As D2C ecommerce brands gear up to make their presence felt in 2021, here’s how data analytics can change the game for them
Data analytics tools can consolidate and filter all of this data to draw out the most relevant insights to improve efficiency, profitability and productivity
By knowing what customers will want in advance, D2C brands can adjust their marketing strategies and recommendations to push those products and thus increase chances of conversion
Given that a massive 93.5% of internet users worldwide made online purchases in 2020, focusing on what exactly these users want is no longer an option. With the right customer data on hand, ecommerce brands can understand exactly what makes their customers tick and nudge them towards more purchases accordingly. Trends and preferences are changing rapidly in today’s fast-paced world and even loyal customers will move away from brands that cannot keep up – another compelling reason to invest in data analytics that drives customer-oriented marketing and improves conversions. As D2C ecommerce brands gear up to make their presence felt in 2021, here’s how data analytics can change the game for them.
Applications Of Data Analytics In D2C Ecommerce
The power of measuring results
Every micro-step a business takes, be it in customer service or product fulfilment, generates data. Clearly, there is an enormous amount of business data being generated every day – much of which is critical towards decision-making. Data analytics tools can consolidate and filter all of this data to draw out the most relevant insights to improve efficiency, profitability and productivity. Accordingly, the business can evaluate its performance and take informed next steps based on pre-determined metrics, such as profit per quarter, time to order fulfilment, number of support tickets resolved per day, cart abandonment rate and so on.
Building buyer personas
Viewing online buyers as merely a faceless demographic will not take a brand very far. Diving deep into what makes customers tick – what jobs they hold, what tastes they have, what their hopes and aspirations are – helps the product team devise a roadmap for what these customers might need and helps the marketing team communicate better with them. This is where applying data analytics to build buyer personas comes in handy. Data analytics can filter out relevant data points and identify patterns based on what customers look for when they visit the site, over a period of weeks or even months. The brand can then segment its customer base based on the buyer personas they construct from this data and share personalised content based on what will motivate each persona the most.
Powering recommendation engines
About 75% of Netflix’s viewership comes from what its recommendation engines suggest, as do 35% of purchases on Amazon. These engines use powerful machine learning algorithms and natural language processing to deliver tailored recommendations based on a user’s browsing and buying history. Recommendation engines are like the friendly neighbourhood shopkeeper who knows what his customers want and suggests other things they might like. In other words, they allow for a more personal relationship between the brand and the customer, which will encourage the customer to keep shopping.
Smarter demand forecasting
Data analytics can analyse historical sales and industry trends to make predictions about demand patterns for the coming month, quarter or year. By knowing what customers will want in advance, D2C brands can adjust their marketing strategies and recommendations to push those products and thus increase chances of conversion. Demand forecasting also helps with price optimisation – D2C brands can offer discounts and gift coupons based on how much customers are willing to pay. For instance, one of India’s largest consumer durable brands experienced increased demand during the pandemic for specific products such as dishwashers, washing machines, and microwaves. Data insights helped them with better forecasting and supply chain management.
Better inventory management
Not all D2C brands may have large warehouses at their disposal to keep inventories in. Moreover, many products may spoil or expire if kept in storage for too long. Data analytics can identify purchase patterns that will help the brand keep enough stocks on hand to meet demand. Analytics can also help predict spikes or drops in demand, such as during festive season or a calamity like a pandemic. This will guard against stocks running out or going to waste.
Better customer service
For D2C ecommerce brands, in particular, offering excellent customer service is what will set them apart from the competition and encourage customers to visit their website rather than shop from an aggregator platform. Based on data analytics, D2C brands can identify any pain points in the customer journey and address those rapidly. It can also pick up cues on what different buyer personas are struggling with and help the support team address those struggles with a more personal touch.
How new-age brands are leveraging data analytics to power D2C ecommerce
Recognising the key role of data in an increasingly digital world, ecommerce enablers have incorporated data analytics into their AI-powered platform. The growth platform of these enablers, help clients benefit from granular data insights on customer behaviour and purchase/browsing patterns that drive new strategies to respond best to those patterns. These platforms also assist clients with segmented treatment for different customer cohorts and a dashboard that manages over 60 online shopping parameters. This enables smarter inventory management, more cost savings, better on-time fulfilment, and ultimately higher conversion rates from satisfied customers.