It’s hard to imagine that there was a time where goods were advertized to just anyone.
Or that you never knew the way your target audience behaved.
Or that sales efforts were generic without research on what your target audience actually wants.
All of this is possible now.
One word: Data.
The online retail industry is exploding at an unprecedented rate. And with such a huge volume of digital transactions, big data has become an essential part of the eCommerce industry.
Data is helping online businesses survive by turning insights into profit margins. It provides information about the buyer’s journey and boosts their sales. In this post let’s take a closer look at how data science is impacting eCommerce profitability.
Tracking Buying Journey
With brick-and-mortar stores, you never really knew how your customers ended up at your shop. Was it word of mouth? Was it the advertizement you ran on the local radio? Or was it the advertisement you ran at an event? It wasn’t feasible to ask every customer either.
Data changes things for online businesses. There are more than 2.5 billion purchasing goods online and with tracking and analytic tools you can assess the browsing activity data and get an understanding of the customer background.
The buyer’s journey provides an in-depth look at what stage of the buying process the customer is in, what kind of advertising medium works for them, what kind of products attract them, and what are their pricing preferences. Analytics tools also capture interactions, product views, and past purchases. All this data makes it possible for eCommerce to curate their marketing campaigns accordingly.
There are only so many possibilities for a customer’s buying journey. This allows eCommerce businesses to group customers based on their type of behavior. Some of the categories that data will enable you to segment your customers are:
Product Interest: What type of products are the customers interested in buying? The answer to this question helps you to develop targeted ads and personalize communication towards a group of customers.
Pricing: What is the price range that the customer is filtering? Knowing this helps you price your products more strategically and increases your chances of sales.
Order revenue: What is the average sale per customer? Knowing so, you get to know high-value customers so that you can prioritize them in your future marketing campaigns.
Mining these data points facilitates online stores to readjust their marketing strategies since they understand how interested their customers are, what they most care about, etc.
Measuring Effectiveness of Marketing and Sales campaigns
Your marketing strategy is an amalgamation of many organic and paid activities. Since you have a budget, you need to spend economically and ensure sufficient returns. You also have so many channels that you need to consider and choose the one that’s best for you.
Online marketing activities are data-driven. Data analytics shows just how much fruit your marketing efforts are bearing. For example, how much ROI you’re gaining from paid ads on Instagram versus the ROI you’re getting from paid ads on Google? Or if you should continue with your paid marketing activities or focus more on organic?
These are critical answers which determine the core of your marketing strategies. Data analytics aid online brands in critical decision-making factors, gaining omnichannel traction, and building a comprehensive marketing campaign.
Evaluating Trends for Demand Forecasting
Logistics and inventory management have the ability to make or break an eCommerce brand. Too much stocking can lead to excessive costs that are irreversible while too little stocking can lead to product deficiencies.
The top priority of brands is to achieve low costs along with high inventory accuracy. Predictive data analysis plays a major role in determining the efficiency of the entire supply chain.
It shows exactly how much inventory the brand should keep at a particular point in time and predict a rise or decline in product demand based on historical data, market conditions, customer preferences, and other internal and external factors.
Building demand forecasting models for decoding the accurate level of inventories is just one of the functions of data. You can also build corresponding models for delivery methods, customer service contracts, return volume, and products at the SKU level. For example, you can optimize shipping and delivery costs by choosing the right shipping methods such as airmail shipping, sea freight, road transportation, etc.
Gaining Customer Insights for Personalized Recommendations
Studies show that 80% of customers prefer buying from brands that provide a personalized experience. And that is only possible with data integration. There are a lot of areas where data helps in personalizing the buying process for customers. For example, it helps in customer segmentation for sending personalized emails, ai-based product recommendations on chatbots and websites, personalized ads on social media, etc.
With the right data, eCommerce brands can also raise their profit by upselling and cross-selling. You can offer discounts and offers to a specific group of customers, send cart abandonment notifications, run pop-up ad campaigns, and ensure that customers are only shown content and products that they’re interested in.
The rule of thumb or cost plus markup prices doesn’t work anymore when it comes to online shopping. Sure, the pricing needs to be profitable, but it also needs to reflect your brand goals, competitor’s pricing, and customer purchasing power.
You can see the bigger picture and monitor the prices of online brands in real time with data analytics tools. Since it also gives an idea of the customer preferences, it becomes easier to figure out what kind of pricing they seek while making an online purchase. Ultimately, you can set a price that has a higher chance of generating sales and increasing revenue.
Data is at the core of today’s online businesses. Regardless of the size and type of an online store, it can only operate efficiently if it has the right data to fuel its strategies. It helps you make intelligent decisions that ultimately improve your bottom line and enhance your revenue.