The Future of Customer Analytics
I was involved in identifying start-ups in the space of consumer insights and data analytics to co-innovate with a multi-brand enterprise some months back. However, while scanning the ecosystem, I noted that most of the start-ups were focusing on analysing the customers’ sentiments and purchase patterns, and hoping that through the understanding of the customers’ behaviour, provide insights to their enterprise customers to carry out demand analysis.
While this approach allows organisations to understand their customers better, tailor more targeted and personalised services and product marketing, I believe the approach will evolve from customer engagement to active customer creation. The embedded presentation depicts my perception of the next stage of Customer Analytics.
How might businesses use customer analytics tomorrow
Today’s businesses make use of analytics to make sense of their data, and understand their customers to provide personalised services, up-sell, cross-sell and optimise their business operations to meet the customers’ needs. Tomorrow ‘s winning businesses make use of analytics to mould their customers into the preferred customer personas, carry out individual and precise early age influencing, and continuously modify their behaviour to create the demand for what the businesses want them to buy.
In future, we do not analyse data only to meet customer’s needs but to actively create the perfect customer through Behaviour Modifications
In future, we do not analyse data only to meet customer’s needs but to actively create the perfect customer through Behaviour Modifications
Level of action based analytics vs level of disruption
Data analytics is not a new concept. From the early days of reporting and business intelligence, to today’s analysis of multi-source big data to derive better insights for the data consumers, the focus has very much been in the creation of progressive business value.
Within the scope of consumer insights, organisations use such data to understand their customers better (stage 1) and try to predict (to a small extend) influence the customers’ decision (stage 3). This will have positive impact on moving the current inventory. However, real value creation and positive disruptions will only happen with the shift to stage 4 and 5.
At stage 4, organisations will be able to use data insights to engage in predictive modelling, customers profile and personas modelling. Through this approach, they will know what customers are buying and influence the customer’s decision through personalised campaigns to achieve in-time up-sell and cross-sell. In addition, they will be able to influence the customers through real-time multi-source data analysis, customers’ location and decision tracking and purchase pattern analysis.
By accurately predicting what each customer will buy, organisations will be able to build intelligence into their production process to meet demand just-in-time. Hence reducing cost of inventory and production.
At stage 5, multi-brand consumer products organisations will learn to deploy real-time decision tracking, predictive intervention, and direct and subliminal influencing technologies to achieve customers behaviour modification and demand creation. In fact, marketeers have already been using some form of behaviour modification techniques in their marketing approach[1]. With the use of technologies, such techniques can be applied with higher degree of accuracy and effectiveness. For example, by direct deliberate influencing through a person's network, the customer behavior can be changed more effectively through vicarious learning[2]. The disruption will be in the creation of the perfect customers and ensuring the customers will buy what they have been moulded to buy.
The technologies and techniques will focus on creating the right customers and increase customers lifetime value by moulding preferred personas through early age behaviour modifications, continuous interventions and real-time decision influence.
This will have a huge positive impact on production and operation as companies now can create the demand way ahead for what they wish to produce.
What kind of start-ups and professionals will stand to win?
The most successful analytics companies will be those that can help businesses carry out continuous behaviour modifications, to create the Perfect Customers.
The most successful analytics companies will be those that can help businesses carry out continuous behaviour modifications, to create the Perfect Customers.
With the move from reactive value creation to active value creation, the demand for analytics technology and data scientists will shift. While data scientists and companies providing data analytics will still be in demand, the shift should be moving toward companies producing self-learning intelligent technologies that actively shape the customers’ behaviour to help organisations create their perfect customers.
In terms of types of competencies, through AIML, machines will be able to generate self-evolving algorithms and have a high chance in replacing part of the work of the data scientists. While the demand for psychologists and neuro technologists will increase.
What We See, What We Hear, What We Eat and What We Use Make Us Who We Are
There is no right or wrong customer through influencing and modifications, but only whose customer will one grow up to be. By early influencing and behaviour modifications, multi-brands organisations will have a high chance of creating their perfect customers.
What We See, What We Hear, What We Eat and What We Use Make Us Who We Are
There is no right or wrong customer through influencing and modifications, but only whose customer will one grow up to be. By early influencing and behaviour modifications, multi-brands organisations will have a high chance of creating their perfect customers.
by Edmas Neo
#customer_analytics #data_analytics #bigdata #consumer_insights #behavior_analytics #data_scientists #aiml
References:
[1] Nord, W., & Peter, J. (1980). A Behavior Modification Perspective on Marketing. Journal of Marketing,44(2), 36-47. doi:10.2307/1249975
[2] Bandura, A. (1962). Social learning through imitation. In M. R. Jones (Ed.), Nebraska symposium of motivation (pp. 211-269). Lincoln: University of Nebraska Press
Disclaimer:
The models and predictions are based on the author’s own perceptions of the future trend of consumer analytics. The discussion on the use of consumer behaviour modification technologies did not take moral ethics into consideration, and if such techniques could be legally deployed.
