What do we want to achieve, what data sources are relevant, which data is available, can it be used, how can we use it, how do we measure effect, what is the impact on the customer experience, what are the cost, etc, etc? To answer these and many other questions requires clarity on objectives and a clear prioritization of them. Thus, effective application of the data potential is dependent on many variables and the learning process is an organizational challenge.
Most organisations are not aware of this complexity and tend to focus on some of the elements for success overlooking others. Marketing technology and GDPR have been top of mind for quite some time and huge investments have been made. Focus on disclosing relevant data sources and developing organizational capabilities that accelerate returns of these investments deserve more attention.
Balancing all elements within the data landscape and working effectively with multidisciplinary teams is challenging but will improves effective use of resources. A data strategy, that integrates activities and recognizes dependencies, accelerates data usage and improves effectivity and efficiency.
Wouldn’t it be nice if just one good analysis or data model would explain how to improve sales conversion, how to make unhappy customers happy again and how customer engagement can be improved. We all understand that the opposite is true but tend to expect this when investing in marketing technology or analytical capabilities. However, many variables impact customer behaviour and it takes time to translate insights into improved customer experiences.
When using data to improve the customer journey, new opportunities appear faster and decision making is simplified. Early detection of improvement potential and efficient decision making accelerate the adaptation to changing market conditions and improve the ability to swiftly adjust to changing customer needs.
Organizations which use data to improve their business should anticipate for constant improvement and organize using learning loops, A-B testing and continuous customer journey development. Finally it should be well accepted that marketing programs, sales distribution and media tactics will constantly change and data analysis is a structural element in the process. This will accelerate your business results structurally.
Artificial Intelligence or algorithms do NOT fully explain customers interests, desires and needs. They can clarify partially why people behave in certain ways but they is no instant fix to fully understand consumer behaviour. Data analysis, data modelling and to a lesser extent AI will help you to explore and explain customer needs and behaviour.
Do you want to identify customer needs and understand their behaviour in each stage of the lifecycle? Then invest time and interest in translating customer feedback into meaningful hypothesis and validate them with actual data.
This explorative journey is a step by step process in which your customers should be at the hart of it.
Using data effectively requires:
Mastering these elements takes time and is challenging. Although it becomes easier when capabilities grow, effective use of data will remain an ongoing learning process that require dedication and tenacity. Hence my statement that data will only help those who persevere.
Written by Dennis Stolk