Retail Insight: How Home Improvement Retailers Benefit from Big Data and Enter New Domains

With the help of Big Data, retailers can not only drive revenue but also enter new domains. In this post, we’ll discuss what new income sources become available to retailers when they harness the full potential of the data they possess.
6 min read
By Alexander Myskiw
Global Innovation Manager
Retail Insight: How Home Improvement Retailers Benefit from Big Data and Enter New Domains

Home improvement chains, like Leroy Merlin and Home Depot, have recently adopted an omnichannel approach to their customer satisfaction, making Big Data the name of the game. But now that they have access to this data, what can they do to harness and leverage it to better drive insights and outcomes? These new data troves have revealed new consumer segments, increasing profitability for these companies.

However, many retailers are also hobbled by sales funnel management issues, including repeat services provision, large accounts control, employees, and partners empowerment. They never had to pay that much attention to their customers, but in today’s competitive economies knowing your customer is a necessity.

In this article, we’ll address the role of effective data management and its benefits, especially when it comes to scaling up to new sales channels and discovering new revenue sources.

Effective Management of Large Accounts

Leroy Merlin had a problem: their in-store sales conversion rate was high, but the purchase values were small. Then, the company innovated and turned to Big Data to expand its offerings to customers. 

The company realized that by providing both materials and labor they could attract the luxury market. Leroy Merlin now offers renovation-as-a-service, partnering with trusted contractors to provide full-service renovations. While the chain normally appeals to DIY-ers, this new service allows them to reach a broader network, one that normally would not shop at home improvement stores. 

Once they connected themselves to the industry’s service side, the average payments began to grow as the Leroy Merlin team started servicing larger accounts, therefore, it succeeded in increasing the size of the sales receipt. 

However, a new problem emerged: the sales team was struggling to manage its funnel with their current technologies. Bigger accounts required more attention, client relationship improvements, repeat services, and decent IRP solutions. Without those tools, Leroy Merlin was losing customers. 

So the company innovated again, partnering with a powerful CRM that manages the sales journey of the customers and improves communication with salespeople. As a result, the company saw productivity increase fivefold and the conversion rate rise from 30% to 65% within a nine-week sales cycle. 

Turning IRPs and Legacy Systems into Streamlined API Solutions

IRPs and legacy systems cannot manage big accounts because they do not provide a full picture of the sales funnel’s existing bottlenecks. Especially, in a customer-oriented world, IRPs do not track pitfalls in the customer’s journey, making them more likely to stop doing business with the company after a bad experience. 

For this reason, it is essential that companies integrate their APIs and provide deep analytics capabilities. Meaningful data collection leads to personalized, timely, and tangible interactions with consumers throughout the purchasing process. 

Access to Big Data also improves the lives of employees, especially salespeople and partners. Rapid data analysis allows for precise decision making, which, in turn, makes it easier to work with customers and provide them with the information they need. In other words, Big Data and an API integration empower all involved parties. 

Giving a Customer Database More Power

Even when salespeople are looking for new sales opportunities, it is often impossible to launch new projects quickly and lose a chance for better conversion rates. This was a case of Home Depot, whose software did not let the company create innovative products for their clients fast. After providing salespeople with a user-friendly UI/UX, the company managed to automate many of its sales processes and respond to customers faster and make smarter decisions regarding their pipeline, speeding up project creation and increasing client satisfaction rates.

When retailers consider scaling up or entering a new market, they often grow their customer database as a result. All of that makes API integration a no-brainer to maintain business efficiency and data efficacy.

How did that work for Home Depot? Today, the company collects as much information about a visitor as possible and targets them based on their unique needs. An automatic localization feature captures the IP address of website visitors and personalizes their homepage experience.

The company also teamed up with Pinterest to glimpse into the early stages of the buyer’s journey and funnel them into Home Depot stores. For personal recommendations, Home Depot uses software from Certona Corp that processes suggestions based on browsing behavioral patterns and purchase history. 

Mapping up a Successful Customer Journey

Big Data is key to predicting and planning a successful customer journey. In-store software helps retailers cut a unified view of consumer behavior. Strong analytics capabilities give significant retail insights, such as audience segmentation, behavioral attributes, machine-learning data, and churn probability.

Big Data analysis can also identify potential partners or contractors based on buying patterns, supplying clients with the best possible workers. It can also analyze first-party sources (CRM), second-and-third party data (vendors, sales partners), online and offline purchases, and social media activity to ensure the best possible customer experience—all in real-time. 

Expanding to Other Retail Industries

How can this practice be incorporated in other retail industries? Big Data works wonders when it comes to learning customer behavior. For example, grocery retailers can use Big Data to personalize client subscriptions on certain products based on previous buying patterns.

Let’s assume that, through Big Data, a grocery store chain found that customers within a certain urban area prefer shipping speed over all other factors. At the same time, their rural counterparts want free shipping. To address this, a retailer can use multiple API integrations to adjust optimization goals for these targeted profiles, providing both urban and rural clients with a shipping option that meets their needs. Additionally, when there is a need to prioritize multiple objectives at once, integrated API solutions let retailers complete a broad spectrum of customer orders while prioritizing the fulfillment of its objectives according to importance.

Increasing the Bottom Line

The unprecedented times we are living in demand that businesses have a clear direction. It is not enough to rely on gut instincts—companies must put data first as they make their decisions. For example, intelligent sales data analysis can boost sales by optimizing pricing, refining messaging, switching or increasing marketing channels, and creating strong marketing content.

The future of Big Data revolves around advanced predictive analytics and AI. As the data industry continues to grow, those taking advantage of the latest systems will lead their niche and those struggling will be left behind.


Retailers—regardless of their industry or experience—can always capitalize on learning about a customer’s journey. Big Data and API solutions can be implemented at any level of operational work. At every single step, they provide major retail insights that level up business models and enhance strategies.

If you are looking for how to adapt innovative solutions to your retail business and increase its profitability, do not forget that it is hardly possible with cumbersome, old-school data management processes. In this case, consulting with a company like DataArt, a global software engineering firm, may be a good place to start.

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