The power of data in retail & e-commerce: How data-driven strategies improve retail sales

Why data is crucial for retail & e-commerce

It’s no secret that data has played a large role in shaping the commerce practices of the present day.

Thanks to digital transformation, data has given businesses of all kinds a competitive advantage, allowing them to leverage consumer insights to influence decision-making and buying habits.

This is largely enabled by the increasing number of ways in which businesses can collect key data. Whether it’s evaluating sales data and customer behaviour (e.g. product performance and purchase history) or assessing inventory or marketing metrics, businesses have an array of tangible proof points that can inform their strategy and key decisions.

If you’re interested in learning how data can potentially transform your business, keep on reading as we explore how AI and machine learning can help you optimize operations and drive revenue growth.

We’ll also discuss the future of retail analytics and how you can stay ahead of the game by leaning into data-driven strategies.

Key data-driven strategies in retail & e-commerce

There are a number of avenues where data can come in handy for your business. These include:

• Personalization & customer insights: Using your customer data in online shopping can help your business create a customized shopping experience through tailored recommendations and targeted marketing.
  Amazon is a prime example of how successful this strategy can be. By looking at data like customer browsing history or wishlists, Amazon recommends products that are likely to be of relevance or interest. This, in turn, leads to higher conversion rates and increased customer loyalty.
  
• Inventory & supply chain optimization: Data and AI can also help business owners predict demand by forecasting trends and sales patterns. This, in turn, prevents stockouts, reducing operational costs and minimizing excess inventory.
  Walmart, for example, has been known to use machine learning algorithms to anticipate the popularity of products. Such insights allow them to make efficient decisions that both improve their inventory management practices and allow for a positive consumer experience (i.e. by ensuring in-demand items are readily stocked and available).
  
• Pricing & competitive intelligence: Another way to leverage market data is by using it to adjust pricing dynamically. Based on factors like real-time market value, competitor pricing and fluctuating demand, online retailers can automatically alter their prices to stay competitive in the rapidly evolving online marketplace.
  The most notable example of this business practice’s success is seen in Ticketmaster. Although the ethics of this data-driven strategy are certainly up for debate, the reality is that using predictive analytics in retail proves how data improves retail sales and overall profits.
  
• Fraud prevention & cybersecurity: Retail data analytics can also be leveraged to detect fraudulent transactions earlier and enhance overall security. By using machine learning algorithms to spot unusual shopping patterns, you can reduce chargebacks and minimize fraudulent activity that could negatively impact your business.
• Omnichannel experience: Finally, data can also help you create a seamless shopping experience across online, mobile, and physical stores.
  For instance, Starbucks’ integrated rewards system is known for using personalized insights (like your first name) to create a customized and frictionless experience both through mobile-ordering and in-store purchase. This, in turn, enhances customer convenience and brand loyalty while also optimizing operations.

The role of AI & machine learning in retail data analytics

As you’ve seen in the strategies above, the use of AI in e-commerce is increasingly becoming the norm. For this reason, we’ve seen a sharp increase in the demand for ML and AI professionals in recent years.

There are so many other ways to integrate artificial intelligence into your business, most notably with chatbots and AI-driven customer service. This strategy is not only efficient from a resources perspective (i.e. your employees can spend less time responding to customer queries), but it can also identify your most frequently asked questions, helping you spot potential pain points in your online customer experience.

AI can also be useful in image recognition for visual search and product recommendations. As we’ve discussed, predictive analytics can be great for forecasting trends and customer demand; however, it can also help your business suggest similar-looking products by identifying visual elements like product colour, material, etc.

Additionally, artificial intelligence can be leveraged in sentiment analysis to understand customer feedback and brand perception. This often looks like analyzing words in customer reviews and assigning them tags to automatically evaluate what people are saying about your product or business.

Real-world examples of data in retail & e-commerce

As we’ve discussed, many large companies are successfully using data to transform their business, including Amazon, Walmart, Starbucks, and Ticketmaster. Some other excellent examples of data-driven strategies in action can be seen in Netflix and Shopify.

While Netflix is not a traditional e-commerce retailer, its implementation of a personalized recommendation algorithm has allowed it to dominate the streaming market. By using data to suggest relevant shows and movies to its customers based on their watch history, Netflix created a tailored customer experience that directly correlated to the platform’s jump in popularity.

