Problems We Solve

Businesses can be managed most efficiently if the demand for their services / products is consistent or varies within a predictable range. However, the market is driven by large number of factors and thus fluctuations are inherent. This results into supply-demand gaps which negatively impact the business operations. But there is a way to bring method to this madness! If one carefully considers the past trends and tries to link them with business environment factors, the future demand can be forecasted. This helps businesses in getting maximum returns for themselves by adopting one or more of the following: increasing supply, increasing prices, reducing man-power, reducing inventory, launch campaigns, offer sales promotion, etc.

Being able to see the future and getting ahead of competition is possible with cutting edge machine learning tools and timely implementation of findings.

Industries: Retail, Automobile, Manufacturing, Services.

Companies incur heavy losses every year due to cyber risks and frauds. With availability of data and computational applications, now it has become possible to scrutinize a large volume of interactions and transactions for such risks. Machine learning algorithms can be designed to identify cases with high probability of fraud and thus acted upon. This helps in eliminating risk and at the same time faster processing of genuine cases.

Industries: Banking, Financial services, Insurance, E-commerce.

It is a well-established fact that acquiring a new customer is far more difficult and costly as compared to retaining the existing ones. Thus, most of the service providers have strategic focus on keeping their customer / employee churn rate at the lowest possible. Machine learning is playing an important role in achieving this objective through its predictive powers. Based on past behavior, high-risk customers / employees can be identified and appropriate corrective measures can be undertaken to retain such customers / employees before they start considering leaving you.

Industries: Telecom, Banking, Airlines.

Logistic involves physical movement of goods and a large amount of resources which enable this movement. Given the competitive nature of industry, the margins are thin and thus keeping a tight control on costs is critical. Data science is helping logistics companies improve their operational efficiency by utilizing power of data in route planning, delivery timing, better choice of transportation means (vessel size, airplane size, etc.), partnership for optimal capacity utilization (own means v/s outsourced services), etc. By generating GPS data, the companies are moving towards even better quality of insights in future.

Industries: Aviation, Logistics.

With advent of online resources and massive information flow, the customers can choose the most cost-efficient purchase channel. This has put pricing pressure across the industry and thus margins have reduced. As a result, industry is focusing on increasing the revenue either by selling to more customers or by selling more to each of the customer. Thus, basket value of a customer has become very important metrics tracked by retailers. To increase the basket value, cross-selling and up-selling have become essential part of sales process and machine learning helps the cause through recommendation engine. Based on past behavior of a customer and behavior of other customers, relevant triggers are sent to buyers in the form of recommendations.

Industries: E-commerce, Retail, Hospitality, Travel.

There are many tasks that are repetitive in nature and thus can become monotonous for the employees working on them. Moreover, with increasing manpower cost, companies are looking at options to automate most of their tasks. In such scenario, machine learning and artificial intelligence offers great solution that can take care of repetitive tasks. The employees can thus focus on activities that interests and engages them.

Document handling, processing, review, translation, formatting, etc. all can be automated with minimal human interface.

Industries: IT, ITeS, Consulting, Media.

Online marketplaces have proliferated over the past decade, creating new markets where none existed. One central challenge faced by designers of online marketplaces is how to build enough trust to facilitate transactions between strangers. Poor rated/unsuccessful/dissatisfactory transactions bring down the credibility of the marketplace. Identifying and eliminating fraudulent vendors and buyers through pattern recognition and analysis will thus help to overcome a major bottleneck in the growth of the industry.

Industries: E-commerce.

Logistics operations in the world require delivering millions of packages every day. If one of their trucks has even a minor breakdown, it can result in driver downtime, late packages and angry customers. Algorithms can analyze data from thousands of trucks to predict when a part is likely to break down and repairs needed. With enough data, patterns begin to emerge that can detect anomalies. We can both cut costs and increase operational effectiveness.

Industries: Logistics, Automobile, Services.

In the present times, big data analytics and HR work together to create opportunities for businesses and enable the management to take evidence-based workforce decisions. Big data can help human resource to speed up the hiring process, improve productivity, understand employee turnover, manage talent, improve sourcing and reduce the hiring cost, all of which will lead to significant competitive gains. Adopting a cloud-based approach and using the HRSS (Human Resource Shared Services) data can make an enormous difference and open the possibilities to find the hidden insights. The key here lies in applying statistical techniques on the data and analyzing it.

