Data Enrichment For Enriching Customer Experiences

“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.

Why Data Enrichment?

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.

Key Data To Collect That Will Help In Future

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.

• Age
• Marital status
• Occupation
• Affluence/income
• Type of residence
• Educational background
• Vehicle ownership

In addition to these basic details, here are 4 types of data that can enrich Customer-Relationship-Management (CRM).

1. Consumer interests: Every person has a set of interests that drive their actions. Location data offers a wealth of knowledge about their interests, hobbies, activities. Understanding these can personalize marketing campaigns to maximise performance. Demographic data can tell an organisation basic information such as age, gender and shopping history which can also enhance Customer Experience.
2. Brand affinity/ Loyalty: This term refers to the emotional bond that consumers have with a particular brand. It allows companies to ensure that their brand reflects the ideas of their target audiences. Location data offers an insight into the affinity of collecting data on the events that an individual attend, their retail locations as well as everyday behaviour and routine. Understanding consumer affinities establishes an emotional connection between the customer and the company resulting in long-term partnerships.
3. Visit patterns: Visiting pattern data not only shows a company how often a person stops at their store, but also tells the number of visits that are normal before a transaction. This data can contribute to making more informed decisions and targeting the prospective buyers.
4. In-Market buying behaviour: consumers who are actually ‘in-the-market’ are actively trying to buy a product/service. Knowing who your customers are, can help meet them at a time they are most likely to make a decision with the right message to convert them into a sale. Data for this may include buying history, past actions, visits to the store and ratio of the number of actual buys vs the enquiry.

How to collect all these data?

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.

6 Steps To Enrich Collected Data

1. Evaluate your Data : The issues that are unseen cannot be solved. It is important to first take stock of your data, see where the problems are and determine the extent of the problem. Data profiling can tell the number of records with null values, illegal values, or missing values to help detect possible duplicates.
2. Fix the issues : Merging duplicate information, filling out missing postal codes, correcting phone number formats, fixing misspellings are the steps to boost data quality. Most of this can be accomplished with technology, but some re-work will require manual intervention.
3. Enrich Your information : Add data that will add value to the customer relationship. There is no single blueprint for this mission, and the needs of the industry will obviously differ. (Customer data)
4. Look forward: Enriching data cannot be a one-off exercise. In order to continue delivering excellent customer service, you will need to create standard formats for classifying customersand structuring data records to meet the needs of several businessdivisions. Having a shared framework for all documents and information systems will help address problems of data duplication in the future, while also providing enhanced data to the users.
5. Train the employees: Enriched data is only useful if the people know how to use it. You need to train the employees to make the best use of data to fulfil customer needs and to keep upto – date customer records with details that will be useful in the future.
6. Using the technology : From data profiling applications to de-duplication programmes, and data mining tools, technology can help enrich data to better meet customer expectations

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.