Big Data for E-Commerce Growth

Juluan Alvarado

Sr. Content Marketing Manager, Sigma

There’s a reason retailers are increasingly using big data for e-commerce — insights based on data can help identify and capitalize on trends, exceed customer expectations, optimize operations, and reduce costs. Big data offers plenty of advantages to retailers competing for market share, but making use of it isn’t without challenges. Let’s look at what you can do with big data, learn how to overcome the challenges, and check out an example of a retailer using data analytics to grow.

How E-Commerce Retailers Can Use Big Data

While there are dozens of ways to use data in e-commerce, here are eight of the most impactful.

  1. Drive sales — Identify customer buying patterns and preferences to improve your upsell and cross-sell strategies.
  2. Forecast sales — Monitoring social media and your e-commerce platform can help you identify conversations relevant to the products you sell and emerging sales trends.
  3. Understand your customers — By analyzing your customers’ interactions with your website, social media, and other platforms, you can better tailor your marketing messages and promotions to alight with buyer preferences.
  4. Personalize the shopping experience — Tracking customer behavior in real-time will provide you with the information you need to customize website content, email messages, offers, and more.
  5. Optimize logistics — When you can predict demand, it’s much easier to streamline your supply chain and logistics. For example, sales data analysis can tell you what quantities of items to purchase and where various quantities of these items need to be warehoused.
  6. Improve customer service — Your customer profiles can do more than help you personalize experiences. Your sales and customer service teams can use them to improve interactions.
  7. Optimize your pricing — Adjust pricing based on customer demand and product availability.
  8. Manage risks — Identify patterns that indicate fraud or other criminal behavior.

Challenges Facing E-Commerce Retailers Today

Retailers have been struggling with a perfect storm of internal and external challenges for a decade now, but the impact of these challenges continues to grow. Technology keeps evolving, and consumer expectations evolve along with it.

Digital disruption — The customer journey is more complex than ever before, with up to 62 touch-points across multiple, increasingly digital channels.

Changing customer expectations — Thanks to forward-thinking innovators that created new standards, customers now expect and demand a seamless and personalized experience. And they’re quick to look for alternative options if they feel their expectations are not being met.

Siloed technology infrastructure — Digital disruption has introduced dozens of new systems that are difficult to integrate, leaving many e-commerce retailers with siloed data and disjointed workflows.

Diminishing brand loyalty — Millennials and Gen Z, as a whole, prioritize experience over brand. A brand is only of value to them if it delivers polished, integrated, and novel experiences. These consumers easily jump to new brands that offer better experiences.

These challenges can all be overcome with a strong data strategy and tools that make use of the capabilities of the cloud (like Sigma!).

Techniques E-Commerce Retailers Can Use to Rise to the Top

The digital nature of e-commerce makes it ideal for mining insights from data. Your e-commerce platform itself is generating valuable data every minute that people shop your site. Here are two specific techniques you can use to rise above the competition.

1. Product affinity and market basket analysis

Product affinity and market basket analysis are data analysis techniques that e-commerce retailers use to uncover the relationships between items. For example, by analyzing the contents of customer transactions, you can better understand correlations between items. This understanding allows you to develop better upsell and cross-sell strategies and know which products to pair together in promotions. In addition, revealing the connections and correlations between product groups helps you improve your recommendation engine rules and algorithms to increase conversions.

