Tag Archives: Marketing

How retailers can leverage next-generation business intelligence and augmented analytics in 2023

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In 2023, retailers will be able to leverage next-generation business intelligence and augmented analytics in a number of ways to improve their operations and drive growth. The current generation of BI tools and platforms has already had a major impact on the way businesses operate. However, the next generation of BI is expected to be even more powerful and transformative.

Here are eight key areas where retailers should consider deploying next-generation BI to drive major business impact and further insulate in times of uncertainty:

1. PERSONALIZED MARKETING AND CUSTOMER EXPERIENCE

Augmented analytics can help retailers analyze customer data and create personalized marketing campaigns and shopping experiences. For example, a retailer could use augmented analytics to identify customers who are likely to respond to a particular type of promotion, and then target those customers with personalized marketing messages.

2. INVENTORY MANAGEMENT AND SUPPLY CHAIN OPTIMIZATION

Next-generation business intelligence can help retailers optimize their inventory management and supply chain operations. For example, a retailer could use BI to analyze sales data and forecast demand for particular products, helping them avoid overstocking or running out of popular items.

3. FRAUD DETECTION AND PREVENTION

BI and augmented analytics can also be used to identify and prevent fraudulent activity. For example, a retailer could use these technologies to analyze customer behavior and identify patterns that may indicate fraudulent activity, such as unusually large purchases or suspicious payment methods.

4. PRICE OPTIMIZATION

BI and augmented analytics can also help retailers optimize their pricing strategies. For example, a retailer could use these technologies to analyze sales data and identify the optimal price for a particular product, taking into account factors such as competition, demand, and margin.

5. INTEGRATION WITH OTHER TECHNOLOGIES

Next-generation BI is also expected to be more closely integrated with other technologies, such as the Internet of Things and blockchain. This will allow retailers to take a more holistic approach to data analysis, gaining insights from a wide range of sources.

6. REAL-TIME ANALYSIS

Another key trend in next-generation BI is the ability to analyze data in real time. This will allow organizations to make timely, informed decisions based on the most up-to-date information available.

7. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

One of the major trends in next-generation BI is the use of artificial intelligence and machine learning to analyze and interpret data. With these technologies, BI systems will be able to automatically detect patterns and trends in large datasets, providing insights that would be impossible for humans to uncover on their own.

8. ENHANCED COLLABORATION AND ACCESSIBILITY

Finally, next-generation BI is expected to be more collaborative and accessible than ever before. With advanced visualization and collaboration tools, teams will be able to work together more seamlessly, sharing insights and making decisions in real time.

 

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Six Ways To Optimize Your Supply Chain To Generate Profit

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Companies use multiple tactics to generate economic profit, including introducing new products, launching marketing campaigns or undertaking acquisitions. Supply chains offer an effective, though less understood, path to creating value through growth, driving down working capital, improving cash flow and lowering cost.

Surprisingly few companies understand the importance of the supply chain, and few have a formal strategy in place for managing global supply chain risk in the years ahead. This is especially dangerous given the volatility and uncertainty in trade relations between the U.S. and China, as well as other scenarios around the world. Besides geopolitical uncertainty, having the right talent, a holistic perspective and appropriate technology may all figure into the supply chain risk factor.

Use the following best practices to optimize your supply chain and minimize risk.

Redefine The Supply Chain

Best practices begin with redefining supply chain excellence and broadening its scope.

Create A Cross-Functional Team

Best practices for driving shareholder value through supply chain optimization can be easily implemented in any company for concrete results.

Focus On The Right Metrics

Following increased visibility and cross-functional team-making, focusing on the right metrics is the logical next step.

Connect With The C-Suite

Another essential best practice in supply chain optimization is building relationships throughout the entire company and starting conversations with the CFO and other key executives.

Manage Risks

Long-standing supply relationships have value, but disruption of those relationships can be devastating.

Total Value Optimization

The Total Value Optimization (TVO) framework promotes greater collaboration, integration and transparency.

Read more at Six Ways To Optimize Your Supply Chain To Generate Profit

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How Big Data and CRM are Shaping Modern Marketing

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Big Data is the term for massive data sets that can be mined with analytics software to produce information about your potential customers’ habits, preferences, likes and dislikes, needs and wants.

