FROM DATA TO DOLLARS: HOW E-COMMERCE ANALYTICS CAN DRIVE REVENUE GROWTH FOR YOUR BUSINESS

Over the last few years, e-commerce has become an inseparable part of global retail. In this fast-moving digital world, the online marketplace and its horizons are expanding rapidly with growing e-commerce technology and an increase in the number of internet users. According to reports from Statista, retail e-commerce sales in 2022 were estimated to exceed $5.7 trillion worldwide, and this figure is expected to proliferate in the coming years.

As Claire Hennah, Global VP of E-Commerce at Unilever says, “E-Commerce is no longer a channel for the future. The future is here.”

Capitalizing on e-commerce technology involves using machine learning and data science technology to optimize the online marketing and sales funnel. In this article, we will look at how data analysis impacts e-commerce, its applications, and some use case examples.

WHAT IS E-COMMERCE ANALYTICS

E-commerce analytics involves collecting, analyzing, and utilizing data to gauge the business impact of an online store. It enables business owners to comprehend and assess customer behavior, sales performance, online shopping patterns, and return on investment (ROI). E-commerce analytics is critical to uncovering and interpreting data, that helps make informed, data-driven decisions that can improve operations and boost sales. By centralizing, managing, and using the information in the right way, companies can intuitively and intelligently define better marketing campaigns and track campaign performance, identify and solve problems in real-time, design better product packages to gain customer attention and focus on issues that impact business operations and profitability.

WHY DO ANALYTICS MATTER FOR YOUR E-COMMERCE BUSINESS

The importance of data analytics in e-commerce is evident from the fact that it helps in all business areas that affect the operations and profitability of the business. An e-commerce business gains answers to the following questions when data analytics is put to best use:

• Who is the target buyer, and what is the buyer’s persona
• What is the buyer behavior during the searching and purchasing process
• Which are the most and least bought products
• How is the product performance varying from season to season
• Which price bracket is most likely to attract engagement
• What is the average time spent by a potential buyer on the e-commerce store
• Which payment method is most used by buyers

Data analytics gives answers to all these and much more information that provides valuable insights to make fact-driven decisions and optimize the online funnel.

APPLICATIONS OF E-COMMERCE ANALYTICS

Now that we have seen the importance of data analytics in the e-commerce business, let us take a look at the different avenues where analysis has a high potential to help in scaling up the e-commerce business.

1.Optimizing E-commerce Funnel:

A marketing funnel is a concept that represents the various stages of the customer journey. The E-Commerce funnel typically has four stages –

• Awareness
• Consideration
• Conversion
• Loyalty

Let us look at how E-Commerce analytics impacts each stage and helps businesses in maximizing reach, improve their customer experience, and increase revenue.

2. Brand Awareness Analysis

Brand awareness measures the level of recognition and association of a brand with a particular product or product category by the target audience. Brand awareness is the first stage in the marketing funnel and companies spend millions just to make their potential customers aware of their brand. It is important because it creates a product perception, fosters trust, creates an association, and builds brand equity.

To optimize the funnel at this stage, e-commerce analytics provides an impactful answer to the following questions:

• What is the extent of your new brand awareness?
• How are new users interacting with your brand?
• What channels drive this awareness
• How effective is each channel

Brand Awareness Analysis focuses on the following KPIs majorly:

• Impressions and CTR (Click-Through Rate) by channels
• Number of new visitors/users
• Source of user/viewer acquisition
• Time on site and page views by unaware and aware users
• Performance of channels such as Google, Facebook, Instagram, YouTube, Email campaigns, etc., in acquiring and engaging new, unaware users/viewers
• Macro and Micro conversion

The following metrics help gauge the success of brand awareness campaigns:

• Click Through Rate (CTR) – refers to how many people clicked on a link, an ad, an email, or a social media post
• Cost per Lead (CPL) – How many new leads an online campaign generates based on the campaign cost
• Cost per Click (CPL) – relates to SEM and determines how much you pay per ad compared to the traffic generated through the ad.
• Cost per Acquisition (CPA) – refers to how many customers are acquired based on total campaign investment. This helps in determining which channel works best.
• Conducting e-commerce analysis for brand awareness helps in investing in the right channels, reducing the cost of new user acquisition, and engaging users in the early purchase journey.

