Data Analytics- A Game Changer for Your Brand!

Why Data Analytics Matters for your Brand

With the rise of the Internet, e-commerce has witnessed an unprecedented boom. Increased exposure and growing awareness has pushed customers towards experimenting with products and making more purchases online. The skin care industry in particular, has become fiercely competitive – the value of India’s direct-to-consumer (D2C) skin care industry is projected to reach $5.6 billion USD by 2025!

In today’s data-driven world, leveraging data analytics has become an integral part of a successful business model. To give customers what they want, it is essential to first know what they want! Analysing data associated with sales dashboards, consumer behaviour and overall feedback gives invaluable insights into how you can approach your branding, alter your formulations, and in the long run, boost sales.

Key performance indicators (KPIs) reflected in your sales dashboard should be analysed to help make better business decisions. These would include indicators such as average profit margin, average purchase value, retention and churn rates, customer lifetime value and customer acquisition costs.

We at Obladi Creatives, help brands like yours in creating Retention Dashboards and Advanced Customer Analytics and Our process involves

Gathering and streamlining data from sources such as your website, Amazon page and Flipkart Daily Dispatch to get crucial information, such as product sales and customer feedback.
Followed by this, we sort out inaccuracies in your data and use advanced techniques to visualise our findings.
Based on the insights generated, we craft effective dashboards and solutions that cater to the unique needs and long-term goals of your business.

Customer Segmentation and Personalization

There are numerous ways in which data analysis can be utilised, with one being customer segmentation. This is a strategy that helps you personalise your products to cater to different customers. Analysing demographics such as age, gender, location or occupation, and aligning them with consumer interests can help with improved ad targeting and increased conversions. By creating buyer personas based on your existing customer base, we can guide you towards tailoring your product range to suit their needs and preferences.

Trend analysis is of great importance in the skin care industry. Social listening, customer reviews and a systematic analysis of popular search trends can help gauge which ingredients (for e.g. hyaluronic acid or Vitamin C) or products are currently in demand (for e.g. under eye creams or sunscreens).

Interestingly enough, in the skin care industry, personalization extends beyond numerical analysis of sales data. Major players in the D2C segment of the skincare industry have employed innovative tools and resources that allow them to engage customers, along with understanding their preferences and needs.

SkinKraft’s hero page contains the link to a skin quiz that customers can fill out. Based on the information they give about their unique characteristics, skin concerns and goals, a personalised skin regime is crafted for them.

(Screenshot from www.skinkraft.com )
Nykaa made smart use of data analytics to increase its customer retention rate by a whopping 55%! It achieved the same by studying the user behaviour of consumers who became inactive on the app, and creating strategies that helped re-engage them. This involved targeted ads and SKU (stock keeping inventory) level analysis, through which they identified best selling and slow moving products, along with evaluating the effectiveness of their pricing and promotion strategies.

Repeat Purchases

On this note, let’s move on to another important factor that determines how well a brand is performing- repeat purchases.

Avid skin care users generally maintain a routine, which entails restocking products on a timely basis. Analysing behavioural data can help predict when customers would want to purchase a product again.. It can be studied by reviewing and understanding purchase history, browsing behaviour, rate of returns and channel preferences

Capitalising on this analysis can help improve retention rates, increase customer loyalty and allow brands to identify any potential hurdles in the customer journey that might be hampering or decreasing repeat purchases.

A number of skin care brands use automated Whatsapp messages or scheduled emails to give customers timely reminders and encourage repeat purchases of recently bought products along with their preferred recommendations. This could also include refill offers and attractive discounts that would appeal to the customer and prompt them to buy the product again.

Churn Analysis

Earning new customers proves to be more tedious and expensive than retaining loyal customers, which is why retention rates need to be higher for a business to continue performing well.

When customers stop purchasing products from a certain company, it reflects overall dissatisfaction, which could result from numerous factors. This unfortunate development is referred to as ‘churning’, and it is extremely essential to perform a robust churn analysis to deal with this issue effectively.

