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!

Book a Consultation

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.