Additionally, Shopify leverages data in its abandoned cart recovery strategy. With the use of AI, Shopify re-engages lost customers by using tactics like automated emails that include a discount code, prompting customers to come back and complete their purchase.

Although massive corporations like Netflix and Shopify are definitely leading the charge on implementing big data and AI, this doesn’t mean small businesses cannot follow in their footsteps. Even if you’re just starting your business, there are many ways you can start leveraging data without a huge budget.

For starters, many free online data resources can provide you with useful information. If you have a Facebook or Instagram page for your business, you can leverage metrics in the Meta Business Suite to analyze consumer behaviour and engagement. Other tools like Google Analytics and Shopify Insights can also be great starting points if you’re looking to optimize cost-efficiency.

You can also try out collecting your own data! Consider sending out surveys to get real consumer feedback and make sure to add some sort of incentive for completing the survey (e.g. a small discount code or a simple perk that reels in customers).

Finally, you can also take advantage of data that is already being collected through your POS or payment platforms. If your business uses a system like Square, Shopify or Moneris, you can analyze your recent sales to see which items are selling better than others.

In terms of fraud prevention, if you’re already using a company like PayPal or Stripe, you can review your recent customer transactions to get an idea of what legitimate purchases look like (these companies often also offer built-in fraud protection).

The future of data analytics in retail

Due to advancements and technology and rapid changes in consumer behaviour, there are a variety of new trends that are actively shaping the retail industry.

One of these is augmented reality (AR) shopping, where customers can virtually try on products before purchasing them.

Warby Parker is a great example of this strategy, an eyewear company that utilizes AR shopping to let customers visualize what different kinds of glasses would actually look like on them. This strategy, in turn, often leads to reduced return rates and increased customer engagement.

Other emerging trends include using blockchain for supply chain transparency, as well as implementing voice commerce. Voice commerce refers to the ability for customers to make purchases by only using their voice, often through AI-powered devices or voice assistants like Amazon’s Alexa or Apple’s Siri.

With all of these exciting ways to increase the use of data in retail, there are still many ethical considerations that businesses must consider. As evident in the Ticketmaster example, there is certainly a blurry line between using data to effectively drive revenue and taking unfair advantage of your customers.

For example, data privacy has been a hot topic of discussion: when collecting customer data like purchase history, businesses must ensure data is being stored securely so it does not become subject to security breaches. Companies should also use customer data transparently and fairly in order to create a greater sense of brand loyalty and trust.

With this idea in mind, strict regulations have emerged in response to this need for compliance, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the U.S..

Laws like these require businesses to be transparent about how they collect and share personal information, which, in turn, gives customers more control over how their data is used.

Finally, responsible AI usage is also a major area of concern. Although artificial intelligence nowadays is incredibly sophisticated, the reality is that algorithms and programs can still make mistakes. It’s important that both large and small businesses temper their reliance on AI with employees who can fact-check or course-correct when needed.

Why businesses must embrace data

It’s clear that data has the power to transform retail and e-commerce businesses, whether through personalizing the customer experience or optimizing inventory. With the help of machine learning, you can join the likes of Amazon, Walmart and Shopify in leveraging consumer data to predict demand and prevent fraud, ultimately leading to increased revenue and improved efficiency.

It doesn’t matter whether you’re a small start-up or a six-figure business: data doesn’t discriminate! In fact, businesses of all sizes can start leveraging data today, and the best way to do that is to hire a tech professional.

If you’re looking to add an in-demand data expert to your team, consider using Lighthouse Labs’ External Talent Acquisition service to ease your search. Whether you need a coding master to build your website or a professional to establish your cybersecurity guidelines, we’ll help you find top talent that aligns with your business’ unique needs.

Want to learn more about data in retail and become an expert yourself? Enroll in our Data Analytics Bootcamp to launch your career in tech! Whether you choose our full-time 8-week bootcamp or our flexible 18-week program, rest assured you’ll come out with the in-demand skills you need.

What are you waiting for? Download our curriculum today to get an inside look at the Data Analytics Bootcamp.