How a Food Industry Player Benefitted from Infusing Big Data Insights into their Decision Making Process

What You’ll Find in this Case Study:

• Impact of Big Data in Food Industry
• About the Client
• Big Data in Food Industry Business Challenge
• Why Quantzig?
• Big Data Solutions Offered and Value Delivered
• Big Data in the Food Industry
• What are the 4 Vs of big data?

Impact of Big Data Analytics in the Food & Beverage Industry

Big data has revolutionized businesses globally. The food industry, in particular, can benefit greatly from big data. Food industry companies including manufacturers, retailers, and food chains can leverage big data analytics to their benefit. Consumers across the globe are all the time more aware, engaged, and mobilized around the sustainability challenges our world faces. But the food industry companies are pressurized to adapt to evolving consumer expectations and increasing demand for transparency. But how can food industry companies level up their tactics and strategies on an ongoing basis to meet customer expectations, bolster their bottom lines, and make the world a better place? Well, thats where we see big data analytics and data mining techniques playing a major role now and in the future.

Our recent survey suggests that food industry companies generate sheer volumes of structured and unstructured data. Though they have pockets of localized analytics capabilities, less than half of them believe their analytic capability to be a differentiator. Whats more, several leading food industry companies continue to struggle with fundamental issues related to analytics. However, it is to be noted that analytics capabilities are deployed frequently to generate hindsight descriptions of what happened rather than leveraging it to obtain progressive insights that can be used to make operative, managerial and crucial business decisions.

Food industry companies need to sharpen their analytic skills and familiarize themselves with the complexities associated with the distribution networks and huge volumes of structured and unstructured data.

Are you someone who is a little skeptical about the potential of big data analytics? Then the power of Quantzigs big data analytics is just the story you should know about request a free proposal now.

About the Client

The client is a major food industry player, with headquarters in the US.

How long does it take to become an analytics-driven company, and does the journey ever end? There is not an ideal big data journey that food industry companies make but there are common guideposts along the way. Get in Touch for more Info.

Big Data in Food Industry Business Challenge

As one of the worlds leading food retailers, the client spent decades extracting insights from customer buying patterns and successfully incorporated them into their upstream business operations. However, a recent improvement in their customer loyalty program resulted in the generation of huge volumes of structured datasets. Owing to the influx of huge datasets, the food industry client soon realized that big data analytics was the only required capability to compete effectively in todays competitive marketplace. Hence the client approached Quantzig to leverage its expertise in big data analytics to extract customer purchasing insights and apply them toward the redesign of their internal business operations.

Top challenges faced by the client included:

Problem Statement 1

The food industry player was finding it extremely challenging to assess the huge data volumes generated through their online platforms to examine hidden data patterns, customer correlations, and extract meaningful insights.

Problem Statement 2

The client was unable to gain actionable insights into the metrics that would help them improve customer satisfaction and reduce customer churn rates.

Problem Statement 3

The client wanted to offer personalized services and develop a personalized offering to suit the needs of their customers by leveraging big data analytics.

Problem Statement 4

The client lacked the ability and skill required to draw deeper consumer insights from big data to make fact-based, quicker business decisions.

Problem Statement 5

The client faced major challenges in launching an analytics-driven roadmap and migration path for enterprise-wide implementation.

At Quantzig, we believe such challenges can be addressed if food industry companies invest and dedicate more resources towards the development of an enterprise-wide analytics strategy and underpin it with an operating model designed to harness the power of ‘Big Data.

Why Quantzig?

Quantzig is a visionary in the field of big data analytics capabilities and continues to set the bar for the most advanced and creative data management approaches. Our analytic solutions have been proven to deliver insight-driven outcomes at scale, to help businesses improve their overall performance. The extensive analytic capabilities of our big data experts range from data modeling, forecasting, development of visual dashboards to sophisticated statistical analysis and the development of detailed reports.

We have a dedicated team of 500+ professionals with profound functional and analytic experience to develop innovative consulting and outsourcing services for our clients across sectors. With decades of experience working with the worlds most successful business establishments, we help clients effectively manage business complexity and transform global operating models to effectively serve developing and mature market segments and drive growth through the evolving market trends.

Competing on big data and building an enterprise analytics capability is no easy task, but within the food industry, it is quickly becoming a necessity. To know why request a FREE Proposal.

