Clinical trials optimization

Finding opportunities in historical clinical trial data

BACKGROUND

Historical clinical trial data frequently hide extremely valuable insights on alternative, or more targeted applications of known active substances. A leading pharmaceutical company asked us to develop a new tool to systematically investigate historical data.

APPROACH

To unleash the potential of historical datasets, we tested, tailored and implemented a variety of machine learning techniques such as PRIM (Patient Rule Induction Method). We also developed a new, proprietary tool with a web-based interface.

OUTCOME

The new tool is now in daily use in the research facilities of the client. It has already indicated a number of important leads, being considered for new clinical trials.

Optimizing promotion by machine learning

Creating value by optimizing marketing mix

BACKGROUND

A leading international technology company was planning expansion in a new European region. The planned promotion needed to combine digital and traditional channels, and to reach well-defined, measurable targets in a short time for a limited budget.

APPROACH

In addition to in-depth understanding of available market data, we implemented a proprietary digital market research tool, in collaboration with a partner company. The promotion approach was then optimized by machine learning, by applying micro-testing and removing bias in collected data through state-of-the-art statistical modeling.

OUTCOME

The proposed promotion action plan, including market segments, value proposition to be communicated, channels and budget, is being rolled out by the client exactly as recommended.

Budget forecast

Creating value by forecasting project spend

BACKGROUND

A Fortune 500 client had a challenge of forecasting yearly spend of a portfolio of 600+ projects with annual budget in excess of 300 million USD. We were hired to understand the root-causes, and to fix the problem.

APPROACH

We first analyzed the client operations, and in particular budgeting, financial reporting and project management practices and procedures. Based on this, we developed a new statistical approach and tool to forecast spend of individual projects, based on a number of predictors. In addition, we proposed new reports, tools, and changes to the existing processes in finance and operations.

OUTCOME

The tool and the process changes have been fully implemented. As a result, the client now plans, approves and delivers 20% more projects for the same budget, with at least proportional increase in delivered value.

Pricing and revenue management

Managing pricing and capacity in real time

BACKGROUND

The hospitality industry is undergoing a dramatic change, with rapidly increasing importance and market strength of the new distribution on-line sites such as booking.com . To achieve optimal, or even reasonable occupancy and price levels, existing manual processes are thus often neither agile nor accurate enough. A leading hospitality company hired us improve it to partially automate pricing and capacity managenent.

APPROACH

We first mapped in detail existing pricing and capacity management processes, and understood opportunities by studying historical data. We then developed an automated revenue management tool, with key levers: forecasting, optimizing and managing overbooking; allocating capacity accross room types and distribution channels; and optimizing prices, all in real time.

OUTCOME

The tool is now operational, managing the entire accomodation capacity of the client. It partially automates revenue management decisions in real time. The added net present value of the project is estimated to exceed value of developing two entirely new hotels.

Best practice examples

Big data for c-suite

Strategic importance of big data analytics

Here is a collection of articles from McKinsey & Quarterly on various strategic aspects of big data, as well as on intriguing case examples.

Picking startups

Choosing VC deals by machine learning

McKinsey Quarterly reports on a machine-learning approach to venture capital. The model recommending the deals already contributes to the decision making. The main break-through seems to be in collecting sufficient amount of quality historical data enabling modelling.

AI la mode

Machine learning in fashion industry

While forecasting of fashion trends by machine learning is still in its infancy, The Economist reports that the industry of fashion forecasting is approaching the "tipping point" driven by AI advances. The winners leveraging it will be both on the retail side, by correctly forecasting trends, as well on the supply side, by more effectively managing supply chains.

Hbr on AI

The business of artificial intelligence

Harvard Business Review in its cover story reviews the current applications of machine learning in business, and predicts some future trends. It is a great introduction for business leaders with no practical AI experience, but with awareness of its likely importance.

A startup for ai startups

AI services to business

The Element AI come up with an intriguing idea: to be an "AI agreggator", connecting academics to businesses. Their recently raised $102 million of capital, as well as a positive coverage by The Economist, will certainly help.

When big data goes lean

Applications of machine learning in manufacturing

A 2014 paper in McKinsey & Quarterly gives a C-level overview of applications of machine learning techniques in manufacturing. There is a particularly insightful matrix of case examples and relevant data analysis techniques across industries.

Ai for Harley

Increasing sales by predictive analytics

A Harley-Davidson dealership has improved sales by measuring and optimizing marketing campaigns. Harvard Business Review tells a story how they rely on Albert, an AI tool developed by Algorithms. The key seems to be using the tool incrementally, to drive experimentation and quantify outcomes.

Winning with digital trees

Predicting house prices with gradient boosting

Modern machine learning methods can give you statistically useful predictions in many business situations, even when the source data is relatively scarce and unstructured. Here is a winning example from a Kaggle competition on predicting house prices.

More on the gradient boosting algorithm used there by a leading data practitioner.

The data is king

Why companies need to lead in data and analytics

McKinsey survey of senior executives indicates that, while they have high hopes of their data and analytics programs, they report only mixed success so far. Some insights on the key success factors can be found here.