Beyond the Dashboard: How data really drives ecommerce

How successful businesses use data to reveal what’s happening, why, and what’s next.

By Wallace Cheung, Data Expert, The Plant

You have probably heard – more than once – that “data is the gold mine” of the 21st century. Or perhaps that becoming a “data-driven” business is the key to growth. As a data strategist, I couldn’t agree more with those claims, but I also know that they don’t amount to much on their own. Too often, I hear them from vendors who are keen to sell storage solutions for data, but less interested in explaining how to unlock its value.

Data becomes valuable when it is part of a solid data strategy. In other words, data only makes a meaningful difference when you know how to use it for your unique business.

This article is the first in a series of four exploring how that’s done: how you can use your data to generate value and compete.

At The Plant, I work with companies to define their goals and establish how data could be leveraged to meet them. Most are already familiar with dashboards that track metrics like revenue, traffic, and conversion rates. There’s no question that dashboards are great for understanding what’s happening, but they can’t explain why it happens or what will happen next.

Think of your data as a high-resolution image of your customer, hidden in plain sight. Understanding what’s happening only gives you an outline of that image. But asking why and what’s next will give you detail, color, and depth. And those questions take more than a dashboard to answer.

This article will guide you through the three fundamental levels of data maturity: the three, increasingly advanced ways of revealing that image for your business. If data is a “gold mine”, this is how to run it.

Level 1: The “What”

At the first stage of data maturity, businesses focus on descriptive data. These are the essential metrics that underpin your day-to-day understanding of the state of your ecommerce. Things like the total sales over a specific period, or figures for website traffic and conversion rates.

Tools like Google Analytics track these figures and help you understand what is happening with your ecommerce. But they don’t go deeper than that. They don’t offer insights into why certain products perform better than others, or why customers abandon their carts.

Many companies, especially in Japan, rely heavily on dashboards. They often track vanity metrics that aren’t actionable. Some track performance indicators but don’t dive into the drivers of customer behavior. As a result, they limit themselves to only a superficial understanding of who their customers are and what they want.

That creates blind spots that block potential revenue opportunities, and restrict a company’s ability to compete. The good news is that, with just a little more insight, businesses can access immediate benefits, well within reach.

Level 2: The “Why”

Dashboards provide processed data – they give you summaries of what’s going on. But to understand why trends change, businesses have to engage directly with the raw material, the granular data on customer behavior that includes clicks, page views, cart activity, and checkout attempts.

At the second level of data maturity, businesses use raw data to segment customers according to their behavior. Grouping frequent buyers, for example, or cart abandoners, or one-time shoppers. By analyzing those segments, businesses can uncover aspects of the customer journey that need to be improved. What specific friction points lead customers to abandon their carts? What differentiates the journey of a one-time shopper from that of a frequent buyer?

When a retailer of children’s products spotted lower than expected conversions in certain categories, they asked The Plant to help investigate. They suspected that issues around sizing and color preferences on some products was causing customers to abandon their carts, but they couldn’t be sure where the problem lay.

To identify the friction points, we added granular product data into the overall picture of customer journeys, including information on color preference and pricing. In some cases, that information had only been available for successful purchases. Now we included that information for abandoned carts, too.

We applied a series of data processing and transformation layers to that raw data in order to create a data mart that addressed the issue. This, in turn, fed into a business intelligence layer where stakeholders could directly filter the data and self-serve reports. For the first time, they could see what customers were doing in the moments before they chose not to make a purchase, such as looking at a particular product details page, or browsing a certain combination of product color and size.

The analysis identified a product – baby shoes – for which a specific sizing was in such high demand that it was frequently sold out. What’s more, these shoes were so desired that when customers found they were not available, they often abandoned their journeys entirely.

The solution? Ensure that that specific product was always in stock. And, as a bonus, the investigation revealed another friction point, further along the funnel: a confusing redirect in the checkout flow that also led to cart abandonments. By correcting the redirect, those transactions could be completed, to everyone’s benefit.

Level 3: The “What’s Next”

At the highest stage of data maturity, businesses use data to make predictions of what will happen next. The effectiveness of those predictions is often the key differentiator between market leaders in ecommerce, and can mean anything from anticipating customer needs and actions, to offering personalized shopping experiences in real time.

A market-leading globally franchised quick-service restaurant (QSR) approached us to create predictive models based on their wealth of customer data. With their team, we created an automated system that made product recommendations from real-time behavioral data.

Like an attentive waiter, the system could then suggest personalized dessert recommendations to customers while they were still in the restaurant. And rather than offer a static upsell, it made dynamic recommendations, to include the most popular desserts in the past seven days. These were often seasonal offers that were higher value products than the classic dessert options, such as a limited-edition grape frappe drink. The impact? A near 6x increase in conversions on those high value products.

Evolving ecommerce data

There are clear benefits to using data at every stage of the maturity model, whether you track the status of your ecommerce, diagnose friction points in the customer journey, or use prediction to deliver more attentive customer service. But those benefits are not equally weighted: at the top end, companies can develop a real edge over competitors, and win market share. The more mature a business becomes in its data use, the greater the return.

Or, to go back to the “gold mine” metaphor: you get more value out of your data, the deeper you dig.