A Customer Data Platform (CDP) is a single unified platform containing customer data collated from multiple sources. It can be integrated with existing data systems and helps map customer data with contextual data to deliver personalized customer experiences.
CDPs are different from customer relationship management software which typically use first-party data. While this limitation can be tackled with the help of data management platforms that have provisions for second and third-party data sources, CDPs go a step further in accurately mapping data points to define individual customer traits. Through advanced analytics capabilities, every step of the customer journey can be mapped out using CDPs.
There could be multiple reasons for customer churn and therefore, actions required to prevent churn may also be different for different sets of customers. To identify the appropriate action, it is important to understand all aspects of a customer. And this means identifying patterns in customers’ demographics data, engagement data, behavioral data on different platforms, transactional data, and customer experience data, among others. It is also important to use these patterns in predicting customer churn and taking pre-emptive corrective measures.
An in-depth analysis of data such as past purchases, service/customer care requests raised, history of products wish listed on e-retailing platforms but not purchased, deals offered to customers, etc., helps understand factors that are most likely to cause customer churn. Historical data about customers that have churned also helps identify trends and patterns resulting in churn. Once these data points have been identified, churn probability of customers can be predicted using Machine Learning models. Understanding the drivers leading to high churn would get us closer to identifying controllable drivers that need to be taken into consideration and help decide on corrective actions needed to increase CLTV. CDPs can recommend actions based on the current behavior of customers as well as predicted behavior.
This is especially important for ensuring consistent profitable business because research on customer churn has proved that "85% of customer churn due to poor service was preventable," and "67% of customer churn is preventable if the customer issue was resolved at the first engagement." The average churn rate in the retailing industry was reported to be 63%,  and companies that are efficiently employing customer data platforms reportedly observe “2.5 times more in customer lifetime value than those that don’t. ” Therefore, predicting patterns of churn and undertaking remedial measures to retain customers likely to churn can provide an unparalleled edge.
Globally, marketers are vying to make an impact on consumers as the competition gets more and more cutthroat, raising overall marketing spend. Studies reveal that the average cost of a retail lead, is reportedly $34 and the average conversion rate for a retail lead is 3%.  However, retail marketers who prioritize and invest in personalization can reportedly “realize upward of $20 in return for every dollar invested.”
With CDPs, retailers can implement perfectly targeted marketing campaigns. By collating historical data available on consumer preferences and behavior, as well as pertinent information collected from relevant third-party sources, retailers can accurately market sales, offers, new product launches, personalized to the likes and preferences of each consumer. This is because CDPs can track consumers throughout their journey and create a holistic 360-degree view with detailed individual profiles, and tailor ads to improve acquisition, engagement and retention. This can be done by segmenting customers based on their current transactional behavior, retail experience, historical customer engagement metricsand lifetime value. More dimensions could be added to align target audiences with each marketing action, in a more accurate manner and take personalization to the next level.
Insights from each marketing experiment can also help predict future customer trends and client responses. This helps anticipate customer needs by making the most of big data, ML model generated analyses and data insights. This further facilitates swift call-to-action, ensuring a seamless, holistic shopping experience to consumers.
Given that CDP pulls in data from relevant third-party sources, companies can also analyze and identify competitors that target customers usually engage with, and compare a competitor’s digital engagement with that of their brand. From these sources, retailers can also gauge brand perception on social media. A cohesive report of these metrics can enable personalized, targeted marketing of offers/promotions/products/services/in-store campaigns, etc., tailor-made to customer preferences.
Tracking consumer journey is vital for not just personalization but also to ensure that the customer experience is on par with industry leaders because reports suggest that almost 30.9% of retailers struggle to track consumer journey across devices, and “38.2% can only track some consumers some of the time. ”
To ensure that customer journey does not become fragmented, it is also important to facilitate seamless multi-channel experiences. Research has shown that omnichannel experiences are pivotal for optimum customer engagement as well as for improved AOV. A detailed study of approximately 2 billion messages pertaining to customer interactions proved that "The average engagement rate of campaigns using three or more channels was 18.96% across all channels, while single-channel campaigns earned only 5.4% ." Furthermore, "The average order value (AOV) of customers interacting with a single-channel campaign was only $58.70 on average, while omnichannel campaigns earned a 13% higher AOV. " Additionally, omnichannel experiences were also proved to be vital for long-term customer relations given that single-channel campaign marketers had a customer retention rate of 34.8% while “marketers using three or more channels enjoyed a 66.12% customer retention rate. ”
A robust CDP can track customer journeys across multiple channels and collate data from offline and online interactions in real-time and enable unique end-to-end customer journeys. Mapping out customer interactions is not only integral to the overall customer journey but also to provide context to customer decisions and this in turn, fuels personalized customer experiences, thanks to analytics insights. This process can also be automated through a CDP and issue immediate data points/inputs that can be fed into the customer’s profile for every step/ action taken on any of the retailer's channels. These data points and insights can be fed back into campaign efforts to enhance ROI.
For ensuring end-to-end, personalized, seamless shopping experiences, extensive data analysis is pivotal. But how many retailers are actually able to extract top quality data and integrate it efficiently with the intended platforms?
A study by Harvard Business Review showed that,
- On average, 47% of newly-created data records have at least one critical (e.g., work-impacting) error
- Only 3% of the data quality scores were rated acceptable by business leaders 
CDPs however mitigate this challenge as they are fundamentally created to ensure quality data integration from multiple sources, and additionally to ensure swift insight generation from these data points. The rapid integration capabilities help ensure that data quality scores remain top-notch and that insight generation is as near real-time as possible.
Additionally, data governance guidelines can also be fed into customer data platforms ensuring that customers are sure that their privacy and security is protected at all times. Not only does able data governance improve customer relations, but “Companies with a Data Governance program in place increase data analysis time by 2%.” CDPs can regulate who has access to what information on the platform, and thereby, ensure optimum privacy and security of customer data and mitigate the risk of data leakage.
Having a robust CDP also necessitates the need for an efficient data model in the backend and this setup becomes especially useful for multiple projects. As CDPs already have raw data available, a good data model can help fragment the data into tables and views and as needed. Such a system will prove cost-effective, reduce the need for redundant analytics efforts and help make the most of CDP investments.
CDPs are vital for retailers’ efforts to ensuring a holistic, bottleneck-free, customized customer experience.
If any of the indications discussed in the article mirror challenges that you are facing, then your business processes might warrant a closer analyses - and a robust customer data platform is likely to help unlock your firm’s potential for further growth and development.
If you are looking for more clarity and would like to discuss your business needs further, drop us a note here.
Mansi is a data science and analytics professional with 5+ years of diverse analytical solutioning experience. She has worked in enabling AI-powered, cutting edge solutions across industries like retail, CPG, digital marketing, hospitality, and much more. When not at work, Mansi loves taking time out for her artistic interests. She loves making greeting cards, scrapbooks, paintings, etc.