Retail Analytics using Microsoft PowerBI

Retail Analytics using
Microsoft PowerBI

Gain market insights, understand customers and improve
supply chain efficiency with interactive reports, dashboards and insights

Retail Analytics using Microsoft PowerBI

Gain market insights, understand customers and improve supply chain efficiency with interactive reports and dashboards

Retail sales involves the purchase of finished goods by the customer. For example, you walking into a Tesco or a Fairprice and buying a carton of milk. Today, with the rapid increase in the amount of data collected from different touchpoints at retail sales, there is an increasing demand for this data to be used to derive actionable inferences and insights which can help both the business and the customer. This in technical jargon is what we term – analytics.

Sales analytics is a process to identify, model, understand and predict sales trends and sales results while helping in the understanding of these trends and finding improvement points. It is used to determine the success or failure of sales activities in a business and also to allow them to prepare for the future.

Every sales activity is measurable. There is more data than ever before. In order to have control over sales performance, use sales analytics and the right tools like PowerBI!

Where does the data come in from?

Data comes in from various sources such as application transactions, surveys and internal applications. All these are collected and analyzed to find relationships and opportunities that may be used by the organisation.

Sales analytics reports and dashboards – How do I go about doing it?

Below is a short video demo on how PowerBI, a growing Microsoft Business intelligence tool, has been used on a sample sales data, to give insights to the business about sales, products and customers. The sample data consists of sales transactions captured by POS systems based on customer transactions and using this data, the demo walks through multiple reports and drill downs to give the organisation deeper information. It also shows the ease of use of a tool like PowerBI with huge data, both for simple and complex reports.

Why monitor sales analytics?

Enhancing the reports

In our next case study, we will talk about how such reports can be extended to have elements of predictive analytics and machine learning which will further help the business use these insights to drive sales and reach out to customers better. Systems such as recommendation, stock prediction and others can enhance the usability and effectiveness of such reports.

 

Want to know more?

If you think this solution would be useful for your organization or you have a relevant use case or pain point you would like to tackle, get in touch with us today and we can help you and work together towards a solution!

Video surveillance and video analytics

Video Analytics was invented with a motive – to help in reviewing the growing hours of surveillance video that a security guard or a system manager (or a human) may never have time to watch. Video Surveillance systems equipped with Video Analytics can help us in finding those minor details that can’t be perceived by naked eyes. Video Analytics or Video Content Analysis is computerized video footage analysis that uses algorithms to differentiate between object types and identify specific behavior or action in real-time, providing alerts and insights to users. Since Video Analytics is based on the technology of Artificial Intelligence, experience plays a significant role. A highly trained model can see through very minute details in video footage.

This technical capability is being used in a wide range of domains, including entertainment, health-care, retail, transport, home automation, flame, and smoke detection, safety, and security.

Video Analytics relies on useful video input. To make the video useful, following techniques are implemented for increasing the quality of the video recorded:
1. Video Denoising
2. Image Stabilisation
3. Unsharp Masking
4. Super-Resolution

What are the Commercial Applications of Video Analytics?

CCTV Systems – This is the most widespread application of Video Analytics. VCA(Video Content Analysis) is distributed on the cameras (at the edge) or centralized on dedicated processing systems. These CCTV’s, for example, can be used to detect and report any suspicious activities of shoppers in a store. Another popular example, is the PIDS(Perimeter Intrusion Detection System). It is deployed in areas whose perimeter can extend to a large radius, like airports, seaports, and the railways. With this technology, we are able to track any intrusions in real-time, giving us sufficient time to react.

  • Traffic Systems – Deployment of Video Analytics on busy squares in crowded cities of the world can be a massive time-saver for the people and the government. At peak times of the day, when the traffic is very high, specialized use of analytics can be used to avoid congestion.

 

  • Counter-Flow Detection – Walking against the flow in specific locations, such as the airport security checkpoint and gates, can be a sign of some danger. It can potentially result in terminal shutdowns. The wrong entry of vehicles in a one-way can lead to congestion affecting a large number of people. These faults can be quickly responded to by the uses of video Analytics.
  • Suspect Search – The data of facial-recognition can be aggregated along with video Analytics for the detection of criminals at high-security places like the airports’ immigration counter, the baggage collection facility, taxi stands, etc. This can lead to the smooth and swift arrest of such people or elimination of such objects. Time is the essence when looking for something critical.

 

  • Long Queue Problems at the Shopping Centres – In the densely populated countries like China, India, the crowd increases significantly in stores during the festive season. Trends in data can be used to analyze the crowd and arrange for particular changes for a short duration of time, increasing the store efficiency, and saving the time of the people.
  • Reducing Retail Shrinkage – Retail and logistics companies can use video surveillance analytics to minimize inventory loss significantly. The model is trained to detect unusual activities like unexpected times of presence, unauthorized access, or any suspicious movement of inventory and more.

