Data are everywhere. Computational power has grown exponentially. Research has driven increasingly sophisticated machine learning algorithms. And the Internet makes anything possible. Organisations large and small are exploiting this convergence of factors to gain that elusive competitive edge.
A large enterprise can afford teams of data scientists. But small businesses too can leverage artificial intelligence. Armed with AI, they can drive incremental growth, optimise performance and make better decisions grounded in facts.
Data science uses scientific methods, processes, and algorithms to extract knowledge and insights from data. Here are just a few ways data science could help a business.
A robust sales forecast can form the basis of targets for the years ahead; perhaps augmented by assumptions about new products. A revenue forecast also provides a good foundation on which to build a budget. See the post But can ravens forecast?
Given an expected event, a business may want to know which products will sell well. Thus, a prediction algorithm, modelled on data from similar previous events, can help a business prepare and maximise profitability.
Retaining existing customers is more cost-effective than acquiring new ones. So, there is value in identifying those customers with a higher probability of attrition. And tailored and targeted offers, may mitigate the risk of churn.
An effective marketing campaign may require the segmentation of customers into a small number of groups. A clustering algorithm can establish if there are natural groupings. Profiling these then makes it possible to tailor the marketing message, and method of delivery.
There’s value in knowing which products or services are commonly purchased together. Specialised association algorithms can achieve this. And thus recommendations may be made, at the point of purchase, on further products and services.
To increase the chances of launching a successful marketing campaign, a business may want to first try a small pilot. If the pilot delivered an x% increase in sales, the question then becomes: could this translate across the market if adopted fully? Hypothesis testing can help make the right decision.
Competitive pricing requires data. Some of these data-points may be in the public domain as illustrated in this post. So, by employing data mining techniques to collect, prepare and analyse these data, a business can set an optimal price to compete effectively.
One way to optimise performance is by comparison. Suppose data mining finds a pattern of exceptional profitability in one geography. A statistical hypothesis test will reveal if this is due to natural random variation. If the influencing factors are real, then a business can raise the bar across all geographies.
A linear programming algorithm could help optimise resources. For example, suppose one wanted to find the shortest delivery routes to make possible more deliveries. Or perhaps maximise profitability by buying and storing the optimal mix of products.
Data mining can reveal the factors driving employee attrition. Maybe many prior jobs predispose an employee to move. Perhaps excessive overtime is a factor. Retaining the best talent requires an understanding of the causal factors. Preventative initiatives need predictive models to identify those at risk.