Our Approach to Optimising your business
Data is the key in evaluating business choices and driving future decision-making. Identifying relevant indicators along which to trace, understand and improve your business processes can be complicated, however. Obtaining the right data and modelling solutions requires an extensive interaction with and understanding of your business. Apadana excels in connecting with your business needs in building top quality solutions.
In tackling any business case, whether guiding a company-wide data strategy or attempting to answer a succinct business questions, Apadana goes through four concrete phases that allow for extensive interaction and evaluation with our clients, ensuring that your business interests are heard. It also provides our clients with timely intervals to evaluate potential and value going forwards – a lack of constant interaction and discussion can lead to highly sophisticated and complex solutions that serve no one. At Apadana, we pride our ability to transcend classic communication barriers between the world of data and that of actual people.
Apadana offers four types of services
Data Discovery
Primarily designed for organisations that want to take their data strategy to a new level, the Discovery phase provides fresh perspectives on the possible routes forwards for your organisation. The discovery phase is purposefully concise and fast. Without having to commit large amounts of time and resources, we provide a number of clear data strategies that can be embarked on immediately.
Deep Dive
Conditional on your organisation's data strategy and goals, Apadana's Deep Dive phase examines the compatibility of your current data infrastructure and your organisation's data goals. In addition to evaluating the completeness and relevance of your internally managed data, Apadana evaluates the potential of external data sources like web-based sources, and public or commercial databases.
Analysis & Modelling
Apadana's Analysis phase is solely concerned with obtaining actionable insights that are in line with your organisation's goals. A framework for these insights are distilled in the markers following the Discovery and Deep Dive phases. This structure is crucial to ensure Apadana's analysis remains relevant and actionable, instead of being driven disproportionately by the data at hand.
Deployment
Apadana's Deployment phase turns actionable, data-driven insights into tooling that can assist your organisation in its day-to-day operations. Part of the Deployment phase is the integration and compatibility of current models with future data sources. We ensure that new hypotheses are continuously evaluated. An extraordinary model today may not be fit for tomorrow.
Examples of previous business cases
Customer Intelligence
Targeted Call-backs
A high-end, medium-sized travel agency did not have sufficient capacity to return all enquiries received through their website. Based on historic data, we estimated conversion rates based on customer characteristics, enquiry characteristics and seasonal trends. Now, our models predict the estimated conversion rate for all enquiries and ranks those with the highest expected profit. Our tooling enables all employees to evaluate this ranking in real-time and make sure the most valuable enquiries are acted on first.
Segmenting
A large theatre wanted to increase the number of yearly visitors. By using large amounts of available information on customers and their visits, we determined the quintessential visitor types and their typical behaviour. In addition to better understanding their customers and booking performances based on this information, our segmentation allowed for the tailored distribution of marketing content and the offering of specialised deals.
Process Optimisation
Efficient speed
For a large shipping firm, fuel represent one of the largest costs. Furthermore, outside conditions - e.g. wind speed, current etc. - and vessel characteristics - e.g. size, type etc. - strongly affect fuel consumption at different speeds. Through statistical modelling of geolocations and fuel consumptions, vessel information and external circumstances, we determined the most cost efficient speed at which to sail. Our model could take real-time information to determine a vessel's optimal speed
CV Screening
For a HR firm, we used information embedded in resumes to streamline the process of selecting candidates and matching them with vacancies. Through the parsing of resumes of past applicants and historical information on their subsequent successes, we created a model that estimated the success probability for each new resume allowing consultants to reach the best candidates first.