 

  • Improving Patient Satisfaction – Video analytics can help hospitals and dispensaries to improve the overall patient experience. Artificially engineered cameras can continuously monitor patients waiting to meet the doctor and ensure they are checked-in within a given time duration. Even an alert can be sent to the staff regarding a patient who has been left unattended for a long time.

Video Analytics is the smart way of engaging customers, reducing wastage of time and improve security. Video data collected is massive and it would be practically impossible for a human to replace a computer. With the fast pace of life and the amount of video content today, using video analytics is a lifesaver for different fields.

Data Science in the Chemical Industry

Data science and analytics is such an evergreen field that finds its use in every industry. Today the world is moving towards automation, and even the chemical industry is starting to adopt such practices and thus the use of data science in the chemical industry has increased significantly. Every experiment starts from a simulation of a process in the laboratory and data science and modeling helps in scaling it from the lab scale to a plant scale. So, let us dive deep into understanding how data science can be applied to chemical engineering.

For example, a lot of times, the chemical industry is full of recording errors. Error in recording parameters may hamper various simulations and processes. In such cases, data science and analytics in the chemical industry provides a significant advantage. A few major advantages of using industrial data science techniques are:

  • It helps in quickly identifying trends and patterns, which is an essential requirement for the chemical industry to recheck an observation.
  • It leads to reduced human effort, which means fewer chances of errors and reduced cost.
  • As data Science handles multi-dimensional and multi-variety data, things can be done in a dynamic and uncertain environment.
  • Observing calculations to estimating the number of chemicals required for a reaction, holds the capacity to benefit the industry.

Considering the above points in mind, we can clearly state that analytics can not only boost production but can also reduce and cut-off unprofitable production lines that are not of any use, helping in both – reduced energy consumption and reduced wastage of valuable resources like labor and time.

Stan Higgins, the retired CEO of the North East of England Process Industry Cluster (NEPIC), who currently is a non-executive director at the Industrial Technology Systems (ITS) and also a senior adviser to Tradebe, which is waste management and specialty chemical company, says that miracles can be done using analytics in chemical industry. He describes that his work accompanied by data analytics led him to win the Officer of the Order of the British Empire (OBE) for the work promoting the UK’s process manufacturing industry. He describes that in production, the challenges are never-ending.

 

The key to any successful venture is maintaining quality production and maximizing output within health, safety, and environmental goals. Every day, new chemicals, and intermediates are being developed in chemical industries, and it requires a lot of attention for a human being, considering all processes like cost, availability, quantity, and then being able to decide the most suitable chemical product and alternative on a daily basis. The chances of error are very high, and it can be crucial to the industry.

What are some of the other uses of data science and analytics in the chemical industry?

  • Use for checking the overall value of an alternative chemical, over the currently being used chemical.
  • It can help in determining precise and essential measurements for the reactivity of chemicals, checking for their optimum conditions that are favorable.
  • It can help in understanding the best reactivity of a catalyst for the different conditions of temperature, pressure, and other conditions.
  • It helps in guessing a pre-determined result after a reaction.

Concluding, it won’t be inappropriate to say that there isn’t a field where data science and analytics can’t find its application. For large industries, business intelligence plays a key role in promoting growth. So, analytics and BI in chemical industries can bring about huge improvements over a period of time.

Data Visualization: 6 Best Practices

Our world is progressively filling up with data, all companies – significant multinationals to the minor young startups are stockpiling massive amounts of data and are looking for ways to analyse this data in the raw form and obtain processed information, that can make complete sense. Data Visualisations represent data in pictorial form for the marketing managers to understand complex data diggings.

According to a fact, 3.5 trillions of e-mails are sent every day for the promotion of many companies; companies prepare ads, stockpile enough resources to deliver them to as many users as they can. With a slight observation, a considerable portion of receivers can be cut-off, who have a meagre – conversion rate. Doing so will not only lower the wastage of their resources but will also help them concentrate more on the people belonging to a higher rate of conversion, thus increasing the chances of the product being sold. For doing this, the implementation of supreme data visualisation is necessary.

Data Visualisation can take everyone by surprise. It is here that a meaningless looking pile of data starts making sense and delivers a specific result as per the likes of the end user or developer. It takes shape with the combined effort of ones creativity, attention, knowledge, and thinking. Data Visualisation can be useful, as well as harmful. (Read: 5 common mistakes that lead to Bad Data Visualization)To help your cause by not misleading your visualisation, here are some of the best practices for making your visualisation clear, useful and productive.