This knowledge allows you to predict the types of marketing, advertising and customer service to extend to them to produce the most sales, satisfaction and loyalty.

Skilled use of Big Data produces a larger clientele, and that is a good thing. However, having more customers means you must also have an effective means of keeping track of them, managing your contacts and appointments with them and providing them with care and service that has a personal feel to it rather than making them feel like a “number.”

That’s where CRM software becomes an essential tool for profiting from growth in your base of customers and potential customers. Good CRM software does exactly what the name implies – offers outstanding Customer Relationship Management with the goal of fattening your bottom line.

With that brief primer behind us, let’s look at five ways that the integration of Big Data and CRM is shaping today’s marketing campaigns.

Read more at How Big Data and CRM are Shaping Modern Marketing

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How to Use Big Data to Enhance Employee Performance

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Big Data has been one of the most significant and influential aspects of the Information Age as it relates to the enterprise world. Essentially, Big Data is the massive collection, indexing, mining, and implementation of information that emanates from just about any activity that can be monitored and managed electronically. Some of the uses of Big Data include: marketing intelligence, sales automation, strategizing, productivity improvement, and efficient management.

Enhancement of the workforce is one of the exciting and meaningful benefits of Big Data for the business sphere. Recently, human resource managers and analysts have been researching the implementation of Big Data as it relates to employees, and the following trends have emerged:

Employee Intelligence

For many decades, companies and organizations have tried various methods to gain knowledge about what their employees are really like. The productivity that workers can contribute to their employers is based on personal needs as they are balanced against the performance of their duties. With Big Data solutions, both personal needs and performance can be diluted into metrics for efficient analysis.

Modern workplace analytics originates from tracking employee records as well as metrics on their performance, interactions and collaboration. The idea is to focus on the right metrics to create a climate of positive engagement.

Read more at How to Use Big Data to Enhance Employee Performance

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Big Data: The Latest Rage in Supply Chain Management

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Early uses of big data were concentrated in two areas: customer segmentation/marketing effectiveness, and financial services, particularly in trading. Recently, supply chain has become the “next big thing.”

Why? A company’s supply chain is rich with data, and it’s also a large cost component. Combined, those facts mean that advanced analytics can become a strategic weapon for optimizing the supply chain.

However, many companies can’t see the forest for the trees. They are optimizing, but not strategically. When applying data to supply chain, it’s critical to step back and look at what truly drives business value.

“They’re Digging in the Wrong Place”

As every fan of “Raiders of the Lost Ark” knows, Indiana Jones found the Ark of the Covenant first. The Germans had far greater manpower and resources and they were more efficient, but they were competently digging a hole in the wrong place. The same goes for using big data in supply chain optimization. You could have the most efficient process in the world, but if you’re making the wrong amount of the wrong product, it will hurt your business.

Read more at Big Data: The Latest Rage in Supply Chain Management

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The TOFU (Top of Funnel Users) Approach to Business Intelligence

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The TOFU (Top of Funnel Users) Approach to Business Intelligence

An interesting article in Forbes.com entitled, “Why Top Of The Funnel BI Will Drive The Next Wave Of Adoption”, written by Dan Woods, sparked some great conversations about bottom of the funnel users (20-30% wanting specific business information), and Top of Funnel Users (or TOFU) that want to interact with information in a personalized way and express their interests. I was fortunate to have Matt Milella, Director of Product Development for Oracle Business Intelligence Mobile Apps, and Jacques Vigeant, Product Strategy Director for Oracle Business Intelligence & Enterprise Performance Management, join me for a podcast to discuss their opinions about “The TOFU approach to business intelligence (BI)”.

Jacques explained that the article is basically about how BI has historically focused on what we refer to as the ‘business analyst’ or the ‘power user’. That’s the person in a company that has the unenviable task of analyzing data, finding trends, and synthesizing data into dashboards that he/she then shares with management. The common thinking, in BI companies, is that roughly 20% of the users prepare data that the ‘rest of us’ consume. There are many practical and technical reasons why BI started using this model 30 years ago, but the world of technology has come a long way since then. Today, the average user can do much more with much less help from IT.

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