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What is customer data management (CDM)?
Customer data management is defined as an enterprise process by which customer data is gathered, stored, updated, accessed and analyzed.
Customer interaction with brands can come from multiple channels across online and offline modes. These include store visits, social media interaction, website visits, digital asset downloads, targeted ad responses, survey responses and so on. Companies that intend to stay ahead in customer-centricity, also intend to unify this data from multiple touch-points to create a single customer view with the most up-to-date information on the latest points of interactions.
The end goal of customer data management is to ensure that the data that is used by customer-facing teams, be it marketing, sales or account management, is the most updated, well-structured and redundancy-free for maximum benefits. This is the same data that is also fed into marketing automation platforms and other customer-facing software that have a direct impact on lead generation, nurturing quality and closure-time; in other words, company revenue.
Moreover, at a time when customer interaction with brands is increasingly digital and omnichannel, the biggest challenge for brands is data hygiene. According to Forbes, in 2019, only 23% of businesses said they could reliably depend on CRM data for important decision making. With increased digitization since the COVID-19 lockdowns, companies that caught up with subpar customer data management practices saw their growth hampered by data redundancies or an inability to capture and unify all interactions online and offline. On the flip side of this coin, companies who had already executed data management principles, were at a huge advantage and saw their balance sheets expand rather than shrink. Data management today in 2022 is a cornerstone for both growth, and disaster management and recovery.
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Customer data management can be explained as a three-step process to reach the final goal where data is no longer stored in silos and is unified for the company and its employees to launch orchestrated customer-facing campaigns and experiences. Here are the 3 key steps:
Step 1. Digitize and capture
The first step to manage any data efficiently is to ensure that its entry points are digitized and automated for the most error-free data entry. These points of entry could range from online forms filled on your website to physical store purchases. The goal is to ensure that all possible points of brand or enterprise interaction are digitized and accurately captured.
Step 2. Storage and update
The data captured across any interaction with the customer needs to be fed into a storage software, which is typically a CRM, survey software or marketing platform. At this step, data is stored and updated but still siloed and not centralized. Therefore, the data updated is only in the specific platform and not updated across all platforms and departments.
Step 3. Connect and unify
This is the step for unification of data for complete customer data management. The connection can be done using API links to communicate across apps, or even better done using a customer data platform or data management platform for advanced analysis.
Factors affecting customer data management
There are several factors that affect the quality and end result of customer data management. Some of these key aspects are:
Factor 1. Level of digital transformation and automation
The stage of digitization is a single biggest factor affecting a company’s ability to capture, update and store customer information. For instance, in a simple scenario of a brand operating a brick-and-mortar store, are product sales captured using bar-codes or is input manually? Is customer experience survey manual or online? Is the survey sent automatically based of interaction triggers or needs to be manually sent by staff?
By a thumb rule, the more the level of digitization and automation, the less chances of any human error occurrence and more accurate is the data capture and updates. It is not so much about staff discipline, so much as it is ensuring that human resources are focused on decision making that improve customer experience, while software and automation can take over repetitive tasks that can be done more efficiently and in an error-free manner.
Factor 2. Sales and martech stack
The captured data from customer interactions have a single goal- to unify and provide the best data input for marketing and sales operations. The whole process of capturing and feeding data into another platform is typically done using a customer data platform (CDP) or a data management platform (DMP) if a company can afford one, else it can also be done using app data exchange through APIs. In either case, what is critical is the sophistication of sales and marketing technology.
In other words, data captured cannot be efficiently utilized without having the right set of technology tools that can absorb and implement it in marketing campaigns. A typical example of a sales and marketing automation tool will be Hubspot. For email marketing specifically, be it Hubspot or MailChimp or any similar platform will need demographic data to either be uploaded or fed through another app such as a CRM, via API links. Only then can the email automation platform take over email deliveries, capture and analyze response data and feed it back into another system.
Factor 3. Ability to capture data across all touchpoints
A business can have several modes of interaction with customers. The number of touchpoints that have been configured for data capture and the depth of the data captured are key factors for the quality of customer data viewed and analyzed by your teams and other systems.
Missing information from customer interactions lead to incomplete views of a customer’s stage of buying journey or post-purchase experience. It is therefore critical to identify all possible customer touchpoints during the early stage of customer journey mapping and make it a map for customer experience planning. This will make it easier to digitize and start capturing data from these missing links. Ofcourse, a business will always keep the cost-to-benefit ratio in mind during any investment, including digitization. But if Covid lockdowns have pushed businesses in any direction- it is digitization.