Churn analysis starts with collecting and organising customer data, which includes demographics, purchase history, social media click through rates and website interactions. Followed by this, churn rates need to be calculated, based on a fixed criteria that is tailored to the brand’s products and business model. Churn rate is the ratio between customers who churned and the total number of customers during the defined time frame.

The next step is to identify and pinpoint factors that are contributing to an increase in churn rates. These can include:

Poor customer experience (includes onboarding, purchasing, customer service etc.)
Attracting the wrong customer base
Decline in the quality of products
Issues with pricing (competitive prices, price sensitivity)
Glitches or issues in the website or purchasing portal
Weak value proposition

Once the key problem areas have been identified, all the gathered information needs to be converted into actionable insights which can help determine strategies to alleviate the issue(s). An example of an effective churn strategy is personalised emails or offers that could prompt customers to resume buying products from the brand. Even something more subtle, such as asking for feedback, could help make the customer feel valued and bring them back.

Real-World Success Stories

1. Mamaearth

Mamaearth was founded by Varun Alagh and Ghazal Alagh in 2016 as a brand that specialised in babycare products. 8 years ago, Mamaearth started out with a limited range of baby care products. Within the next few years, they leveraged a smart, dynamic and futuristic data analytics strategy to expand their product range and make their presence felt in the D2C skincare market. Let’s review some distinguishing factors of their strategy, and how these steps worked out in their favour:

Data-Driven Optimization

Mamaearth has cleverly used data analytics to fulfil three integral functions:
- Understanding customer behaviour (by tracking past purchases, returns, repurchases, product reviews etc.)
- Optimising marketing campaigns (based on consumer behaviour and emerging market trends)
- Personalising product offerings (catering to different skin types and a variety of skin concerns)

A Rapidly Expanding Product Catalogue

This is a feature that has made Mamaearth stand out in a highly competitive market. Unlike most other skincare brands that stick to a certain set of product ranges for a longer span of time, Mamaearth makes use of its data analytics to launch new products every few months.

This reflects an awareness of changing trends, which greatly appeals to new-age customers who value responsiveness and innovation in skincare. As of 2024, Mamaearth has over 300 SKUs (stock keeping units)!

2. Minimalist

Founded in 2020 by Mohit and Rahul Yadav, this brand broke the glass ceiling to emerge as one of India’s top D2C skincare brands merely in a few years. As the name suggests, Minimalist believes in providing customers “clean beauty” that is also affordable, with products that feature scientifically backed formulations that are cruelty free and fragrance free. Few of the Data-driven strategies that helped Minimalist are listed below

Enhanced Customer Engagement

Data analytics has significantly boosted customer engagement for Minimalist. Their personalised email campaigns, powered by insights into customer preferences and purchase history, have achieved open rates that surpass industry benchmarks. This level of engagement not only drives conversions but also fosters brand loyalty.

Optimised Search Performance:

Minimalist has utilised SEO and SEM analytics to transform its online presence. By targeting high-performing keywords and adjusting pay-per-click (PPC) strategies based on real-time data, the brand has seen a remarkable increase in organic search traffic and ad performance, resulting in a higher return on investment.

Informed Marketing Strategies

The brand’s careful analysis of customer search trends has allowed them to create personalised marketing campaigns that resonate with their audience. By segmenting email lists and tailoring content to meet the specific interests of different customer groups, Minimalist ensures that its messaging is relevant and impactful.

Customised Skincare Routines (Routine Recommender)

Minimalist uses data analytics to offer personalised skincare routines based on individual skin concerns. By analysing customer preferences, the brand categorises products and simplifies searches with filters like routine steps, ingredients, and price. This data-driven approach ensures customers find the best products for their needs, enhancing user experience and satisfaction.

Tools and Technologies

Certain tools can be used to improve your data analytics by enhancing customer understanding, product development, and marketing strategies. Here are some key options:

1. Customer Data Platforms (CDPs)

Tools like Segment, Tealium, and Treasure Data help unify customer data from multiple sources, enabling skincare brands to create detailed customer profiles. This consolidated view allows for better segmentation and personalised marketing efforts.

