Traditionally a supply chain includes a supplier followed by a manufacturer, wholesaler, distributor, marketplaces, and retailer, from whom it finally reaches out to the customers. This is followed by negotiations at each stage of change of hand, a long time in product launches, and an even longer time for customer feedback.
Better e-commerce facilities have now paved the way for D2C or direct-to-customer. They do not adhere to this traditional supply chain as this type cuts out the middlemen getting rid of entities claiming a cut of the profit margin while also establishing a better connection with the customers.
Some of the common features for the D2C brands are-
- Cutting out the middleman
- Capital flexibility allowing them to lease or rent a part of their operation
- Pricing flexibility as they have to divide the profit cuts among a lesser number of hands now.
- Direct communication with the customers
- Direct access to first-party consumer data like cookies and identifiers
- Increased use of digital platforms for marketing
- Access to harnessing 1st party data and analytics
The D2C witnessed a massive surge in numbers across the country as they allowed a brand to bypass intermediaries, thus enhancing their reach to customers faster. In the past few years, more than 800 brands have bid farewell to middlemen taking the D2C route.
COVID-19 pandemic resulted in a change in behavioral patterns of customers, making brands realize the importance of shifting towards the DTC model. Young start-ups like Mom’s Co, Bewakoof, Pee Safe, MamaEarth, and many more, have now started to challenge age-old market-dominating giants like Hindustan Lever, Lakme, etc. The D2C brand websites recorded a rise in demand by 88% in 2020 than the previous years it is said that the Indian market is estimated to reach the hundred billion mark by 2025 according to Inc42 Plus’ recently released – The Rise Of India’s D2C: Market Landscape And Trends Report, 2021
5 data solutions every d2c brand needs
- Customer segmentation on buyer persona
Of the most simple yet complex things that data can help us achieve is getting user-level behavioral attributes and device logs. AI and machine learning can now help us understand the demographic details of the prospective customers and their tastes and preferences and help in customer segmentation. Data can help us identify patterns and build buyers. We can filter out information based on what customer search time spent and so on. buyer Persona can also help us achieve targeted marketing, correct allocation of resources, and maximization of cross-selling and up-selling opportunities
2. Powerful recommendation engines
Studies suggest 75% of viewership of Netflix comes from the recommendation engines and so do almost 35% of Amazon‘s purchases. Personalization makes it easy to find out what the product can be and on the customer‘s end and it is even addictive at times. For example, on Netflix, if you are binge-watching a series and you are recommended a similar series you often tend to end up watching that too. The D2C brand needs to collect data around how customers behave and probably on what device to use and the other user-item interactions of the customer experience so that it can provide a better experience for them the next time. Netflix or Amazon even personalize your homepage based on the different things that particular customer searches for or views. In the long run, the end customer feels specially catered to, which helps in increasing brand faith.
3. Use of computer vision
Who is of computer vision is a driving force in these D2C brand management. The use of Computer Vision or Visual Similarity allows us to help identify the current trends and bestseller items of competition brands – providing a scope of opportunity for my brand to introduce similar items or highlight existing visually similar items more to the customers. This can be an interesting tool, and here at DeepFlux, we have achieved better conversion rates and more add-to-cart rates as we have implemented the concepts of visual similarity to client marketing campaigns. Computer vision is creating a user experience that is markedly easier and more efficient than the current purchase experience.
4. Demand Forecasting and Inventory Planning
Inventory management is a critical component of how a brand functions. Obviously, as a customer, I would not prefer to see sold-out styles/products. More so the D2C brand which aims at providing greater customer satisfaction without the use of middlemen must keep in mind to withhold their brand favoritism. Data analytics can study historical sales data and industry trends and make useful predictions about demand trends in advance. Analyses are also being tested now on how an accurate sales forecast can be provided. Prior forecasting of demand will help brands not only manage their inventory but also help design different pricing policies, introduce discounts or freebies to attract new and existing customers! With discounting, it is important to keep an eye on the profit margins and break-even points, while discounting, to avoid conditioning customers waiting for a sale, and thus understand exactly why and when a brand would want to discount products. Machine learning and data help us learn effective markdown optimization. Inventory management will also guide the sourcing of the new products in the right quantities.
5. Customer Interaction
One of the most important features of a D2C brand model is focusing on direct interaction with customers without the presence of a middleman. Data can help identify the pain points during the customer’s journey on the website, and with help of the buyer persona study, the support team can adhere to areas of concern. This would help in setting a brand apart from the others or the aggregator platforms! More and more customers now prefer buying through chat mediums compared to websites and apps because of a personalized store-like experience coupled with faster response times and round-the-clock customer support. Conversational e-Commerce providing a human-like experience is a brilliant tool that can identify language, sentiment, and the intent to deliver personalized and natural conversational experiences to customers.
We, at DeepFlux, work extensively with D2C brands, with machine learning models and techniques attempting to increase sales with the proper use of first-party data and external intelligence data. Though in nascent stages, implementation of data analytics as a tool shows an improvement of over 50% betterment in the overall conversion rates, reduction in bounce rate by 35%, add to carts increased by over 40% for the client companies.
You can read the previous blog here.