How to increase your market share using customer analytics

Every business understands the need to acquire new customers, whilst retaining their profitable customers for growth. It is a universal truth irrespective of the businesses’ focus, be it business to consumer (B2C) or business to business (B2B).

By segmenting your target customers, you can understand who might buy a service or product.

Market segmentation is about separating a group of customers belonging to the mass market into smaller groups of customers (targets) with similar needs and behaviours. In doing so, you can better customise services and products to meet each target segment’s preferences.

In today’s every-changing business environment, customer behaviours and experiences are always changing, which makes segmentation difficult as these shift invariably.

Traditional segmentation methods are becoming outdated, as they are not geared to identify potential pockets of growth for targeting. Therefore, the rise of machine learning-based segmentation models are driving change.

Machine learning models can process customer and contextual data, discover patterns across various features. It can help marketing teams find customer segments that would be very difficult to spot through usual traditional methods of data analysis.

Furthermore, this will help in tailoring or identifying gaps in the product-service mix to meet the opportunities offered by target segments. The approach will also help in reducing the overall cost to acquire a new customer and increase the return on investment.

Start the journey

Every organisation must first assess the data they hold about their customers, relevancy to their product-service mix, revisit their prior marketing expenditure and its return on investment.

With this data in hand, you should review your overall business strategy, understand the pitfalls from the prior year and define the growth levers for your business.

Based on this exercise, you should aim to identify and model different target segments from existing data, applying machine learning-based models and work out scenarios that will provide the best possible outcomes, leveraging contextual data where appropriate.

Challenges

Quality in-house expertise and insight into how to create strategic advantage using data can be a challenge for many businesses.

A solution to resolve these challenges is to have a trusted advisor who can provide the appropriate involvement and support, as your data partner. This approach can provide a viable method to help an organisation harness its data's power and refocus resources on areas to deliver organisational value and growth.

How can we help?

If you are interested in learning more about how we can help you make the most of your data, please get in touch via the form below.

Enquire here