Neeraj Nathani SmartBridge Trading Solutions Pvt Ltd

(Neeraj Nathani SmartBridge Trading Solutions Pvt Ltd)

The Big Value In Big Data: Seeing Customer Buying Patterns

Is Big Data simply a popular catchphrase or the launch of a new era? Overuse doesn’t automatically transform a buzzword into a best business practice, and several factors, such as measurable results, focus and sustainability, determine whether an idea is much more than just that. The core question is this: does big data actually solve real-world business problems? The short answer is yes – and here’s a real world example of how leveraging Big Data can solve the complexity around product proliferation by helping companies align product offering and supply chain based on customer-buying patterns.

Big Data: More Than Just A Trend

According to Gartner, unstructured and structured data held by enterprises continues to grow at explosive rates. However, volume and velocity of data – what the business world is beginning to understand as the “Big Data Problem” – are becoming less of an issue than the variety of data. Each silo within the enterprise – operations, supply management, sales, marketing – faces its own data variety challenges, where bits exist in a multitude of formats and types.

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Due to the variability of data across silos, systems can’t “speak” to one another, and gaining an accurate, enterprise-wide view of demand and performance seems impossible. In fact, most business and IT managers accept the lack of intersystem collaboration as a given, an inevitable limit that must be worked around. As a result, what we know is being increasingly outpaced by the things we don’t know. Performance within individual silos is clear, but this view does little to inform effective strategic direction for the organization.

There is a better way to tackle this challenge in variety and capture the opportunity posed by Big Data.

Patterns And Connections
Traditionally business silos have individually sorted, stored and managed data that is relevant to the needs within the silo. This has resulted in enterprise-related data being disrupted into disconnected pieces across the silos. The Big Data challenge requires aggregating the data across these silos for a single view of the organization. The purpose of the aggregation is to reveal the intelligence and causality across the business functions for strategic insight. This Big Data challenge requires solutions that can harness the intelligence from the data and deliver actionable intelligence to the business user. Conventional business intelligence and data warehouse tools aren’t designed to analyze, identify and surface critical data linkages and causality. As a result, insights, contexts and market opportunities remain hidden from view because users don’t know what they don’t know.
These critical connections and causalities are the key to managing big data and allowing companies to see a more comprehensive picture of their product and product variants based on actual customer-buying patterns. The connection patterns reveal valuable information quickly, accurately and allow for faster, more relevant decision-making.
Freeing the data to reveal connections and causation through pattern-based analytics solutions will paint a bigger picture – one that can better manage product variants and streamline sales by shedding light on what customers are buying, when, where and how. Currently companies pour their non-standard data into spreadsheets that then require teams of data analysts to interpret and derive meaning from it. This is not scalable and often misses the mark. Big data demands applications that can interpret and deliver immediate actionable intelligence to business users

Leveraging Data To Address Product Proliferation

One of the major business problems companies are facing today is the complexity caused by product proliferation – one of the biggest drivers of material cost and inventory levels. This is a complex issue that isn’t easily resolved with traditional approaches, but can be addressed with a systematic and enterprise-wide pattern-based analytics approach to leveraging the data across the silos within the organization.

For high value manufacturing, product variety is the single biggest driver of cost as it dictates the material requirements and inventory, which is typically over 80 percent of the total cost. Chasing diverse customer demands with an explosion of product variants dramatically increases total cost and causes volatility across the supply chain. But what are customers really buying? Are customers really buying all that companies are offering? Is there a way to satisfy them with fewer variations? Or alternate variations that improve supply chain efficiency? This is crucial knowledge for not only satisfying the demand, but also reducing supply chain costs and increasing margins.

As product proliferation has increased, so have operational complexity and cost structure; “what-if” scenarios with alternate product portfolios that meet the majority of customer demand have been an underutilized lever and represent the next frontier of business process improvement. A poor product mix will drive complexity throughout the value chain – impacting supply chain, marketing and sales, and service and support. In the same vein, a good product mix can dramatically improve delivery costs and increase profits.

The product portfolio also impacts the sales efficiency and top line revenue. To understand the opportunity cost with sales, consider the typical OEM salesperson: Let’s say he or she has a $4 million quota and spends 10.5% of his or her time defining a customer solution, configuring and pricing it, and then tracking delivery. Reducing that time by just 1 percent for a 100-person sales force represents an opportunity cost of $42 million.

Pattern-based analytics solutions are already being leveraged by some companies, giving them a much-needed competitive advantage in a high-risk environment by providing insights into customer-buying patterns to guide the product offering, supply chain planning and execution. For example, when NCR needed to optimize product configurations across their ATM line, they implemented pattern-based analytics solutions to analyze customer-buying patterns. With insight into what’s selling where, to whom, when and how often, NCR optimized the product line, defined customer segments and then seamlessly pushed this to the sales team to help reduce lead times.

At NCR, this resulted in a dramatic improvement in sales efficiency and supply chain performance. Pattern-based analytics can reveal deep insight into what customers are buying, and leverages that to offer and guide customers to the best choices based on availability and product margin. This approach uses customer-buying patterns to create the best product offering, and simultaneously guides customers to the best choices based on what is on hand. This is a win-win as the supply chain builds what is being bought and the sales reps sell what is in stock.

Source: Wikipedia.