Tips for better product affinity and market basket analysis:

  • Tap into a single source of truth — Online transactions, product information, clickstream, loyalty program, and omnichannel customer data from your e-commerce software can all be fed into a cloud data platform, like the Snowflake Data Cloud. Having a central source of truth eliminates data silos.
  • Leverage the cloud’s capabilities — Spreadsheets were not built to handle the volume and velocity of big data. Modern cloud data platforms are designed to leverage the unlimited scale and speed of the cloud so you can seamlessly store, manage, and analyze billions of live data points in real-time.
  • Use an analytics tool that provides direct, real-time access to your e-commerce platform’s data— Your merchandisers and marketers should be able to analyze and explore data at the same level as analysts, without having to know code. Your analytics tool must allow users to work in a familiar interface to independently generate insights and take action, without sacrificing security, governance, or compliance.
  • Zoom out — Looking at seasonal or year-over-year trends can provide important insights that weekly or monthly views cannot. For example, milk from non-dairy sources has been steadily rising in popularity over the last several years. As a result, sales of oat milk have been up 289% year-over-year.
  • Analyze abandoned carts — Incomplete online checkouts can reveal many areas for improvement on your e-commerce platform, including product discoverability, recommendations, payment processes, website speed, and more. In fact, it’s estimated that $260 billion worth of lost orders are recoverable through a better checkout flow and design.

2. Recommendation optimization

Recommendation engines are designed to present products and services that customers would likely be interested in based on past purchases, interests, behavior, season, or other relevant factors. This personalization is an effective way to increase conversion.

Digital product recommendations are designed to better meet the needs and desires of customers, as well as boost sales. The best time to get a customer to convert is when they are already ready and willing to spend money. A well-tuned recommendation engine can generate huge profits. For example, it’s estimated that’s recommendation engine is responsible for 35% of its revenue!

Tips for recommendation optimization:

  • Build a Customer 360 — Having a clear view into the customer journey and understanding every touchpoint across the e-commerce funnel allows you to determine where each buyer is on their path to conversion. This insight helps you develop rules for your recommendation engine that improve customer interactions by providing the best possible recommendation for their current funnel stage.
  • Create customer profiles — Build out profiles with a definitive set of demographics, behaviors, and characteristics that allow you to deliver more personalized recommendations at scale. For example, look at customers with similar ages, zip codes, behaviors, etc., and recommend their purchases to one another. The longer your relationship with the customer, the most personalized and relevant your recommendations will be.
  • Integrate third-party data sets — Fully leveraging all of your data is a great first step, but leading e-commerce retailers augment their data with third-party data sets for even more powerful data insights. External data can be used to build product association rules based on external factors that are having a direct impact on the individual shopper. Snowflake’s Data Marketplace is one resource for live, ready-to-query datasets.

Sigma Powers Big Data E-Commerce Performance

To implement the strategies and experience the benefits described above, you need a powerful yet simple-to-use tool that will allow you to quickly access insights. Three key elements set Sigma apart.

It’s built for the cloud

Sigma is purpose-built for cloud data platforms like BigQuery and Redshift and Snowflake’s Data Cloud. It’s uniquely positioned to take full advantage of their speed, security, and power.

It’s uniquely scalable

With Sigma, you can analyze billions of rows of live data down to the lowest level of detail, effortlessly grow and diversify your data sources, and seamlessly integrate dozens of data sets in just a few clicks.

It has a familiar user interface

Sigma’s interface is one most people already know and love — a spreadsheet. That means anyone in the company can go beyond high-level dashboards, ask questions and get answers from their data, and build on each other’s work without having to know code or extract data.

Blue Bottle Coffee: Big Data in Action

Blue Bottle Coffee is using big data in its e-commerce operations and seeing incredible success as a result. Lucy Dana, Blue Bottle’s Product Manager of Growth, shares that the company collects omnichannel data every day across its cafes, web store, and mobile app. “Here’s a recent example,” Dana says. “The percentage of our espresso drinks prepared with whole milk has decreased over the last two years as oat and almond milk have increased in popularity. We’ve used these trends to shape our consumer packaged goods (CPG) strategy for new products.”

Blue Bottle began working with Sigma to help democratize data across their nationwide locations. Dana adds, “My goal was to enable anyone — from our VPs to our green coffee buyers — to pull data and make informed decisions without having to be a SQL guru or ping our Business Ops team.”

Read our interview with Blue Bottle’s Product Manager of Growth to learn more about how the company is using analytics to improve business.

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