Try imagining your day without having instant access to information. That means no scrolling through social media to start your day. Or watching the news as you’re getting ready for work. Or listening to your favorite podcasts on your way home.
Seems hard to imagine, right? And it doesn’t even include the time you spend working!
Data is all around us. It helps business leaders optimize their processes, understand market trends, and wow their customers. And its volume will keep on growing.
Predictions are that the world will generate more than 180 zettabytes of data by 2025. That’s over 180,000,000,000,000,000,000,000 bytes – enough to fill 244,472,461,682,742 CDs.
So it’s not surprising that data gave rise to a new way of doing business – one where we prize data and information above all else. Data-driven companies are businesses that value data as a strategic asset and ensure it is used when making decisions on every level.
To get the most out of this approach, market leaders must improve their organization’s data literacy and ultimately – understand what kind of technology they need to get a complete view of their business.
With a wide range of technology solutions available, it’s okay if you’re not sure where to start. Different industries and business models need different kinds of data – so they require different approaches to gathering and analyzing it.
But what are the most common use cases of data analytics across industries? And how can you benefit from data-based insights?
In this article, we’ll cover:
How Retail Analytics Benefit Both Retailers and Brands
The data revolution is disrupting every industry. And it’s having the most significant effect on the retail and CPG businesses.
With rapidly changing customer demands, massive amounts of data are being generated every day. As a result, the global retail analytics market will be valued at more than $9.5 billion by 2025.
This rise is powered by market leaders who want to know everything – from tracking how customers behave on social media to understanding their purchasing power. This data helps leaders anticipate market trends to generate value for the company.
But with so much data, legacy systems and Excel sheets are not enough. Retailers and CPG brands must eliminate data silos and introduce technology solutions that can recognize and extract actionable insights.
With the adoption of the right mindset and technology, retail analytics help market leaders improve their businesses on many levels.
Some of these include:
1. Improving Customer Centricity with Data-Driven Personalization
Delivering personalized experiences has become a standard for forward-looking retailers and CPG brands. But true personalization means much more than sending individualized emails or customizing campaigns for a specific target audience.
Customers want you to address them at the right time, with the right messaging or offer. Clearly, the key to powerful personalization is – data. When you know what your customers want or need, it’s far simpler to design an offer to match.
According to nearly two-thirds (64%) of marketers, the primary goal of having a data-driven personalization strategy is to deliver a better customer experience. Retail analytics software can now easily track the pages your customers visit, the products they buy, and the campaigns they engage with. These, combined with demographic and offline data, make an excellent foundation for real-time personalization.
With deep customer insights and predictive analytics, retailers and CPG brands can understand customer behavior and build long-term loyalty by acting on their customers’ expectations.
Beyond that, AI solutions can help organizations uncover new segments and hidden opportunities. For retailers and CPG brands, data is truly the new gold.
2. Empowering In-Store Experiences through Customer Insights and AI Solutions
Knowing your customers’ needs and purchase history allows you to customize your in-store experience so that they feel delighted and understood.
When in-store staff knows who the customer is and what they purchased in the past, they can easily recommend the right product or use this information for up- or cross-selling.
Moreover, AI-based solutions can help customers find and choose the right product, removing unnecessary friction from their shopping. For example, instead of trying to spot a specific item among hundreds of others, they can simply use the visual search feature in-store.
Skincare retailers have already introduced apps that help customers choose the right skincare products based on their photos – by analyzing the actual age of their skin. Likewise, furniture retailers use visualization apps to let customers see how a piece of furniture would fit in their home.
The possibilities are endless – and technology is here to support them.
3. Minimizing Risk with Optimized Supply Chain
There is no doubt that the last couple of years disrupted supply chains across the globe. But the proper use of retail analytics can help you keep the shelves stocked and deliver an exceptional customer experience.
The big issue here is – data silos. Retailers and CPG brands often lack end-to-end visibility due to countless systems and databases that don’t communicate. So building a data lake or a data warehouse is an obvious first step towards getting a complete view of the business – and then planning accordingly.
In a recent retail and consumer goods analytics study, 61% of retailers said that forecasting is a top area of their analytics focus. Further on, 52% of surveyed CPG brands will invest in their enterprise BI and reporting capabilities within the next 12 months.
By better understanding your customers’ demands, you can optimize product assortment and distribution to avoid empty shelves. Moreover, a complete view of the data helps make operations more efficient – helping avoid delays, duplication, and customer frustration.
Manufacturing Analytics: Use Cases and Benefits
The manufacturing industry started its transformation with the arrival of Industry 4.0. and IoT. With the rapid advancement in technology, the ways manufacturers can collect and analyze data are also growing. As a result, companies have access to more precise manufacturing analytics and are even starting to use big data in manufacturing.
The global smart manufacturing market is expected to grow from $277.8 billion in 2022 to $658.4 billion in 2029. However, there is still a big gap between what’s possible and what the organizations currently work with.
In a recent study about the use of analytics in the manufacturing sector, 72% of executives said that they considered advanced analytics important – but only 17% said that they were happy with the value they received from it.
By taking advantage of operational, machine, and system data, you can improve your processes and make faster and better decisions to stay ahead of the curve.
1. Optimized Business Processes and Better Performance
If you want to keep improving, the key is understanding why things went right (or wrong) in the past. And having the right data makes it much easier to understand the root cause of a problem.
