Real-Time Analytics in Retail: The Key to unlock customer insights

retail analytics
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Retail today is in the midst of an exciting revolution in which power has shifted from the retailer to the customer. Customers today have high expectations. They anticipate that companies will meet them where they are and when they want. They respond to experiences that are timely, targeted, and tailored to their specific needs—and reject those that aren’t. But, what’s the best way for businesses to differentiate themselves today? By delivering unique, real-time customer experiences across all touchpoints—one that is based on a solid, connected business strategy driven by data and analytics insights. We believe brands that gain the ultimate analytical advantage—by unifying the analytics life cycle from data to discovery to deployment—will also gain the ultimate competitive advantage through brand preference.

Brands that build the most effective, real-time customer experiences are those that master three interrelated capabilities—through analytics and insights:

Unified Data platforms: This capability unifies a company’s customer data from online and offline channels to extract customer insights and shape the customer experience.

Proactive analytics (with ML and AI):  To act Proactively or controlling a situation by causing something to happen rather than responding to it after it has happened. These purpose-built data collection and analytics capabilities incorporate insights on customers, marketing programs, and related customer-impacting functions such as service, operations, and support.

Contextual interactions: This capability involves using real-time insights on where a customer is on a journey digitally (browsing product reviews) or physically (entering a retail outlet), drawing her into subsequent actions the brand wants her to pursue.

Delivering these capabilities will also require modernizing the underlying data infrastructure in order to make it more robust and agile. This should happen in three key areas:

  1. Data volume and variety: Both analytics and AI workloads rely on data—and lots of it. The data infrastructure should be able to accommodate massive volumes of data, as well as many different data types.
  2. Data, analytics, and AI strategy: Without a clearly defined strategy connecting each of these critical aspects—one that includes stringent data governance and lineage policies—the underlying data infrastructure may never deliver on the promise of world-class customer experience.
  3. Unified data and analytics infrastructure: Only when data and analytics are working in lockstep with one another is it possible to accelerate and scale analytics and AI across the organization.

All of this will require a fully optimized stack of technology—from hardware and storage to software and applications—all designed to handle a variety of analytics and AI workloads running seamlessly in any environment, from the edge to the cloud and on-premises.

With analytics, brands can see the world as their customers do—and shape customer experience in real-time accordingly. The reward: higher brand preference, revenue and cost improvements, and lasting competitive advantage.

But where should companies begin?

The broad adoption of real-time analytics takes a significant investment of time, effort, and resources. In fact, many companies find the idea of transforming their entire organization overwhelming. But we at Knoldus say it’s important to recognize that these efforts are most successful when they’re iterative. Transformation doesn’t have to happen all at once—it just has to happen.

Those seeking to master the real-time customer analytics life cycle can take a number of actions to increase the likelihood of their success:

  1. Start at the top
    The people, process, and technology changes required to deliver differentiated customer experiences based on real-time analytics are significant and require support and investment at the C-level.
  2. Put the customer first
    Think from the outside in, rather than the inside out, working backward from the customer problems you want to solve.
  3. Develop and prioritize use cases
    Outline a few initial applications for real-time analytics, clearly defining the business objectives for each and prioritizing them based on value and viability.
  4. Get your data in order
    Ideally, companies will have the budget and CEO-level support to create a foundational customer data asset that provides a 360-degree view of the customer. Those that don’t can prioritize those use cases that are possible given data currently available.
  5. Map out the transformation
    Determine the technology infrastructure (e.g., data platforms, decision engines, content management systems) and organizational changes necessary to deliver the desired outcomes.
  6. Consider usability from the start
    Ensure that analytics solutions and dashboards can be used by employees throughout the organization. Consider co-locating analytics developers and data scientists with real-time analytics users.
  7. Build for scale
    Make sure your infrastructure can handle the volume and speed of your real-time data aspirations.
  8. Add data, capabilities, and technologies over time
    Those who are successful in adopting real-time customer analytics embrace a culture of experimentation and continual learning and layer in new data, systems, and functionality over time. They also track the results of real-time decisions and modify rules and analytics accordingly.

While it’s important to figure out the strategy, at the end of the day, 95% of the work is in doing it—starting small and seeing results. Every time a customer engages with your business, they can tell you something new. Get a complete picture of who your customers are and what they want so you can gear your business toward helping them achieve their goals.

Just knowing what your customers’ present needs are is often not enough in today’s extremely competitive environment. You have to even predict what smart customers would appreciate as they expect more from you rather than just doing what is expected. Retail analytics helps you do just that. This infographic explains how organizations are taking it seriously?

Understanding customers and anticipating their needs at the point of interaction is going to be essential to a company’s ability to compete in the future. Learn how to deeply connect with your customers by understanding their values by booking a meeting with our analytics expert or drop us a message here or at hello@knoldus.com and we’ll help you get started.

Written by 

Ruchika Dubey is a Marketing Manager having experience of more than 4+ years. She always wants to flex her creative muscles while solving real-time business challenges. She is engrossed in delivering business value by generating marketing & promotional ideas. On a personal front, she is a shopaholic and likes to travel and explore different cultures.

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