DEVELOPING AND USING CUSTOMER INSIGHT

What is Customer Insight?

A better understanding of your customer based on their:

• Behaviour
• Experiences with you 
• Beliefs or needs
• Analysis of qualitative or quantitative data.

This builds stronger relationships.

Big Data

he vast quantities of information produced digitally that traditional methods stuggle to analyse.

A collection of data from various sources, often characterised by what's become known as the 3 V's: VOLUME, VARIETY and VELOICTY. Over time, other V's have been added to descriptions of Big Data.

VOLUME: The amount of data from myriad sources

VARIETY: The types of data: unstructed, semi-structured, structured.

VELOCITY: The speed at which big data is generated

VERACITY: The degree to which big data can be trusted

VALUE: The business value of the data collected

VARIABILITY: The ways in which the big data can be used and formatted. 

About 90% of all data generated is 'unstructured' meaning it does not fit into an existing database.

The volume of data will help you make very good business decisions if analysed correctly and simplified.

Types of Data

1. Identity Data 

Name information: Title, first name, last name, designatory letters etc.
Person information: Date of birth, gender etc
Postal address information:  building number, name, address lines, town, country, county, post code etc
Telephone information: Home, work number, mobile number etc
Email address information: Personal, work email address etc
Social network information - Facebook, Twitter, Linkedin addresses etc
Account information: Details of your customers account ids or user ids
Job information: Company name, department name, job title etc
Permission and Suppression Data - Not distinctly an identity element of data, but equally imporant as the information concerning persmission to communicate and reason for not communicating (suppressions).

2. Quantitative Data

Transactional information (Online and Offline): Number of products purchased, actual products purchased, order/subscription value, order/renewal datas, product abandonments (abandoned baskets), product returns, etc.
Communication information (Inbound and Outbound): Communication date, communication channel, opens, click throughs, etc.
Online activity: Website visits, product views, online registrations etc.
Social network activity: Facebook likes, Twitter interactions, etc
Customer services information: Complaint details, customer query details, etc

3. Descriptive Data

Family details: Marital status, number of children, age of children, etc
Lifestyle details: Property type, car type, number of car doors, pet ownership, etc
Career details: Profession, education level, etc.

4. Qualitative data

Attitudinal information: How do you rate our customer service, how do you rate the value of the product, how likely are you to purchase the product again etc.
Opinion: Favourite colour, favourite holiday destination etc
Motivational: Why was the product purchased, what was the key reason for the purchasing of our product (locality, price, quality) etc. 

First, Second, Third Party Data

First Party Data:

• The most useful data, collected directly from your customers - most relevant and accurate
• Likely to give more accurate picture of your target audience than bought data which is taken from people who are most likely not customers
• You are the only one to own that unique data set unless you choose to sell it…