AI and Analytics Trends-compressed
5 Steps to Build an Analytics Driven Organization
Embedding Advanced Analytics into Business Decision Making
What is Advanced Analytics?
Advanced analytics describes data analysis that goes steps ahead than simple mathematical calculations such as sums and averages or sorting and filtering. Advanced analytics leverages statistical and mathematical formulas and algorithms to recognize patterns, generate new information, and to forecast outcomes and their respective probabilities. It can be applied to several cases, ranging from the prevention of contract termination, classic customer value and success forecasts, or sales, price, and demand forecasts to new tasks such as the machine failure prediction, monitoring of social media and evaluation, and predictive policing. Advanced analytics enables both innovation and optimization. It has the potential to improve existing processes.
Are you finding it difficult to make informed business decisions, while ensuring information privacy and security? Get in touch with our analysts who can complement you with a shared need to discover and build those capabilities and drive continuous market excellence.
What are the Different Advanced Analytics Techniques?
In advanced analytics, “advanced” refers to quantitative methods such as algorithms, statistics, and stochastic processes. All the advanced analytics techniques are not predictive, but they are future-oriented as the core idea of the techniques is to support data-driven decisions in the future. Furthermore, the advanced analytics techniques can be categorized into three functions:
The descriptive analysis focuses on understanding an underlying process or phenomenon. The descriptive analysis has the potential to answer questions such as, “Which products do consumers usually purchase together?” or “What are the typical characteristics of customers who tend to churn?”
The predictive analysis aims at studying hidden relationships between factors and outcomes. The data derived is then used to forecast or estimate an unknown value. For example, a predictive model will allow us to estimate how much revenue will be lost if temperatures drop 10 degrees or forecast which customers are going to churn.
Simulation and optimization
Simulation copies the operation and characteristics of a process and condenses the outcome whereas, optimization prioritizes the options of decision-based on a key performance index.
Are you struggling to manage the high volumes of data generated by high-tech sensors? Leveraging advanced analytics solutions may help. It has the potential to generate predictive insights that facilitate proactive business decisions and pre-emptive planning. Request a free proposal here to check out our portfolio of advanced analytics solutions.
Why Advanced Analytics Is Becoming So Important for Businesses?
In this era of digitalization new technologies and analytic methods are improving business operations and are helping businesses to maintain a competitive advantage in the following ways:
- New big data technologies enable processing and analysis of large amounts of data and cost-effective storage
- Modern and intuitive user interfaces allow user groups to derive informed insights and make better decisions
- Advanced analytics software facilitates better analysis of collected data, relationships, and future events.
Today, modern technologies like predictive analytics accelerate and improve decisions and processes along the value chain. Therefore, you should carefully evaluate how advanced analytics can be used within your company in order to excel in the competitive landscape.
Key Benefits of Advanced Analytics
Detailed analysis helps in personalized marketing
Advanced analytics helps identify patterns from less aggregated datasets, such as at the store level or at an individual customer level. For example, advanced analytics can help businesses to capture the characteristics of every individual customer and offer him or her customized products by analyzing the pool of data. This eliminates the method of simply concluding the overall market demand of a customer segment and then offering the full audience’s favorite product. By leveraging big data analytics solutions businesses can significantly improve marketing effectiveness as well as customer satisfaction.
Better performing advanced analytics techniques improve accuracy
Most of the advanced data analytics techniques used today are more robust, faster, and accurate. For example, artificial neural networks greatly enhanced pattern recognition performance by generating a collection of functions that transform the input variables to approximate the required outcome. Predictive analytics and deep learning algorithms are applied to create more powerful hierarchies. Additionally, big data solutions allow machines to learn the complex business world in a much more flexible way. As a result, the prediction from analytical models is becoming more accurate and reliable.
Identify the level of risk in the implementation of the decision
By leveraging predictive analytics solutions, businesses can gain a confidence level for their prediction and select models based on performance measures. Furthermore, it helps companies by conveying the level of risk involved in the recommended action. Additionally, advanced analytics benefits businesses by conveying the risk level of implementing the recommended action. There are circumstances when there is a high cost for acting on false-positive or false-negative predictions. In such cases, companies can adjust the parameters of the predictive analytics model to choose not to act on low confidence predictions or avoid high-cost false prediction.
Advanced analytics can help organizations to deal with confusions and challenges by delivering future-oriented and fact-based insights. Furthermore, it strategically extends traditional analytics solutions from being descriptive to predictive, and ideally prescriptive.
We understand the challenges companies face in managing large volumes of data and set up analytical frameworks to derive real-time insights that facilitate more informed decisions. Our advanced analytics solutions provide best-in-class frameworks for multi-dimensional data aggregation and use visualization-based data discovery tools for insight generation. Request a free demo below for more insights.
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Digital marketing is gaining tremendous importance across all industries and within an environment where there is an immense value of ROI. Therefore, for companies, it is imperative to measure and quantify all web and social media activity to monitor the effectiveness of digital marketing campaigns and media investments. But businesses face several issues in evaluating their digital presence in the era when a strong online presence can be the key to business growth.
A recent report indicates that only 23% of companies have data-driven initiatives achieving remarkable results. 77% of marketers are still not able to report or measure how their business is performing online and how their customers react to their products or services. This is where companies need to realize the importance of web analytics services.
“Web analytics services equips businesses with the ability to decide what’s working and where there is scope for improvement.”
At Quantzig, we understand the impact that web analytics services can have on your business. And to help companies stay ahead of their competitors, our team of experts have created a comprehensive guide on web analytics services, that offer extremely helpful insights on leveraging web analytics services to improve business growth.