3.?Conversion Analysis

A conversion e-commerce analysis is a process of analyzing website or app traffic data for specific desirable actions taken by users. It focuses on the middle and bottom sections of the funnel and gives an idea about the behavior that leads to conversion

This behavior may include actions like

• Supplying contact information
• Creating an account
• Signing up for a subscription
• Making a purchase

This analysis answers a few important questions regarding the purchasing behavior in the online funnel:

How various channels are performing in the engagement of the customer

• What is the user demographic
• What is the user persona that engages with our brand
• What behaviors lead to conversion
• How effectively is our marketing strategy engaging this behavior
• Which products/services/subscriptions are the most popular
• What segments of our target customers are driving the highest conversions
• What channels drive the highest conversion

The following metrics help in determining how an e-commerce company is performing with conversions:

• Transactions: Is the total number of transactions per day, week, or month increasing?
• Sales Volume: Are the daily sales volume and revenue increasing?
• Sales conversion rate: Is the ratio between visitors and purchasers increasing?
• Average Order Value: What is the average value of each order, based on total sales and number of orders
• Cart Abandonment rate: How many people added products to the cart but didn’t purchase them?

Conversion analysis helps you understand if your content and website optimization strategies are working. The trends and patterns in data reveal optimization opportunities. They can pinpoint where users are dropping off so you can focus on the issue and also help you know where users are getting engaged the most so you can double up the efforts in the right direction.

4.?Loyalty Analysis

The marketing funnel doesn’t stop at conversions. Creating loyal members and turning customers into brand evangelists should be of utmost priority. Conducting a loyalty analysis answers the following questions:

• How good are you at retaining customers?
• What is the average purchase frequency of your customers?
• What are the hurdles in customer retention?
• How can you improve customer experience?
• How many customers are you ‘at risk’ of losing?
• How many customers have engaged in social advocacy?
• How many customers joined the loyalty and rewards program?

The following metrics are important to be tracked in Loyalty Analysis:

• Customer Lifetime Value (LTV)
• Churn rate
• Retention rate
• Repeat purchase rate
• Product reviews
• Customer Satisfaction Score (CSAT)
• Net Promoter Score (NPS)

A high repeat purchase rate means the company is doing a fantastic job at creating loyal customers. Tools like CSAT and NPS help in gauging customer satisfaction.

The E-commerce funnel helps in defining why and how customers interact with the website or app. Starting from building awareness, you need to travel down the funnel from conversion to creating brand evangelists. This is not easy as it requires a deep understanding of channels, factors, and tactics that work at each stage of the funnel. To improve user experience throughout the funnel, companies need to keep a repository of data and constantly refine their e-commerce data analysis.

1. Product Recommendation Engine:

Product recommendations are nothing but an automated form of a ‘friendly shopkeeper’ who knows which food items you bought, which laundry detergent you purchased, and what kind of clothes you were looking for, on your last visit to the store. He knows the shopping budget and makes recommendations, and you buy what the shopkeeper recommends. Product recommendation engines use machine learning algorithms to suggest products that the customer may be interested in and improve the engagement and experience of the customer. The recommendations can happen through channels like websites, apps, email campaigns, or online ads. There are largely two types of engines:

a. Personalized Recommendation Engine – Use the following customer data to recommend the best offers and promotions:

• Purchase history
• Social media activity
• Online behavior
• Recent activity

These engines use deep learning algorithms and Natural Language Processing (NLP) to make the customer experience as personalized as possible. They can greatly boost e-commerce sales by:

• Increasing the average purchase value of customer
• Improving conversion rates
• Reducing bounce rates and cart abandonments

b. Impersonalized Recommendation Engines – Sending out recommendations through emails or bulk newsletters can be useful even when customer data is not utilized. They can be used to recommend:

• New products launched
• Trending products
• Slow-moving products

Here’s an example of how the Amazon Recommendation Engine looks:

Image source: Amazon

Recommendation engines can be used as a targeted marketing tool to increase order value, to up-sell and cross-sell by providing product suggestions to customers and to increase product awareness and customer engagement.

