In today’s fluctuating marketplace, no marketer can survive without mastering data analytics and automation. Artificial intelligence (AI) and machine learning (ML) solutions are revolutionizing the niche of marketing today. These technologies enhance the system in such a way that it learns from experiences and uses them to improve performance across brand collaterals, segmentation, communication, content recommendation, and more. The need for human intervention is reduced since the entire process is automated. Several companies have already discovered the importance of making machine learning and marketing go hand in hand.
In a recent webinar hosted by Quantiphi, Mahek Harwani, the Customer and Marketing Analytics Lead at Quantiphi, brought to light how ML can help marketers extract actionable insights that impact marketing campaigns. This webinar dives into the realm of unifying customer data to perform marketing analytics using advanced ML technologies in just fifteen minutes. Watch Mahek Harwani and Nikitesh Patil explain the ability of AI/ML to enhance marketing abilities in the webinar recording.
Mapping the Customer Data Universe
If the entire customer data universe is broken down into two broad sections - internal data and marketing data - the unification of both these sections is essential for data-driven insights analytics. The internal data for any organization is every data received from every data point. This could include transaction history, contact center or customer service center data, claim details, or any other personally identifiable information. This can be derived from CRM or ERP systems, KYC, or point of sales data sources and helps determine who your customers are, what they do, and the value they add to your enterprise. The internal data comprises any and all data sets that you currently have and own in any system or are stored in data warehouses, and data lakes.
The other half of the data universe helps you reach out to customers, engage with them and develop your brand positioning. There are various channels and mediums that you can use to reach out to them such as websites, social media, applications, community channels, and one-on-one communication channels. Mostly, these data are siloed with different teams within the company.
A holistic view of the customer
The approach here is that these different data sets shouldn’t be sitting separately. When the internal and marketing data streams are unified, it offers organizations a higher chance of getting a 360 view of the customer, which gives them a competitive advantage over their competitors. When data from all entry points are combined, organizations can take advantage of previously untapped variables that can add immense value to the organization. You get a better understanding of your audience cohorts and characteristics, which gives a better sense of who your customers are.
Also Read: Deliver Exceptional Customer Experiences With Customer Data Platforms
This newfound knowledge can be utilized to revamp acquisition, retention, personalization, and branding in a data-driven fashion. By identifying sales, trends, and external factors such as seasons, holidays, etc. that have an impact on business, organizations can make better decisions that enhance the customer experience and provide them with better services and offers. When data from all points are combined, the resulting analysis makes it easier to identify a high-net-worth individual and clearly demonstrates how they add value to the business.
Enhancing Marketing Strategies using Data Science
Machine learning models can be developed to perform analysis based on unified customer data to provide better insights. In the webinar linked above, ML models are used broadly for -
- Predictive analytics - Using ML algorithms from a predictive sense
- User experience personalization - Enhancing user experience and enabling personalization
- User behavior analytics - Helping organizations understand their users better
1. Predictive Analytics
ML has made it possible for organizations to easily generate insights from structured and unstructured data sets, which is difficult to achieve with traditional business intelligence solutions. 98% of organizations consider analytics to be an important aspect that drives business priorities. ML-based analytics solutions usually operate in real-time and generate insights for front-line employees while older models continue to generate reports for senior decision-makers. When combined with ML, predictive analytics is a powerful tool that helps entities get the full value of the massive amounts of data generated during everyday operations. The primary reason why ML-based predictive analysis is growing in popularity is that it benefits virtually every industry. From analyzing and predicting customer churn to understanding conversion propensity, when tuned properly, ML can generate insights that optimize the marketing and administrative efficiency of all possible industries.
2. User Experience Personalization
There was once a time when user experience personalization was a luxury. However, in today’s economy, it has become a baseline service. From getting personalized recommendations on Netflix to generating clothing combinations on e-commerce websites, consumers expect seamless and personalized experiences at every touchpoint. However, it is not always easy to achieve those set expectations as customers interact with a brand through a multitude of devices, touchpoints, emails, and sales channels. It is therefore important that data systems talk to each other to create consistent customer journeys and marketing messages that appeal to your target audience. When user experience personalization is performed by ML models, it not only automates the whole process but also solves several common problems such as the “popularity bias” or “cold start” that tend to dilute the customer experience. Over 62% of customers have reported a propensity to stray away from a brand that delivered unpersonalized content to them, as personalized content makes consumers feel that they are personally attended to. All businesses today must embrace ML to look for opportunities that provide a better understanding of the customer's preferences and interests to deliver timely and relevant content to them.
3. User Behavior Analytics
With e-commerce organizations investing significantly towards strategies to enhance business performance on the internet, it is imperative that they know as much as they can about their customers. Traditional methods of measuring user behavior on the web fall short of the organizational demand for effective evaluation. User behavior can include anything from site interactions such as clicks. As user behavior tends to be complex and varied across different websites depending on the target audience, organizations need specific information about their users. With the advances made in ML theories, ML models are capable of handling large amounts of data and can self-evaluate and adjust their parameters from a data set of all coherent and necessary information. ML algorithms can be used to do so much more, such as perform sentiment analysis that provides better insights into the mind of the visitor. Studies have been conducted to test the effectiveness of ML algorithms in understanding online behavior and detecting malicious users and separating them from legitimate ones. By adopting data-based behavioral marketing, organizations can adopt new and efficient strategies based on the recorded information that represents the activities of potential clients.
In a recent use case of applying ML algorithms to solve business challenges, Quantiphi helped an American bank build a model to predict the purchase, refinance and churn probabilities for mortgage products. The client wanted to leverage data from multiple sources including clickstream, account transaction, and Google Ads Customer Match API to reach out to its users. Quantiphi mapped the probability scores against different customer identities such as user login ID and WebID so that the output can be used to engage with customers across channels. The resulting solution led to a significant increase in cost savings due to marketing optimization and a 25% improved efficiency of the model. Quantiphi also created profiles and segments of people seeking mortgage services from banks to allow enhanced retargeting and customer experience. This not only solved the challenge of high data imbalances but also stitched data across clickstream and other sources.
Quantiphi has been at the forefront of curating solutions that effectively solve business challenges and improves marketing RoI across industries. To know more about Quantiphi’s abilities and get your organization’s digital transformation journey started, contact us.