Web analytics services have the potential to help companies analyze and understand their brand sentiment, provide information on the competing brands and effectiveness of their promotional activities. But before we move on to the benefits of web analytics services, let’s understand the definition of web analytics.
Are you wondering how to analyze the effectiveness of company communications and customer interactions on different digital marketing platforms? Customized KPIs and metrics may hold the key to success. Get in touch to know more about the benefits of our web analytics services.
What is Web Analytics?
In this age of digital intelligence, procurement data is no longer restricted to spend data. Now, data from multiple sources including both internal and external is easily accessible to the procurement team. There is immense opportunity to deploy advanced analytics on this combined data to understand market dynamics, purchasing trends, behaviors of business stakeholder, and team performance. The derived insights can be used to brace future procurement decisions and drive value for the whole organization. Moreover, today procurement managers need to assess their real position on the ‘analytics value chain’, to get a true picture of where they actually stand. Once they gain clarity on this, actions can be taken to move ahead based on the level of maturity. The analytics experts at Quantzig have identified four metrics through which procurement analytics can help businesses reduce overall cost base and achieve more for less:
How can procurement analytics help your business?
Procurement analytics can help to optimize the working capital. Furthermore, it helps in controlling spend over budget by comparing the purchase order value or the budget with the actual invoicing. Procurement analytics also aids in analyzing “Incoterms” to identify cost-effective opportunities.
#2. Pricing metrics
Procurement analytics helps organizations to understand whether they are paying different prices for a similar service or product across its geographies and divisions. It helps identify price variance by division or geography. Furthermore, it also helps analyze spend or price development to assess whether there is an increase in spending for an SKU or category and if it is resulting in a per unit cost reduction.
#3. Compliance metrics
Procurement analytics helps organizations to identify any variance from the agreed KPIs and defined process. Typically, these cannot be avoided completely but can be definitely controlled as they always come with a higher price point. Moreover, procurement analytics identifies spend from non- preferred and unapproved suppliers. Also, it aids in fraud detection by analyzing factors like spend near approval limits and large spend without a point of sales.
#4. Supplier base metrics
Procurement analytics helps organizations to understand the existing supplier base’s performance and identify opportunities to integrate further by using more global contracts and secure better pricing. Furthermore, procurement analytics helps to analyze the geography or division that has a high supplier base built up over the years. It aids in leveraging the geographic reach of existing suppliers to secure global competitive rates.
If you want to employ robust procurement analytics and uncover better insights from data for better vendor management, negotiation tactics, and purchasing strategy- request a free proposal now!
Today, most of the forward-thinking companies have their strong presence on the internet. In fact, websites have become the new address for businesses. One of the key benefits of operating on digital platforms is that companies have access to a large amount of data that they can leverage to track and improve performance. However, a notable challenge of digital analytics lies in knowing what metrics to track, and what value to gain of them. Moreover, the knowledge and expertise required to set up and use digital analytics programs are complicated. The investment for digital analytics tools and required expertise can also be high. Often, despite putting in the best efforts many clients see unclear returns from their analytics programs. This might be due to some of the following mistakes in the company’s digital analytics strategies:
Mistake #1: Unclear goals
The first challenge of digital analytics is not having a specific goal for using digital analytics tools. Marketers are often unclear on what metrics to track, and what value want to derive out of them. This results in businesses finding it difficult to link their business results with the factors that drive those results. So, it must be ensured that every analytics program answers specific business questions and concerns.
Mistake #2: Choosing overkill analytics tools
Companies tend to believe that choosing over-the-top or expensive digital analytics tools give deeper insights and solve their problems better. And this assumption could prove to be a grave mistake for them in the future. Advanced digital analytics tools may offer more sophisticated analytic capabilities when compared to the features offered by fundamental tracking tools. But the big question is whether the business needs all those capabilities or not. The idea is to select an analytics tool that is based on the company’s goals and business needs, not by how advanced the tools are.
Mistake #3: Failing to detect tracking errors
Tracking errors can produce unreliable data and misleading analysis and can hence prove to be devastating as far as a company is concerned. Yet many companies tend to overlook tracking issues when they occur. Tracking errors can occur in the form of the developer mistakenly removing the tracking pixels, transferring incorrect values, and the tracking code firing unstably or multiple times. In order to tackle this problem effectively, companies must frequently check their data accuracy and look for unusual signs in reports.
Mistake #4: Following too many metrics
When companies have access to advanced digital analytics tools and strong computational power, it could become tempting to make the most of the tool by capturing every data point possible. However, following too many metrics can dilute the company’s focus on the core metrics that reveal the pressing needs of the business.
Find the right digital analytics solution for your business
At present, the oil and gas industry is going through a tough time, owing to the escalation in costs and need of refinement in operational processes. In a bid to overcome these challenges, oil and gas companies have turned towards advanced technologies. Improvements in software tools are creating huge scope for oil companies to boost their production and operational processes. A huge amount of data is transmitted and collected through logging tools and sensors but hardly 1% of this data is used for making informed decisions, which indicates a huge untapped opportunity. Advanced analytics has immense potential to enhance operational processes across the oil and gas value chain, ranging from exploration to production to processing to retail, distribution, and operations at back-office. The use of advanced analytics in oil and gas industry can help bring in a new wave of operational efficiency and help save on time, efforts, and costs. This article sheds light on four different ways in which advanced analytics ensure a competitive advantage for oil and gas companies.