How Predictive Analytics Can Improve Your Marketing
Try to think of a product or service that is bought by everyone, consumed in the same way, and always used for the same reason. You probably can’t think of many – because hardly any markets are so universal. Most markets are segmented in terms of who purchases them, or how/why they are bought and used.
The key way in which predictive analytics can improve your marketing is to help you understand and identify the consumer segments that exist in your market. Armed with this knowledge, you are equipped to tune your marketing activities accordingly to align with those segments.
Depending on the industry you operate in and your priority business objectives, there are several approaches available to you for applying predictive analytics – here are some examples:
First, you can simply identify which consumers are more likely to purchase, or be interested in, each of your products and services. This allows you to target communications and advertising to your best prospects – an approach that can significantly improve the efficiency and relevance of your marketing.
Turning this question around slightly, you may wish to identify which of your products is likely to be of greatest interest to each customer – in order to communicate with all prospects but only offer the most relevant products to each. Predictive analytics can help you to identify the next best products in different ways, according to your communication channel.
The content, style or tone of communications can also make a big difference to the outcomes you achieve. By creating a small number of segments, each containing customers with a different set of needs, spending power or interests, you can then tune each contact according to the customer’s segment. Some organizations apply this approach to customize content displayed on their home pages, for example, when customers go online.
For businesses that experience high rates of attrition or churn, predictive analytics can help identify which of your customers are at the greatest risk of leaving and potentially switching to one of your competitors. Provided that an ‘at risk’ customer is also sufficiently valuable to warrant their retention, you may want to send them a loyalty offer that encourages them to stay.
This approach extends to predicting survival times: how long will customers continue to use a particular product or service, and when are they likely to stop? Survival time analysis may be applied to identify key points in the customer lifecycle, at which the attrition rate is high. It can also be used to predict survival probabilities for each customer individually, for management and retention.
Predictive analytics is an established discipline that can be successfully applied to these and many other marketing scenarios, enabling users to deploy their marketing budgets more efficiently, increase the value of their customer base, and measure the effectiveness of their activities.
I am certain that by applying predictive analytics, you can improve your marketing. I wish you every success!