focuses on understanding how brands view and manage their user data organizationally, accordingly, data unification is carried out to ensure no errors and duplication is there.
How does data unification simplify the marketing process?
Today’s users blend online and offline interactions when making purchasing decisions, emphasizing the importance of unifying data for a seamless and effective marketing strategy in the retail sector. For example, if a customer buys a t-shirt online for Rs 1,500, the brand may only consider them a Rs 1,500 user. However, if the same customer later shops offline, which creates the possibility of purchasing not just for themselves but also for their family, their customer lifetime value becomes Rs 5,000 offline. However, when these brands shift to omnichannel, their cumulative customer lifetime value (CLV) is much higher at Rs 6,500 combining both online and offline, and not just a separate Rs 1,500 online or Rs 5,000 offline.
Another prime example is implementing powerful loyalty programs, which are a hallmark of the retail industry. For instance, if a brand generates a significant amount of its business through its loyalty program and starts its digitization journey relatively late, its offline operations remain substantial. By integrating and sharing data from these offline loyalty programs, we can effectively engage users. For example, users close to earning attractive rewards (e.g., 900 out of 1000 points) can be encouraged to earn the remaining points online rather than visiting a distant store. This kind of data unification approach leverages loyalty programs as a powerful tool for customer engagement and incentive-driven marketing.
It’s essential for brands to establish a presence across both online and offline channels to cater to omnichannel users. Users prioritize their overall experience and value from purchases, regardless of the channel they use. Many retail brands struggle with bridging the gap between online and offline activities due to outdated offline systems that don’t capture user data at the point of sale (POS), making data unification essential for a brand’s marketing strategy.
How the usage of hyper-personalization and AI/ML tools help retail brands?
Personalization and the integration of AI and machine learning are vital tools for enhancing customer engagement and retention. Personalization involves tailoring interactions based on individual customer preferences and behaviours, such as past purchases and shared items with friends. One way of conducting this is by recommending products they’ve shown interest in or providing exclusive promotions to loyal customers. It extends beyond email and SMS to include personalizing the website experience. This enables the creation of highly contextual and sharply personalized offerings, not only for products but also for promotions. For instance, an effective approach to cart abandonment strategies for brands would be automating content that is specific to the products left in the cart, rather than simply sending generic emails. This automation eliminates the need for manual content selection for each email. By providing customers with tailored content related to the items in their cart, brands can offer additional incentives for completing the purchase. This approach aims to create a sense of urgency and motivation in customers, encouraging them to finalize their purchases quickly.
Additionally, AI and ML are used to optimize marketing efforts, ensuring that customers see relevant content and offers, such as displaying the specific product from an ad they clicked on. The AI tools used by WebEngage are not limited by geography and can serve clients across various countries. We have been serving a large FMCG company in many international locations, showcasing the tool’s geographic agnosticism. The tool provides insights into the best timing for communication, considering users’ behaviour. For instance, if someone reads messages in the morning and another person reads them before bedtime, the tool can optimize the timing of communications accordingly. It also helps in selecting the most appropriate communication channels based on user preferences, aiming to optimize costs and ROI. Additionally, the tools support A/B testing, segmentation, and personalized recommendations, increasing the likelihood of conversion. The focus is on empowering marketers with data-driven insights to enhance conversions and ROI continually.
Is there any interesting case study or example of a brand campaign that you have worked on and would like to share?
We have conducted an interesting campaign for one of our clients, Beco, by helping them take personalization to the next level by incorporating AI-driven features. So, when a customer abandons their cart on Beco’s website, WebEngage triggers a personalized video message from popular celebrity Diya Mirza, encouraging the customer to complete their purchase, often including a discount offer.
This strategy has proven effective in re-engaging customers who would have otherwise abandoned their carts. This level of personalization goes beyond using just first names or last names; it takes into account various factors such as product preferences and even the colour of the items in the cart, all of which are captured and analyzed through AI algorithms.
Consequently, the tool’s impact is not solely measured by the direct revenue generated but also by its influence on purchase decisions across various channels. The aim is to actively engage with users to ensure prolonged website and app interaction, as increased user engagement correlates with higher purchase likelihood.
Our journey through different industries, particularly in the D2C and enterprise sectors, showcases our commitment to helping clients embrace digital transformation and enhance customer engagement. As businesses continue to evolve in an ever-changing digital landscape, we aim to be a valuable partner in driving growth and customer retention.