2. Market Basket Analysis:

Market Basket Analysis is a method that determines the level of correlation between a pair or a group of products that are commonly purchased together. In simpler words, it is based on the concept that if a customer buys a certain product, they are likely to buy a related product along with it. Market Basket Analysis uses deep and machine learning algorithms and helps in up-selling and cross-selling products. The most common example of Market Basket Analysis is when you see ‘frequently bought together’ product recommendations on e-commerce websites or apps.

Image source: Amazon

3.?Price Optimization:

Around 85% of people say that the most important factor of online shopping is competitive pricing. Data science and machine learning algorithms can help in determining the optimal price of the product using the following:

Segmentation of Customers and Products

This is the first step in price optimization where similar products and customers are grouped together. Classified and clustered algorithms help in defining customer and product segmentation.

Regression Modelling

Once the customer and product segments are defined, regression models on various data points around conversion rates, sales, product attributes, seasonality, marketing channels, etc., can help in fixing the optimal price of the product.

Dynamic Pricing

Dynamic pricing uses competitor data in machine learning algorithms for pricing to determine product prices at different times, depending on the market forces of demand and supply. This helps the product to have a hold in the market with frequent price fluctuations. The following are the types of dynamic pricing that can be arrived at using data analysis in e-commerce:

• Peak Pricing: Based on the current supply
• Segmented Dynamic Pricing: Based on Customer data
• Competitor Pricing: Based on competitor pricing data
• Time-Based Pricing: Based on dynamic data at the time of purchase and seasonality.

4.?Demand Forecasting:

Analytics techniques are utilized in demand forecasting to anticipate product demand and predict future sales. Demand forecasting helps in determining sales trends in advance and gives a competitive edge in the following way:

• Better inventory management
• Better cashflow management
• Better pricing strategy
• Better customer service

Data analytics algorithms can understand the correlation between demand and supply and then create strategies to increase sales by ensuring that the items that are ‘in demand’ are always available, leading to reduced fallouts and loss of customers’ trust.

5. Customer Feedback Analysis

The E-commerce experience is all about satisfaction and convenience. For E-commerce companies, satisfied customers are a key driver of revenue. So, customer feedback is the most important factor that can help in keeping the e-commerce business alive. Techniques like sentiment analysis can help in understanding how customers feel toward a company and help resolve the concerns and issues that customers express or experience. E-commerce companies can use tools such as natural language processing, text analysis, computational linguistics, etc., to understand the general sentiment of customers and ensure that accurate measures are taken to improve the same.

6.?Fraud Detection:

E-commerce companies are prone to fraud as customers pay using credit cards or make repeated returns of products bought. Data science helps identify suspicious customer behavior to minimize such losses. Using data analytics, e-commerce companies can identify the anomalies that occur in credit card history and financial purchases, spot credit card fraud, and freeze the account of such users. Clustering algorithms can be used to spot cluster patterns of suspicious behavior such as multiple and continuous return requests, and bulk buying of a particular product.

BENEFITS OF E-COMMERCE ANALYTICS

E-commerce analytics is now the core of the success of any e-commerce company, as everything from product determination to pricing and customer satisfaction is impacted by data. In short, E-commerce analytics offers the following benefits:

• Competitive Edge
• Better product offerings
• Data-driven campaigns with a high likelihood of success
• Better understanding of the target market
• Better customer preference understanding
• ?Act on customer data
• Understanding marketing ROI
• Evaluate trends for better forecasting
• Deliver personalized experiences at scale

E-COMMERCE ANALYTICS BEST PRACTICES

E-commerce analytics helps drive more online sales with data, and a lot has to be measured to ensure the objective is achieved. Here are some of the best practices that can guide you in your analytics journey:

• Set goals
• Use predictive data for inventory management
• Refine and polish customer persona regularly
• Focus on customer engagement as a top priority
• Back all product decisions with data
• Use the right analytical tools depending on the goals that you have set.

Want to start your E-Commerce analytics journey? PBI Analytics can help you in every stage of the journey, from setting your data objectives to using data to boost your e-commerce sales.