Since time immemorial, businesses have collected and stored customer data for the purpose of improving marketing outcomes and sales outreach. What began as an exercise for marketers soon evolved into a basic enterprise requirement. With the arrival of Customer Data Platforms, organisations have been able to isolate their target demographic and talk to their customers/clients with a more personalised tone.  


When tracing the evolution of a Customer Data Platform (CDP), we inevitably stumble upon one of the oldest forms of customer data storage: the Customer Relationship Management tool (CRM). As businesses and establishments interacted with customers regularly, personal details pertaining to their physical selves (such as age, location, gender etc) were recorded and stored onto the CRM tool. This data was then utilised by marketers to create a sales play. 

Such CRM tools functioned as a single storage location for prospect data, which was used mainly by marketers and sales teams. With the development of cross-functional marketing teams and dedicated customer service teams, customers began to come in contact with different verticals of an enterprise, with the expectation that experiential conversations would be able to continue seamlessly. As the number of touchpoints grew and as the focus shifted to consumption rather than sign-up, it caused problems in the traditional CRM-driven sales closure mentality. 

Customer Data Platforms serve the fundamental purpose of being able to integrate data in order to create individualistic customer profiles. Such data could come from across the breadth of touchpoints the enterprise has, as well as the various steps in the customer journey be it pre-sale, post-sale, or during active consumption. This data could then be analysed to derive insights for multiple touchpoints across the customer journey. Marketers and customer success managers could now design campaigns that maximize conversions at various steps in the journey by taking action on the insights from the CDP. 

With time, the advantages of CDPs over regular data management tools have become evident. Given the CDP was a single store for all customer data, it became important that any conversation with the customer at any touchpoint could pick up from where a previous conversation ended by querying the CDP live. This brought in the expectation of retrieval of data in real-time at mass scale. Next, as marketers struggled with the volume of data, it became more efficient to build into the CDP the capability to segment behaviours and extract insights without requiring to move large data quantities using technical know-how. Finally, as volumes grew, the possibilities for individual targeting also grew, and so crucial data science methodologies for hyper-personalization were best instantiated within the CDP.  

Despite such rapid innovation in the field of CDP, a question still haunts the decision maker’s minds: To Buy or To Build?

Why is it more profitable to buy

Businesses have to focus on the basics when deciding on whether to buy or build a CDP. Cost, time and human effort are some of the leading determinants that either encourage or discourage companies from undertaking the process of building their own in-house data management tool. In the past, many organisations embarked on a journey to build their own Data Management tools to help reduce customer friction and boost productivity. 

Cigna Healthcare, the fourth largest insurance provider in the United States, decided to overhaul its entire CRM database in 2002 with a vision to improve the efficiency of recording and disbursing insurance claims. The new system would migrate over 3.5 million customers onto a new platform and remodel legacy infrastructures. The 1 Billion USD effort would allow for a more integrated mechanism to register, process, scrutinize and settle claims. Fast forward a couple of years, and it didn’t pan out as expected. The delays in migrating customer data alongside inefficiencies in cross-referencing data related to processed payments resulted in delayed payouts. This led to a series of legal implications, which resulted in the company shelling out millions in settlements and losing close to 6% of its healthcare market share. [1] 

When building a customer data platform from scratch, it’s also important to consider the time taken to complete the project, notice tangible returns and perform routine operational tasks (i.e. the “bread-winners”) without setbacks. While one could argue that time is dependent on factors relating to scale and size, the entire exercise could still take months or even years. Moreover, the build-out of the platform is itself not the end-goal but it is in using the platform to deliver value for the business that the returns accrue.A data platform that isn’t giving results is just another storage location. Many enterprises have gone down this road of ‘data lake purgatory’, where simply dumping and storing data in a cheap storage powered by open source technologies like Hadoop masquerades for success only for a limited time. 

Furthermore, the costs pertaining to hiring the right technical minds to handle and manage the platform, training and regular maintenance further adds to the TCO.  Customer analytics involves analyzing behaviours of millions of customers over thousands of data points each, in essence a big data problem. Crucial data science talent is in short supply world-over and even more so when needing to apply the same techniques on large data. Most data science exercises run as experiments which may or may not be effective. Any data science practitioner would agree that a ratio of 1 successful outcome for every 8 to 10 experiments run is the norm. The cost of iterations and arriving at a meaningful outcome for business is therefore already multiplied by an order of magnitude. Coupled with this, there is the intangible cost of hiring and retention given the very same talent is in short supply. Almost every enterprise wants their hands on such talent but the prime supply goes to the well-funded startups and unicorns. With retention comes the problem of hiring new talent and training them afresh on the domain and letting them make the same mistakes as their predecessors.  

The car-rental industry is hyper-aggressive, as thousands of companies are competing with each other for a share of the 45 Billion USD pie. Companies operating in this sector are forced to wrestle between direct bookings on their corporate websites and through third-party travel portals. The portal bookings were subjected to a vendor fee, which ate into the rental car company’s profits. By deploying a turnkey CDP platform, a leading car-rental organisation was able to execute a Direct-to-Consumer (DTC) initiative to increase revenue from direct bookings. When a potential customer visited the website, the platform would record and observe interactions to gauge customer behaviour patterns. By looking at the customer’s browsing history, the platform would deliver tailored experiences on the website to nudge the customer towards booking a car (either based on travel plans, car-class preferences, price etc.). The car-rental company saw a 25% increase in sales during the first 3 months. [2]   

To settle the question of “To Buy or To Build” then, here’s one more statistic: 78% of organisations either have built or are building a CDP, but only about 19% claim that these tools deliver any form of marketing intelligence. [3] Given the proven successes of a CDP, the global CDP spend is projected to grow at a CAGR of 34% by 2025, to about USD 10.3 Billion. [3] 

The CDP is here to stay, so ride on top of it, as opposed to racing it alongside.

Cadenz: Success Metrics for a successful CDP

Cadenz, by TheDataTeam, is an intelligent CDP that uses Artificial Intelligence to equip organisations with all the necessary data and insights to create a 360-degree view of every single customer. 

Built by TheDataTeam, Cadenz understands customer behaviours and purchase patterns to give intelligent recommendations for business excellence. A leading telecommunication company in the ASEAN market managed to track over 500+ behaviour based attributes of over 90 million customers. Hence, the enterprise was able to generate a comprehensive view of its subscribers’ purchase history and browsing behaviour to build complex behavioural models. TheDataTeam’s CDP also focuses on minute details such as brand affinity, location preferences and relevance. By processing all of this information, they were able to monetize their sales through designated partners with trust. 

About the Author:

Nikhil Menon is a B2B Content Developer, who currently works for TheDataTeam in Bengaluru. Specializing in topics surrounding AI, Machine Learning, Blockchain (LBT) and Innovative Tech, Nikhil has worked with Fortune 500s, SMEs and Government bodies. As a speaker who has addressed professionals and students, In his free time, you’ll find him watching historical documentaries.

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