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BUSINESS INTELLIGENCE SERVICES

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For upon |For 13 years, we have been rendering end-to-end business intelligence (BI) services that include consulting and development, data visualization, health check and testing to help businesses stop the guesswork and benefit from informed decision-making. We treat every project individually and select the services that cover our customer’s business needs best.

OUR BUSINESS INTELLIGENCE COMPETENCIES AND ACHIEVEMENTS

Our data analytics achievements - ScienceSoft
  • Data analytics services since 1989.
  • Business intelligence services since 2005.
  • Since 2008, a member of the Microsoft Partner Network, Gold Data Analytics Competency attained.
  • Since 2017, a member of the Amazon Web Services Partner Network.

BUSINESS INTELLIGENCE SERVICES WE DELIVER

BI Services - ScienceSoft

  • BI consulting. We help our customers develop a BI strategy, ensure data quality, design or optimize BI architecture and automate reporting to satisfy our customers’ needs in informed decision-making and maximize the ROI of a BI project.
  • BI development. We provide our customers with a robust BI environment. Depending on our customer’s business needs, our BI services cover hardware consulting, suggesting a stack of technologies, developing a data warehouse, setting up ETL and data cleaning processes, delivering OLAP/ROLAP systems, as well as selling software licenses.
  • Data visualization. With data visualization in our portfolio of BI services, we help our customers look beyond the figures, focus on the important information and immediately spot any changes and trends. Besides, we tailor reports and dashboards to user roles, solve security challenges, develop versions for mobile devices.
  • Testing. We not only develop a BI solution, but also make sure that it runs as intended. For this reason, we deliver a data warehouse and ETL testing, OLAP and report testing, as well as load and performance testing.
  • BI health check. We support those customers who already have a BI solution in place but aren’t sure if they make the most of it. We can audit the solution to find out if the technologies used are compatible, if the processes work properly, if there are any possibilities for improvement.

CHALLENGES WE SOLVE

  • Limited scope of data used for business intelligence. ScienceSoft’s team can do data integration and merging to enable the analysis of data taken from multiple data sources.
  • Growing gap between the required and existing BI solutions. ScienceSoft delivers a BI solution tailored to the company’s short- and long-term business needs.
  • Inconvenient reports and dashboards require extra efforts from employees, so their adoption rate will be low. ScienceSoft develops user-friendly reports that contribute to a fact-based corporate culture.

PRACTICAL APPLICATIONS

A path to informed decision-making starts with multifaceted business questions. Thanks to business intelligence, our customers turn their data into insights.

Customer data analysis

Customer intelligence

A business question: We had 10,000 first-time customers last quarter, of whom only 30% visited our store for a repeat purchase. What is the reason for a poor conversion rate?

How we answer: We analyze the baskets of first-time customers to see that they have purchased 3 products on average, while a regular customer buys 9. Another report shows that there were no serious problems with out-of-stocks. The number of customer complaints has not been growing. At the same time, a quarterly survey reports that customers believe our store is expensive. Can this be the reason?

Ecommerce data analysis

Ecommerce intelligence

A business question: What is the typical behavior of our loyal customers?

How we answer: Customers loyal to an ecommerce retailer shop in the store twice a month on average and spend 120$ each time buying 3 items. As a comparison, let’s look at one-time customers – they spend only 50$ and buy 1 item. Women apparel and Beauty & Care are the most popular product categories among loyal customers. Household appliances is #3 with a great potential for improvement.

Performance data analysis

Performance intelligence

A business question: Our subcategory of vacuum cleaners is declining. What can be the reason?

How we answer: We start from drilling down to different SKUs that the manufacturer produces, and see that only one new model is experiencing problems. It seems that we need to check why end customers do not like the product, so let’s have a closer look at customer complaints and survey results.

Financial data analysis

Financial intelligence

A business question: We have closed the 1st quarter. Are we still within our yearly budget?

How we answer: The manufacturer didn’t reach their production and sales targets in the 1st quarter (90% and 85% correspondingly). Besides, they have a lot of accounts payable scheduled for May. The figures show that by that time the manufacturer will not have had enough free cash to cover all the payments. So, they either have to start cutting costs or thinking where to borrow funds. Let’s see which scenario is better.

Marketing data analysis

Marketing intelligence

A business question: What is our leading brand?

How we answer: In 12 countries where the manufacturer is present, Brand X has gained 9% market share on average, while the typical figure (25th percentile – 75th percentile) for their other brands is from 4% to 6%. The performance is splendid and the trend is sustainable. It makes sense to roll out best practices to the rest of their brand portfolio.

Sales data analysis

Sales intelligence

A business question: This Monday, we had nearly as many customers as last Monday, but the sales figures were disappointing. Why so?

How we answer: A quick look at another report is enough to understand that almost a quarter of the retailer’s key products were out of stock.

HR data analysis

HR intelligence

A business question: What performance bonuses should we budget?

How we answer: We look at the historical figures of employees’ performance; forecast several scenarios of the total amount to be paid on a monthly basis, depending on how many employees would reach their targets.

Operational data analysis

Operational intelligence

A business question: A month ago, we introduced a new quality control procedure. Is it efficient? Let’s look at the initial results.

How we answer: The company’s call centers registered a 5% growth of complaints about product quality. At the same time, the number of product returns increased by alarming 20%.

The article was originally published here.

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