By combining shop floor data with data science models, manufacturers can do better demand forecasting and improve inventory management. Likewise, understanding cycle times can help them optimize pricing strategies and cut costs.
The warranty analysis is another area that manufacturing analytics can optimize. For many brands, a one-size-fits-all approach to warranties is common – but it doesn’t always fit the real picture. By applying data science models to information captured in the field, manufacturers can understand which product features require improvement or open a path for better warranty concepts that fit them better.
On the other hand, just like data from production, manufacturers can also capture data from materials in transit. This allows them to redirect, speed up or slow down resources, ensuring complete control over the supply chain.
Overall, manufacturing data analytics enables you to improve your productivity and performance, and cut costs, while staying ahead of the competition.
2. Data-Driven Predictive Maintenance
Predictive maintenance is probably the oldest and most common use of data analytics in manufacturing. Predictive analytics in manufacturing enable you to monitor and analyze system data during normal operations to reduce the likelihood of unexpected failure.
By combining data from advanced sensors, IoT, and machine learning algorithms, manufacturers can detect and assume operational issues or product defects, allowing a proactive rather than reactive approach.
As more data is collected and more correlations are made, preventive maintenance gets more accurate, bringing higher benefits. For example, it prolongs the life span of your equipment, decreases reactive requests for repairs, and prevents sudden malfunctions, which lead to unplanned breaks in production and high costs.
Plant Engineering reported that even though 60% of the manufacturing industry worldwide still performed reactive maintenance in 2020, 76% of companies stated that they will prioritize predictive maintenance in the future.
3. Research and Development, and Better Competitiveness
Technological development is a crucial influence on modern manufacturing. Therefore the industry has the potential to change as fast as technology does.
B2B customers demand an equally smooth experience as B2C customers – and there is no doubt that data is the key to providing a seamless customer experience. After all, who would be happy to dig through your outdated customer portal if their Netflix account knows exactly which movie or tv show to recommend next based on past activity?
Data analytics can help market leaders understand how the market changes over time and how the customers behave. Instead of using only historical data, manufacturers can use analytical models to understand market trends, gaps, and future needs.
These insights can be a game changer when developing new products and innovation strategies. In the long run, those who continuously invest in understanding changing trends through data analytics will bulletproof their business and act as trendsetters.
Logistics and Data Analytics
Logistics analytics transformed the industry, and went from being a “nice to have” to a “must have”. From helping you track the number and type of goods stored in warehouses. Or providing information about the shipment weight, content, size, origin, and destination. To giving you information about weather, traffic, vehicle diagnostics, etc.
Logistics data is being used everywhere, and its importance will continue increasing in the years to come. The global logistics market was valued at $7,641.20 billion in 2017 and is projected to reach $12.9 billion by 2027.
Data analytics in logistics can be highly complex but are sure worth the effort. Actionable insights collected through data, and enriched with machine learning algorithms, can help the industry players streamline factory operations, improve routing, and make the entire supply chain more transparent.
Finally, logistics analytics allows executives to make data-driven decisions to improve overall performance and business results.
1. Optimized Shipping
Two things are crucial for customers when it comes to shipping: make sure the package is not damaged, and that it arrives at the right place at the right time.
By harnessing the relevant logistics data and using logistics software, you can optimize shipping routes and complete the orders most efficiently. To achieve this, route optimization algorithms track and analyze GPS device data, weather data, road and fleet maintenance data, and driver schedules with available hours and total stops.
When paired with machine learning, route optimization can also help improve future routes and overall operational efficiency.
Logistics analysis also plays a crucial role in optimizing the last-mile process – the journey from the warehouse shelf to the doorstep of the end customer.
2. Improved Customer Service
Customers demand full transparency and timely updates from the moment an order is placed until it is delivered.
Basic shipment data enables you to track the status, estimate the time of arrival or inform the customer about an unexpected delay and the reasons behind it.
Logistics companies that want to go a step further can also use artificial intelligence to enhance customer communication. To do this, they use chatbots or the automatic categorization of emails at the help desk.
On the other hand, logistics executives are looking to make more data-driven decisions by analyzing indicators such as fulfillment rates and delivery times. Paired with feedback survey data, these can provide valuable insights into customer satisfaction and areas where improvement is needed.
This data then allows you to locate gaps and introduce changes to reduce the overall cost and improve operational efficiency.
3. Development of New Business Models
Data helps logistics organizations not only optimize and improve existing processes – but also to innovate. In logistics, the ever-growing volume of data, combined with smart technology that produces relevant insights, can help market leaders identify and benefit from new business opportunities.
For instance, when it comes to loading – logistics analytics can help you understand where you can build new business relationships and, ultimately, provide improved offerings to your customers.
According to Eurostat, 24% of vehicles in the European Union are running empty, while the average loading of the rest is 57%. If logistics companies understand when and why those gaps are happening, they can plan and build strategic partnerships and use those gaps as new business opportunities.
With data analytics, predictive logistics also allow companies to anticipate needs and introduce new delivery types, payment options, or routes.
Logistics analytics brings many benefits to the industry today, but it is only a matter of time when it won’t be an advantage – but a “must” for the logistics market.
Even though data maturity levels are different across industries, there is no doubt that data analytics will be the crucial ingredient to solving the business challenges of tomorrow. The best time to start thinking about building a data-driven strategy has already passed – the second best time is now.