Data Analysis

Ian has spent his career working in highly numerate environments, specialising in extracting maximum amounts of information from data. Information that can be used to drive and refine business processes to streamline the business and ensure it is running as efficiently as possible.

Techniques employed to extract information involve a combination of business expertise, commercial understanding and data knowledge. There is little to be gained from extracting information from data unless it can be used to make business decisions to support and improve the efficiency of the business.

The information is extracted through a combination of business and data workshops, combined with analytical technique to process the data through a combination of transformation, classification, grouping and predictive techniques to derive actionable insights for the business.

Business profiling problems that Ian is experienced in completing include:

Customer Segmentation

There is no such thing as a standard customer. In today's competitive market place companies are constantly battling with each other to build and protect their customer base and maximise the spending of their customers. In order to thrive under these conditions a company needs to understand their customers in order to ensure that they can respond to their needs and provide the goods and services they require.

Customer segmentation involves building models of customers, understanding the source and triggers for the customers before building and testing actionable strategies to respond to the needs of the customers.

Customer Value Analysis

The 80:20 rule crops up time and time again within the real world. Customer value is a classic example; in the majority of businesses, the large portion of companies profits come from a small portion of the customer base. Armed with this knowledge and an identification of the value (or prospective value) of each customer, a number of actionable strategies can be developed to ensure the business becomes more profitable. Examples of the strategies include ensuring better services for the more profitable, improving the sales to the slightly less profitable or not wasting marketing budget on unprofitable customers.

Customer Profiling

Ensuring you provide the right products and services for your clients is essential to derive maximum value from those clients. Customer profiling enables the user to visualise the value and interests of a customer to ensure the business has an understanding of the needs, value and potential value of each customer.

Website Analytics

Within today's information driven world, a company's web site is increasingly important as a sales and marketing tool. To ensure the website is working efficiently for the business it is important to have the site structured appropriately to ensure that customers can navigate through it and the company can extract information to understand more about the interests, value and profile of the customer.

Basket Analysis

Basket analysis involves developing and understanding of the how and why a customer puts groups of items within their basket for purchasing. The business can derive additional value from this type of analysis by learning more about the natural groupings of the products they sell, customer behaviour, providing recommendations of other items to buy, stock control and developing marketing campaigns.

Retention (Churn) Analysis

Customers are becoming increasingly fickle with where they place their business and acquiring new customers is generally significantly more expensive than reactivating existing customers. To maximise the chance of keeping a customer and minimising the spending on acquiring new customers, it is important to perform analysis of customer behaviour. Predictive models can be developed to look for trends in customer behaviour and identify customers who are likely to default. This information can be used together with predicted customer value information to identify customers that the business would like to keep to ensure appropriate, targeted campaigns can be developed to reactivate customers who are likely to churn.

Call Centre Optimisation

Companies are spending increasing sums of money on outbound call centre activities as a way of acquiring customers or cross-selling other products to existing customers. At the same time, the market place is becoming increasingly resistant to sales calls and it is important for a business to minimise the number of calls they make to their clients to maintain a beneficial relationship.

Using a combination of commercial knowledge and historic data, predictive models can be build to identify pools of prospects for a particular campaign based on the likelihood of a successful outcome from the campaign. These predictive models can be incorporated into selection and dialling strategies for the call centre to minimise the number of calls required while still maximising the commercial benefits of the campaign.