2. Business Intelligence (BI) Tools

Tableau, Power BI, and Looker are popular BI tools that can visualise complex data, track key metrics (like sales performance and customer demographics), and generate actionable insights from large datasets.
These platforms allow for real-time data analysis and dashboard creation, making it easier for brands to monitor trends and make data-driven decisions.

3. Predictive Analytics and AI Tools

Google Cloud AI Platform, IBM Watson, and DataRobot use machine learning algorithms to predict future trends, customer behaviours, and product preferences. This can be particularly useful for anticipating seasonal product demand or detecting shifts in consumer preferences.

4. Customer Relationship Management (CRM) Systems

CRMs like Salesforce, HubSpot, and Zoho CRM not only manage customer interactions but also provide data analytics capabilities. These tools can analyse customer journeys, identify high-value customers, and optimise marketing efforts.
Integration with email marketing platforms can further help in tracking campaign effectiveness.

5. Social Media Analytics Tools

Hootsuite, Sprout Social, and Brandwatch help analyse social media metrics, such as engagement rates and sentiment analysis. Skincare brands can use these insights to understand customer opinions and preferences regarding their products.
Social listening tools like Mention or Talkwalker can track brand mentions and industry trends to inform product development.

6. E-commerce Analytics Platforms

Platforms like Google Analytics, Shopify Analytics, and Kissmetrics help track customer behaviour on websites, including conversion rates, product performance, and customer acquisition sources. This data is crucial for optimising e-commerce strategies.
Additionally, tools like Hotjar and Crazy Egg provide heatmaps and user session recordings to analyse how customers interact with product pages.

By leveraging these tools, skincare brands can gain a more comprehensive view of their business, make data-driven decisions, and enhance customer experiences across various touchpoints.

Upcoming Trends

Here are some upcoming trends in data analytics that could significantly impact the skincare industry:

1. AI-Powered Personalization

Machine learning and artificial intelligence (AI) are enabling brands to deliver hyper-personalised skincare recommendations. AI can analyse customer data, such as skin type, lifestyle, and preferences, to suggest tailored products. This trend supports customised skincare routines and targeted marketing efforts.

2. Real-Time Data Processing

Real-time analytics tools are gaining traction, allowing skincare brands to track customer interactions and respond immediately. For instance, monitoring social media conversations or website behaviours in real-time can help brands quickly address emerging trends or customer concerns.

3. Predictive and Prescriptive Analytics

Beyond understanding past data, predictive analytics forecasts future trends, while prescriptive analytics provides recommendations for actions. This approach can help skincare brands anticipate customer needs, optimise inventory, and improve product development cycles.

4. Integration of Augmented Reality (AR) with Data Analytics

AR tools that allow customers to “try on” skincare products virtually (for e.g. the feature present in Nykaa) can be integrated with data analytics to gather insights about preferences. Brands can then analyse this data to refine their offerings and personalise experiences further.

These trends highlight the growing role of advanced analytics in driving personalised, ethical, and sustainable strategies in the skincare industry.

Conclusion

The use of data analytics has become essential for skincare brands in today’s competitive market. With the rapid growth of e-commerce and rising consumer awareness, data-driven insights are crucial for brands looking to stand out and meet the growing demands of the D2C skincare market. By leveraging analytics, brands can gain deeper insights into customer behaviour, optimise marketing strategies, and personalise offerings.

Analysing key performance indicators (KPIs) such as profit margins, customer retention, and acquisition costs can drive better decision-making and business growth. Techniques like customer segmentation, trend analysis, and predictive analytics allow brands to tailor their products and campaigns to individual needs.

Success stories like Mamaearth and Minimalist show how data-driven strategies can optimise marketing, improve customer engagement, and boost revenue. The integration of cutting-edge tools and emerging trends in data analytics will continue to shape the skincare industry, driving innovation and sustainable growth.