Industries: All.

Businesses frequently forecast revenues. Revenues can fluctuate wildly and we have great uncertainty about them. Data science offers a unique solution to this. By properly accounting for the particular mechanisms by which companies generate revenue and the uncertainty in sales, businesses can gain much more nuanced revenue forecasts that not only give you the best estimate revenue for the next month, quarter or year, but can also give transparency into the variability of the different revenue outcomes—the good and the bad.

Industries: All.

Rising healthcare costs and deteriorating health outcomes requires that Payers and Providers collaborate to reduce fraud, waste and abuse, as well as deliver a higher quality of care by putting the customer at the center with patient-centered approaches. Big Data and analytics are helping Payers identify members likely to develop adverse health risk conditions and predicting members that can be safely redirected from unnecessary treatments in favor of lower cost alternatives.

Industries: Healthcare.

Big Data analytics is critical in infusing customer insights into the decision workflow around all areas of the organization, including customer strategy, pricing, marketing, supply chain and merchandising. Big data is providing insights into which content is the most effective at each stage of a sales cycle, how Investments in Customer Relationship Management systems can be improved, conversion rates, revenue and customer lifetime value. For cloud-based enterprise software companies, big data provides insights into how to lower the Customer Acquisition Cost, Customer Lifetime Value, and manage many other customer-driven metrics essentials to running a cloud-based business.

Industries: Retail, Advertising, Transport, Hospitality.

With the rapid increase of social media, consumers frequently post their thoughts and feelings about products and companies in online venues. In turn, shoppers researching products often seek out those opinions, also known as Consumer Generated Media (CGM) when making buying decisions. While CGM can provide companies with a rich source of consumer intelligence, it can also be difficult to work with the unstructured data and extract relevant insights without the right tools and analytic expertise. We utilize our expertise in gathering and analyzing CGM data to uncover consumers’ perceptions of the brand and determine what actions may be necessary to bolster the brand. We identified the relevant social media sites such as internet forums, blogs, wikis and discussion lists, as well as private data sources– customer emails or website feedback.

Industries: Clothing, Hospitality.

Some of the more advanced marketing organizations today are turning to digital data to identify trends that are going to be the next big thing, before they actually are. This gives Marketers a huge first mover advantage when it comes to developing and launching products for which a ready market exists. Digital data analysis can also help Marketers understand usage patterns and look for unaddressed gaps where they could innovate. Multiple sources of data including client reports, census and commercial third party data, our analysts determine the market-level opportunity and likely product share over the next five years.

Industries: Technology, Manufacturing, Engineering.

With increased competition and decreased consumer attention spans, marketers need to ensure their messages are customized and relevant to each and every consumer segment. Marketers can improve the effectiveness of their programs and see increased returns on their spends, by using marketing analytics to understand their consumers’ path to purchase and the interplay between channels- online and offline. Various factors are at play: advertising, in-store promotion, online activity, pricing strategy, and competition. We create an economic marketing mix model to identify which elements support the brand's growth over time, and which do not. By quantifying the relationship between each element and sales volume, the company becomes able to reallocate their funds with confidence.

Industries: Media, Marketing.

We deployed the Supply Chain Optimization to identify the ideal number of distribution centers to service all dealers without piling inventory beyond a point. An optimized production mix was recommended and potential routes for backhaul were identified based on the analysis provided by the supplier. The insights resulted in a major business model change where one dealer is serviced by more than one distribution center. This resulted in a reduction of inventory cost, reduced number of distribution centers maintaining the dealer demand and improving the production mix and help in identifying warehouse locations.

Industries: Manufacturing, Trading.

Price Optimizer helps you manage and simplify the price management process while enhancing pricing accuracy and consistency across the entire product lifecycle. It offers a comprehensive pricing solution by leveraging consumer demand and market insights to study customer price sensitivity. Price Optimizer enables you to plan standalone pricing, category price optimization, markdown optimization and competitive price positioning. The result – maximization of margins and increased sales by optimally clearing inventory. We also cover relative price points, demand elasticity measures and optimization to establish prices that will maximize margins. Finally we look at profiling transactions, customers, stores and products to understand what is it that seems to be correlated to margin loss or profitable transactions. What segments are not price sensitive, which ones are deal seekers, which are more or less profitable.

Industries: All.