Why Retailers Fail to Make the Most of Big Data

Why Retailers Fail to Make the Most of Big Data

Big Data, when harnessed effectively, can radically transform your business. Forward-thinking retailers are now using it to obtain a single view of customers, improve customer experience, seamlessly orchestrate supply chain processes, and accurately forecast sales and revenue so they can plan and act proactively.

With the digital universe doubling in size every two years, retailers can access an enormous amount of data from virtually all sources such as e-commerce transaction data, electronic point of sale (EPOS) data, in-store sensor data, and supply chain systems. Social media metrics and customer online engagement are also a rich source of real-time data for real-time insights.

But even though there is a treasure trove of data at their disposal, most retailers struggle to make the most of data. According to a recent survey, almost half of data-driven initiatives are failing in retail organizations.

Abundant Data, Unclear Objective

According to IDC, worldwide data will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025 (CAGR of 61%). But even this amount of data will become inutile if retailers do not tie it to their unique objectives. They must point Big Data to a specific business problem in order to realize its maximum benefits. In order to do so, they must be clear about their goals first.

What should be the goal?

"Big Data is just a means to a much bigger end." So figuring out what to do with data is a wrong Big Data goal that leads to wrong Big Data strategies. This approach focuses on the data (tool) alone and not on the purpose or intent, which is to gain the right insight to solve the company's unique problem and achieve the intended business outcome. If the focus is on data alone, retailers may end up buying Big Data solutions that fail to cater to their and their customers' needs.

The goal should focus on the customer. Instead of asking what to do with data, retailers should be asking how can data help them understand their customers' needs and priorities, what should be their pricing strategy, is a promotion going to increase sales, should they give promotion to a certain product, how can they improve sell-throughs, what are the most optimal locations to setup stores and what size of stores should they setup and how can they orchestrate their retail activities and environment around them.

Poor Big Data Strategy

Retailers that have more-than-enough data volume and clear goals but are unsure what to do next, end up underutilizing their data. As Dina Gerdeman puts it, "the problem is that, in many cases, big data is not used well [because] companies are better at collecting data — about their customers, about their products, about competitors — than analyzing that data and designing strategy around it."

While there is no single Big-Data-strategy-for-all, the following tips can help retailers devise a good strategy:

  • Involve the entire organization, vertically and horizontally. Determine how ready they are to gain value from Big Data.
  • Break silos by integrating diverse data sources. Ensure constant access to different types of data (i.e., transaction, interaction, and external data) and protect its quality and integrity throughout its entire lifecycle.
  • Once ready, pick the right automation tools, machine learning methods, and analytics tools to interpret data and automate decisions
  • Consider how to visualize insights for easier understanding and faster decision making.
  • Constantly evaluate and enhance Big Data assets and strategies.

Insufficient Decision Support Tools

McKinsey suggests that when designing or planning a Big Data strategy, companies should also consider selecting the right decision-support tools. There is a wide selection of tools available, but if retailers make the following technology selection mistakes, they will fail to maximize the value of Big Data:

  • Make a technology investment and look for a problem to solve, rather than identifying the problems and looking for a technology solution to solve them. Buying a data solution without specifying what problems to address can result in low or negative ROI. They have the tool but it can only solve a few problems, or worse, just a single problem that is not critical to the business. It can also lead to underutilization of data because the focus is on what the technology can do rather than how every piece of data can be harnessed more effectively and what tool to use to be able to do so.
  • Buying à la carte solutions instead of a full-suite of decision-support tools, leading to more silos and fragmented insights. To obtain a single view of customers and supply chain transactions, retailers should have a single source of truth, which separate tools cannot provide. Buying à la carte solutions runs counter to the goal of breaking data silos to enable consolidated insights.

Maximizing Big Data value is not an easy task but successfully doing so can bring retailers various competitive advantages. To make the most of Big Data, retailers must ensure that they have clear objectives, an effective strategy, and the right decision-support tools in place.

ORS GROUP has years of proven experience in helping retail companies maximize the value of Big Data by offering cross-industry solutions for optimizing and automating business processes using proprietary A.I., Machine Learning, and Big Data Analytics algorithms. Contact us to learn how we can help you with your Big Data initiatives.

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