Big Data Solutions Offered and Value Delivered

In the food industry, there is clearly a growing opportunity to use deeper, more comprehensive big data analytics solutions to improve performance by addressing different issues, including the ones stated above. To help the client overcome such challenges our big data experts adopted an issue-to-outcome approach. This approached focused more on linking big data analytics directly to the decision-making process to deliver the value for improved business performance.

To achieve business outcomes, a big data analytics operating model was developed to meet the clients core requirements. The big data analytics engagement was basically divided into three phases:

Phase 1

To embed the ‘analytics first philosophy into the clients business processes, we started the engagement with a detailed analysis of their business issues and then moved on to defining the most relevant big data and data mining techniques that would help them re-engineer their decisions and use the resulting insights to leverage decision-making.

Phase 2

In the second phase, Quantzigs analytics experts spent several months researching and providing the industry knowledge to build a solid foundation in the form of an operating model. This not only generated insights aligned to their business strategies and objectives but also proved to be beneficial in better identifying food industry trends and consumer buying patterns.

Phase 3

The final phase revolved around training the client to leverage and incorporate the insights into day-to-day business decisions and defining new processes that encouraged and rewarded use of big data analytics across the organization as a whole.

Instead of bolting analytics onto the food industry clients existing functions, we advocated the adoption of a cross-functional approach to infusing big data analytics into the day-to-day decision-making process. This approach not only generated a greater return on analytics capabilities but also helped institutionalize the way the client used big data in everyday decisions. Additionally, our big data analytics solution helped the food industry client to proactively track customer usage patterns across their online platforms, improve customer experience, and develop innovate product offerings. As a result, the client successfully captured millions in operational cost savings, in addition to revenue and the profits gained from the improvements in their analytic capabilities.

Big Data in the Food Industry

In the food industry, there is clearly an emerging opportunity to leverage big data and use deeper, more comprehensive analytic insights to improve performance by addressing different issues. To cost-effectively and efficiently extract actionable insights from big data, food industry companies will need to address some basic organizational issues, including:

Supply chain optimization: The ongoing pressure to reduce costs will ultimately leverage the need to enhance service levels and improve decision making throughout the global supply chain network.

Understanding the global consumer base: Intense competition for customer loyalty indirectly hints at the growing need for food industry companies to develop the ability to draw deeper consumer-centric insights from big data and in order to enhance their decision-making capability.

Improving sales margins and marketing tactics: Big data helps track the purchasing decisions of customers, directly impacting the sales margin of food industry companies.

What are the 4 Vs of big data?

The consensus of todays world is that there are specific factors that define big data, they are generally referred to as the 4 Vs- volume, variety, velocity, and veracity. Big data refers to huge volumes of structured and unstructured datasets that inundate a business on a daily basis. To tackle such huge data volumes food industry companies are now leveraging big data analytics and are building data warehouses to secure their data.

Leading players in the food industry have huge data volumes in their possession that require a detailed analysis of decision making. Inability to manage this data may hinder the companys decision-making and eventually reduce efficiency and lead to compliance issues. However, with the right big data analytics strategy and by leveraging the 4Vs, food industry companies can achieve their business objectives. The 4 Vs are discussed below:

Volume: The main factor that defines big data is the sheer volume. It makes no sense to focus on minimum storage units because the total amount of information is expected to grow exponentially every year. Such huge data volumes can be filtered using big data analytics and data mining techniques to extract important metrics that are useful for the business.

Variety: The availability of data from different customer touchpoints increases complexity and creates several challenges for food industry companies looking at assessing them. However, the efficient management of such huge data volumes requires segregation of data based on their source. Segregation of data obtained from varied sources will help food industry companies to make the necessary changes according to customer behavior.

Velocity: Velocity refers to the speed and the ability of an organization to analyze and utilize data. Processing the data using analytic tools can help answer the queries through reports and visual dashboards. By leveraging these results, a company can make suitable decisions that increase the efficiency and achieve customer-relation objectives.

Veracity: Veracity refers to the uncertainty of datasets which makes it difficult for food industry companies to act quickly in accordance with the changing food industry trends. To make the right business decisions, organizations will have to develop a robust strategy to use the available data.