A. Plan your Resources
Create a sequence of steps by obtaining your requirements, your raw data, and other factors that might affect the result. This requires knowledge and experience for a data scientist to choose which method to use to for visualising your data. Planning the resources can be very helpful, as it will lead to greater efficiency with the efficient workload.

B. Know your Audience
The most essential and unavoidable step in creating great visualisations is knowing what to deliver. Focus on the likes of the audience, their mindsets, their queries, their perceptions and beliefs; and then plan effectively. It is not necessary that all the viewers will receive the information in the same way. For example, a probability density graph has a different meaning for an HR manager and a chief sales executive. So, it’s very vital that you know your target audience and prepare visualisations according to their perspective.

C: Predict after-effects
Predicting what might be the effect on the end users can add up to your cause. There can be a no-action session where everything is going positive in your way, while a downfall in a particular field may require some immediate action.

D: Classify your Dashboard
There are three main types of dashboards – strategic, analytical and operational. Following the below steps would let you know which dashboard suits best.

  • Strategic Dashboard: It represents a top notch level view of the inquiry line answered in a daily specific routine and presents KPIs in a minimally interactive way.
  • Analytical Dashboard: It provides a range of investigative approaches to a central specific topic.
  • Operational dashboard: It provides a regularly updated answer to a line of enquiry based on response to events.

E: Identify the Type of data

  • Data is of three types: categorical, ordinal and quantitative. Different types of visualisation work better with different kinds of data. A single relation of a data works best with line plot, two pieces of data work better with a scatter plot. A brief description of the type of data is given below:
    • Quantitative: Defines the number of data
    • Ordinal: Data that belongs to the same sequence. Ex: Medals – Gold, Silver and Bronze.
    • Categorical: Data that is of one type. Ex: Gender – Male, female and Other.

F: Use of Visual Features

  • Having done the above, a perfect choice of colour, hue, saturation can glorify your visualisation. It is just a matter of the presence of mind that draws attention.
  • Using the wrong hue and saturation configurations can bring ruin to all your efforts. A good set of visual features gives a final touch up to your data visualisation.

Create some stunning reports and real time dashboards with Xaltius’ BI and Analytics Services.

Concluding, modern technologies like machine learning and AI by itself will find no use for business corporates, if not for data visualisation. Data Visualisation has itself found its field of study and interests and finds its importance in every walk of analysing data.

Artificial Intelligence – Transforming Human Resource

The fourth industrial revolution is bringing forward transformation powered by artificial intelligence in many sectors. The Human Resource department is no less affected.  While AI cannot replace the HR department as a whole, it can certainly bring forth massive improvement. Experts point out that many innovative methods using AI can be adopted to minimize unnecessary work load, while maximizing employee selection and their efficiency.  Machines can takeover tasks that are tedious and time consuming. It can also bring transparency and accuracy to many processes that are usually subject to discrimination. Thus, it would help make the process better and take informed decisions.

Data management and analytics

The data collected by the HR department of the corporates can be effectively managed using AI. Face-recognition and other technologies that are capable of identifying gender and measuring employees’ psychological and emotional traits can be used and the data generated can be used for analytics. Each employee’s performance can be analyzed in depth so that their employees will have a clear picture on who to keep and who to let go. Evaluating the workforce can bring about smarter decisions that will lead to better performance results.

Analyzing such data can predict the future ROI, increase or reduce engagement levels of employees, solve problems pertaining to completion of projects and other unforeseen glitches that would normally go un-noticed by the human eye. It can also provide insights to employees on how to work more efficiently.

The hiring process

Talent acquisition is one of the major areas where AI can be a blessing. An analysis of the resumes can put forward the best candidate for the job, with the algorithm giving importance to the factors that the company wants. Focusing on performance, culture and career-alignment analysis, AI can quickly identify whether or not a candidate is a good fit. AI will also be devoid of the biases based on race, gender or other factors that usually influence the process.

Replacing Administrative Tasks

Repetitive recruiting tasks such as sourcing resumes, scheduling interviews and providing feedback can be replaced by machines, giving the officials time to work on other matters. Conversational interfaces can be used instead of emails for communication. Chatbots can be used to answer real-time questions raised by either the employees or the customers.

AI can never replace this human driven sector completely, which places so much importance on personal relationships. AI will most likely never replace processes which involve connecting with top talent, providing a more personalized interview experience and establishing training and mentoring programs. In lieu of the above, Ben Peterson from BambooHR say that “increasing speed, quality and efficiency without sacrificing meaningful communication and relationships seem to be the right balance leading to the best possible outcome.”

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

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