Today, companies are realizing the wealth of insights they can gain from studying the data they collect on a daily basis. Digital tools (mobile apps, client reports, etc.) have allowed today’s businesses to gather information systematically in order to become better.

From improving operational efficiency to innovations in targeted promotions and in inventory forecasting, these three cases have proven how data analytics can achieve real business impact.

Creating Targeted Promotions

Businesses can rely on data analytics to optimize promotions and drive business growth. Digital technology has allowed marketing professionals and agencies to create data-driven campaigns that lead to better customer acquisition and retention.

2017 was the year US digital ad spend surpassed the combined total of TV, broadcast and cable advertising. Online advertising provides a good alternative to the traditional, “spray and pray” kind of advertising because it allows for more specific audience targeting and recording of valuable metrics. Enterprises now use platforms such as Facebook to create promotions that target various audience sets. For example, marketers can use the social network’s database and pick subcategories under purchasing behavior (such as Buyer Profiles, Clothing, Food & Drink, etc.) to create more relevant campaigns. They can even find out how many user profiles could be targeted per campaign based on offline purchasing data.

Beyond Facebook campaigns, businesses can harness the power of their own customer data to segment their customer base and design their customer-facing activities around these segments. Campaigns and offers can be adjusted and optimized accordingly, and messages can be delivered through the most relevant digital channels – whether that be through mobile apps, SMS, chat, email and others.

Targeted campaigns can also take into account various activities in the customer journey that can serve as real-time triggers. By reaching customers during their moments of intent (e.g. browsing through products with the intent to buy, reading product reviews, looking for guides to complete a task, etc.), marketers are able to increase their relevance and move the customer along the buying journey.

The goal is to deliver the right message to the right customer at the right time, and ultimately, provide the best customer experience possible.

Improving Supply Chains Through Machine Learning

Data analytics play a big role in optimizing modern supply chain management. With the volume of data produced in supply chains increasing exponentially, the demand is growing for more sophisticated solutions. Further enhancements in the capabilities of artificial intelligence (or AI) and machine learning could also mean more efficient processes and higher productivity.

Adopting new systems also allows suppliers to solve traditional problems in supply chain management. Most suppliers depend on legacy management and supply methods without the use of modern data analytics or AI. With these new technologies, supply chains can modernize their processes to keep up with the demands of clients.

The advancements in AI are now paving the way for better product quality inspections using advanced image recognition software. In lieu of manual inspections in logistics hubs, automated systems controlled by AI can accurately scan for product defects and faults before shipment. Meanwhile, machine learning models are streamlining auditing and compliance monitoring of component parts found in highly regulated industries, such as aerospace and healthcare.

A New Process for Inventory Forecasting

For international fashion brands, it’s not enough to just have the right kinds of apparel well stocked per season. It is crucial to have extremely agile development cycles to ensure the timely delivery of in-demand products, from concept to sale floor.

New technologies and data gathering methods allow fashion houses to adapt quickly to the needs of their shoppers. By adjusting their internal controls based on their consumers’ shopping habits, they were able to better anticipate which items were selling better, and which ones should be changed out.

An international clothing company uses its customers’ data on purchasing decisions to better forecast inventory changes. They developed an app with a catalog that is constantly updated with the latest items. They use this to collect information on what items are selling out quickly. Data on purchasing behavior also allow merchandising teams to make better inventory decisions to reduce over and under-stocking.

How Do I Start Using Data in My Company?

Understanding the value of your data and how it can help improve your business is the the first step in your data analytics journey. Amihan can help you map out a viable data analytics strategy. Just fill up the form and we can help you get started.

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