When do you need customer data management (CDM)?
Technology directors and decision makers need to know when customer data management (CDM) is needed to bring about customer experience, marketing and sales campaign effectiveness.
Below are some of the key indicators for enterprises that they need to employ CDM practices:
When data is too large and siloed
Business-to-consumer companies have large data volumes vertically and business-to-business companies have deep data volumes horizontally. So whether it is a B2B or B2C enterprise, after a certain stage in their expansion, the customer data is no longer error-free or updated fast enough using human resources and a company employs software to capture the data.
However, the data is still typically kept in silos of platforms and departments and no process yet exists to unify the data. This leads to fractured marketing and sales campaigns and therefore, fractured results. This is when the company is ripe for a customer data management process to unify the siloed data for the most comprehensive and updated customer views.
According to a 2021 BCG report, companies that can deliver personalized customer campaigns can not only increase sales by 6% to 10%, continued personalization post-sales can also lead to longer customer lifecycles and better value per customer through increased upsells and renewals.
However, personalization happens in stages. A simple email which carries the name of the reader is one level of personalization, but ensuring that even the ads and articles served to customers on company websites are based on their needs and stage of customer journey, is a much more upgraded level of personalization.
The quality, accuracy and depth of personalization depends largely upon data management practices of the company. The level of sophistication of customer data management will determine the quality and depth of data captured, updated and stored, and this data is then used to deliver personalization.
Delivering omnichannel customer experience
If personalized experiences is the depth of a customer campaign, omnichannel experiences is the width. In other words, technology managers and directors need to ensure that customer experience, marketing and sales teams are equipped with the right data and technology to be able to deliver uniform experiences invariant of channel or platform of interaction with a customer. This is a shift from multi-channel campaigns where all channels are utilized but may not be uniform due to siloed data that exists in these channels and platforms.
Customer data management needs to now be employed to unify this data and finally enable teams to execute omnichannel customer campaigns.
Top 5 Customer Data Management (CDM) strategy best practices for 2022
Cultivating data-centric customer centricity
Enterprises today need to move past only data-centricity or only customer-centricity, and need to combine the two into customer-centricity that is data-centric. This means technology directors and customer-facing teams have to work together for data governance that is based on a customer-centric culture and aims to utilize the data captured for enhanced customer experiences across all channels, platforms and points of interactions.
A CDM strategy that includes a customer-centric culture ensures that all data is utilized with the aim to improve customer satisfaction, while data-centricity ensures that the data is captured from all interactions and is as fresh as technologically possible at their current levels of tech-maturity.
Investing in customer data security
Capturing customer data to orchestrate meaningful campaigns that enhance experiences is a great benefit for any enterprise and gains the company a good reputation and demand. However, a single slip in data security can undo all these benefits and can bring about negative attention from the media and law enforcement agencies.
With data capture on the rise, so are data breaches. It is the responsibility of the data security team to ensure that all customer data is captured and secured using encryption and/or security software.
Setting data governance policies
A data governance policy is a company-wide set of rules that govern how data is captured, where it is stored, and who can access and update it. Data governance rules are meant for all departments across the enterprise, and encompasses the governance of internal company data and external customer data. The goal of such a policy is to ensure compliance in data management across the enterprise to minimize risks of data loss, corruption, breaches and redundancies, while maximizing data capture, unification and utility.
Ensuring data integration across all customer-facing departments
An example of a common mistake and loophole in enterprise data management is employees purchasing customer-related software for a specific need and keeping it siloed. Often only a few people may be using this software, but the data captured in this software can be valuable across customer-facing departments and teams. This is typically the case, when data governance is weak or shallow and does not entail how a new software needs integration with existing platforms.
Ensuring data integration across all and new customer-facing software, preferably as part of the data governance policy itself, is key to ensure that any new customer-facing platform is immediately piped into the larger customer data management strategy.
Investing in platforms for complex data orchestration campaigns
Data orchestration platforms such as Data Management Platforms (DMP) and Customer Data Platforms (CDP) are used to manage complex, omnichannel and highly personalized campaigns to manage customer experience and/ customer acquisition campaigns. This includes syncing web-content recommendation engines, email campaign platforms, adtech platforms, CRM software etc., and delivering campaigns based on triggers and live data from across 3rd party or in-house software.
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