Adding business value by understanding the value of a customer
24 Jul 2017
Justin Gane of 1Digit, explains the benefits for retailers of understanding more about their customers and target markets
Big Data and machine learning are creating new opportunities for retailers to gain a deeper appreciation of customer behaviour. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed and to change when exposed to new data.
By understanding the unique value of a shopper (their current monetary value and potential future purchases), you can add value to both your business and your customer.
A key goal for every retailer should be to understand their customers, by gathering intelligence and insights into their behaviour.
Data collection should be a priority for any transactional based business. Even if a company is not able to effectively use this data in the short term, a roadmap should be established to maximise its benefit in the future.
With a flood of new open-source technologies entering the market, big players such as Amazon, Oracle, IBM and Microsoft are scurrying to adopt or mimic them.
Open source software is software in which the source code used to create the program is freely available for the public to view, edit, and redistribute.
For the first time, affordable data platforms are available to SMEs. Cloud-based “pay for what you use billing” means smaller businesses don’t have a large minimum cost of entry when stepping into this space.
Security and data governance is inherent within public clouds and all services are fully managed so businesses don’t need very technical support staff and can focus their energy on what is important i.e. Converting customer insights into strategic revenue uplift campaigns.
Modern data platforms drive customer intelligence by helping businesses understand the cost of customer acquisition, customer behaviour, churn, attribution and more.
Not every business use-case requires the use of machine learning to derive value for a business.
For example: Analytics can help improve customer conversion rates, retention and help customer targeting by using a scorecard to predict the behaviour of your customer or prospect base.
These Propensity models can identify those most likely to respond to an offer, or to focus retention activity on those most likely to churn.
In the next 2-5 years, machine learning driven customer intelligence will become common place.
Customer’s expectations of being “known” and targeted accurately a norm and they will expect to engage with personalised marketing material and offers.
Multi-channel user-experience can be personalised through machine learning models, adjusting and shortening user journeys in websites and apps by determining what their next best action would be and present them in real-time with this call to action.
The opportunities are there for retailers to grasp and the sooner you start this the more value it could bring to your business.
To find out how your business can maximise the opportunities of an effective data platform, please contact Justin at 1Digit at email@example.com.