“Creating exceptional customer experiences.” Companies in all vertical industries are embracing this mantra as means of coordination of efforts to boost sales and productivity.
As per a study done by McKinsey, it is expected that customer experience leaders can see an increase of sales (5-10%) and cost reductions (15-25%) within 5 years. These are the sort of figures that can really turn the stakeholder’s and make the C-suite pop the bubble.
Data is a vital part of providing superior customer service. We are aware of the fact that bad data can create negative experiences to the customers.
Data enrichment is the method of integrating new changes and knowledge into existing databases to increase accuracy. Applying these to existing data, makes it possible to make better marketing and business decisions through trend analysis, knowing customer needs, identifying pain-points, competitor analysis and differentiating the products/services in the market.
Customers want to be handled as if they are exceptional.
They expect companies to know, appreciate and remember them. Delivering on that expectation needs a lot of customer data.
Enriching data and knowledge of customers can help enhance services, develop loyalty, and offer better customer experience leading to customer satisfaction with the service/product/brand.
Long-term customers can be acquired through enrichment of Data!
Essentially, data enrichment helps you know your client better so that you can personalise interactions, deals and solutions more specifically in order to provide better support and sales.
Gaining insights into customer details such as the following can help in Consumer Segmentation and bring out the best results from marketing, sales, and customer service resources by targeting customers personally with relevant messages.
In addition to these basic details, here are 4 types of data that can enrich Customer-Relationship-Management (CRM).
Data enrichment requires machine learning and Artificial intelligence technologies to create data models for validation that breakdown data from complex structures into actionable data to decide on marketing strategies for the business.
Below is the exact flow of work to collect the necessary data from demographic and location data of the customers.
Annotation- Performing quality annotation of the data (text, image, video, audio) to produce a relatable dataset for data enrichment.
Recognition- Training the model dataset for ensuring the recognition of objects (static/dynamic).
Segmentation- Reducing the dataset by segmenting the required data from complex data by labelling and categorizing the data.
Transcription- Ensuring accuracy through optical Character Recognition (OCR), image transcription and other machine-learning models to structure the data for validation.
Comparison- Comparing and de-duplicating data to ensure quality for a good validation process.
Classification- Tagging images, text, objects and categorizing them based on the requirements to produce a model for validation.
Appropriate technology must be able to interact with different customer data and CRM systems, promote data governance programs, simplify compliance with regulatory mechanisms such as GDPR,and help employees continue to fill customer records with reliable,helpful and detailed customer data.
By Uma Raj
By Uma Raj
By Abishek Balakumar
Vaibhavi is a Digital Marketing Executive at Indium Software, India with an MBA in Marketing and Human Resources. She is passionate about writing blogs on the latest trends in software technology. Her passion further encompasses writing blogs on fashion, religious views, and food. Singing, dancing & mandala artwork are her stress busters. Sticking to the point and being realistic is her mantra!