Customer Data Management (CDM) enables you to make new customers and keep current ones satisfied with your products and service. Businesses use data about customers for targeted marketing campaigns and to develop products and services tailored to their customer personas. Customer data management also tracks customer communication methods to ensure they are effective.
An effective database needs to identify the type of data to be collected and ensure its value. Analysts then split this data into four key segments: Identity, Quantitative, Descriptive, and Qualitative.
Identity data enables businesses to profile customers and potential customers to reach them effectively. Quantitative data tracks the customer life cycle from discovery to purchase. Descriptive data is all additional demographic information that further outlines customer personas. Lastly, qualitative data describes the reasoning behind customers choices.
Your marketing team can only calculate metrics like Customer Lifetime Value (CLTV) with a fully functioning CDM strategy.
The customer’s path to purchase can be long and unpredictable, with multiple touch-points, numerous devices, around-the-clock consumption, and participation both online and offline. However, business can improve sales with relevant data collection from each stage of the customer journey.
In the end, one underlying factor begins to stand out: for a CDM system to produce results, data collection from various sources needs to be coordinated around the customer rather than a channel or device. In other words, businesses need a holistic view of individual customers called the Single Customer View (SCV).
Businesses increasingly rely on machine learning programs for customer care. These programs have demonstrated their efficiency and convenience for both customers and businesses so their influence will only continue to grow.
Machine learning programs use customer data to predict sales and resolve customer issues without human oversight. For example, chatbots simulate human interaction with customers and resolve simple inquiries right away. ML programs also learn which responses produce the best results and when they should gather information from customers to send reports to customer service agents.
Similar to chatbots, businesses use virtual assistants to help customers with various inquiries. Unlike chatbots, virtual assistants do not simulate human interaction—though they also use ML programs to improve their customer responses.
Machine learning also helps businesses tailor content on their websites to current and potential customers. Many web users claim they can’t find content they were looking for in the Help sections of websites. AI programs analyze data in support tickets to suggest ways agents can update their Help articles.
Similarly, AI machine learning programs enable businesses to personalize the customer experience—in particular, by providing recommendation engines.
Another example of the use of AI in CDM is predictive analytics. In this application, AI analyzes customer interactions to forecast business futures.
The future of CDM lies in advances in speech technologies and emotion detection. Data enthusiasts also expect advances in multi-modal models that combine audio, textual, and visual inputs that allow customers to show customer service departments exactly what they want or need.
IBM Watson – IBM offers the Watson Assistant. This IVR system uses NLP technology to change the way companies interact with their customers. According to IBM, the Watson Assistant reduces the need for consumers to speak with human agents to solve problems quickly.
Amazon Web Services – Companies can create intelligent virtual assistants for their needs with enterprise services like Amazon Lex and Amazon Polly. Amazon Lex uses deep learning and NLP and Amazon Polly turns text into lifelike speech to best help customers.
Verint Next IT – Conversational AI software helps businesses of all sizes deliver better customer service and reduce costs with custom-made NLP-based chatbots.
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.