Benefits of personalisation in marketing and beyond
Personalisation is now more important than ever
Onset of the Covid-19 pandemic has increased the online presence of consumers and their digital interaction with companies. This increased presence motivated brands to invest in and offer personalised services to their customers to build relationships and create better experiences. Consumers today expect seamless, helpful, and dynamic personalised experiences on every channel and during every interaction. According to McKinsey, 71% of consumers expect companies to deliver personalised interactions, and 76% get frustrated when this doesn’t happen. Companies that ignore this trend run the risk of getting left behind and could suffer from customer churn and loss of revenue.
Dynamic personalisation drives customer satisfaction
Personalisation not only helps improve customer satisfaction through a better engagement experience, it also offers several other benefits that can lead to increased revenue. A survey conducted by McKinsey states that 78% of consumers are more likely to refer friends and family members to companies that personalise, 76% consider purchasing from brands that personalise, and 78% are more likely ro make a repeat purchase. These numbers clearly illustrate the potential positive impact of personalisation on revenue through improving customer base and product sales.
Domains across an organisation that benefit from personalisation
Personalisation aims to improve a user experience through offering customised services such as tailored product recommendations or dynamic messaging. While marketing is one of key domains that could benefit from personalisation, it is not the only one. Other functions and practices can also benefit from personalisation. For example:
Personalisation in Customer Service
Personalisation can help achieve a proactive outreach to customers by analysing their profile and interaction history and identifying likely customer issues to target. This helps improve customer satisfaction and retention.
Personalisation in Sales
Personalisation in Human Resources
Personalised messages, employee benefits, and compensation offerings can increase satisfaction of existing employees and attract new talents.
What is needed to embark on a successful personalisation journey
(1) Identify personalisation use cases with top business impacts
(2) Build data strategy, capabilities and infrastructure
The foundation of personalisation is data; the data that describes customer behaviour, demographics, psychographics, and any other information that allows tailoring their experience. While tracking users at an individual level reveals the most about them, it might not be always feasible due to lack of an appropriate data collection mechanism or privacy concerns. Loyalty programs are an effective way to collect customer information. It is recommended to treat data as products with assigned ownership to ensure quality and longevity.
The data should be collected through all available channels, get reconciled, and made accessible to relevant teams and people in a timely manner. This requires a reliable data platform that is capable of ingesting, transforming, and storing the data into secure data repositories. In addition, it shall support AI/ML capabilities for analysing data and delivering value. To avoid technical debt down the line, it is essential to consider a data platform that offers flexibility and scalability. Cloud-based infrastructures are appealing alternatives for this purpose.
Data governance and data collection policies are two other important aspects that must be considered from day one. This helps ensure data security and customer privacy. When it comes to interacting with customers, less is more. While customers prefer a personalised experience, they expect companies to respect their privacy by collecting as little data as possible and using the collected data only for the purpose intended. According to Gartner, 57% of consumers say they will unsubscribe and 38% will stop doing business with a brand in response to perceived creepiness.
(3) Establish advanced analytics and AI capabilities
Collecting data alone is not sufficient to achieve personalisation. It needs to be complemented with establishing advanced analytics and AI capabilities to generate insights and value.
Developing these capabilities requires a talented team that not only has strong analytical and machine learning skills, it is also capable of translating business needs into technical requirements and designing solutions and experiments to validate those solutions.
To achieve success, the team needs to be empowered with the right tools and platforms and operate in a collaborative and agile environment in which different stakeholders interact with each other in a dynamic and timely manner. This helps communicate business goals more efficiently and identify and adapt to changes and new customer requirements more quickly.
To ensure quality of service and avoid unintended customer confusion and dissatisfaction, it is necessary to manage, quality-control, and coordinate personalisation analytics services properly. This is particularly important when there exist multiple analytics and AI models that could prescribe different and conflicting customer engagement actions.
It is also important to highlight that customer requirements and expectations are not static and evolve over time. So shall do the personalisation engine and models. Performance of the platform could deteriorate significantly when encountered with new conditions, such as new market dynamics (e.g. introduction of new trends and products) or changes in the data quality (e.g. when third-party cookies are banned). To avoid such performance drops and surprises, it is helpful to develop Model Operations (modelOps) and Machine Learning Operations (MLOps) capabilities that systematically monitor and improve the personalisation outcomes.
Success stories of personalisation
Retail is one of major domains where businesses strive to make hyper-personalised recommendations to their customers. One of Australia’s largest retailers runs programs that encourage customers to try out new products. In a recent capability uplift project, Eliiza helped the business leverage customers’ purchase history data with state-of-the-art AI models to have better new product recommendations. This resulted in >200% improvement in the recommendation engine’s performance. In addition, eliiza helped the business establish rigorous offline and online experiment testing procedures to test new personalisation strategies safely and in a shorter time frame.
Telecommunication is another domain where personalisation could be of great help. Modern telecommunication businesses rely heavily on personalised direct marketing to retain customers and encourage up- or cross-selling.
An Australian telco company aimed to upgrade their legacy marketing data pipeline and decision engine, which required laborious and time consuming manual intervention. eliiza helped the client with a comprehensive assessment of their data, infrastructure, and analytics capabilities, and created a feasible roadmap towards a personalised next-best-action system. By leveraging cloud infrastructure and AI-driven decision engines, the business was able to handle marketing actions and customer-specific experience at unprecedented scale and speed. This helped the business engage customers with the best offering, at the right time, using the right channel.
Keen to know more about personalisation?
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Meet the authors
- Senior Data Scientist
Senior Data Scientist
- Data Scientist