One of the most important targets for any B2C model businesses is “Customer Satisfaction”. Be it through providing a better value for money, or through customized consumer support or by any other means of business.
But how does a business understand the finer aspects of individual customer persona, what the customer is trying to do, what have they done before, and how best to serve the customer?
Customer 360 is a process of creating a SINGLE VIEW of a customer by capturing various attributes across several different touchpoints and channels within the business, which helps in predicting the customer behavior .
Here is an example of a typical 360-degree customer view in the retail domain:
The above picture displays few of the channels and the attributes that are captured for a specific retail customer. Campaigning, Engagement and Loyalty are few of the trending channels where customer behavior can be understood better. The list of channels for another customer might look slightly different. For example: Say the company reaches out to its customer through marketing calls which may be different from the existing Engagement channel, this becomes an exclusive touch point for that company. In a similar fashion, the channels for Customer 360 for an Airline domain would look completely different. There could be few additional touch points like reward programs, interaction through entertainment devices on board, usage of paid Wi-Fi, purchase of food & beverages on board the customer feedback. While the channels and touch points vary, the need of collecting the attributes to understand the behavior remains the same across the domains.
Let’s say a retail company runs business both online and in store and has unique customers in both the segments and omni customers as well, the value of sale in these segments can be very different and are driven by several factors like marketing campaigns, product needs based on demographics, offers & discounts.
After companies reach a saturation point in acquiring new customers, it becomes essential to run sales through customer service and customer retention. This is where knowing the customer and predicting the behavior helps in accurately determining the appropriate investments required in better interaction with the customer and enriching the value through the customer touch points.
A Single View customer 360 simplifies the objective of data democratization by capturing multi-dimensional attributes that are accessible from a single source for various internal teams like marketing, IT, customer support, campaigning etc.
Here is a “Customer View” generated from Customer 360 information for a business user: Such a derived metric helps the business users to exactly get a view of the type of customers and the various segments they belong to, thereby enabling the business teams to make better decisions.
And finally, here are a few use cases that can be developed simply by using the data from Customer 360.
1. Customer life time value
2. Customer propensity
3. Churn prediction
5. Conversion/Cross Sell
6. Loyalty optimization
While the above uses cases can also be built without implementing C360, the engineering cost in moving data for specific use cases is redundant. And this is where C360 helps in optimizing the cost of data engineering for an IT stakeholder.
There are several off-the-shelf products in the market today which can enable businesses to implement a Customer 360 view at a faster pace. However, the decision of building an in-house platform vs. purchasing an off-the-shelf product often boils down to choosing customization vs cost.
Building a Customer 360 view in-house requires a relatively less effort for a company whose analytical maturity is already high.
Here are the steps involved in building a Customer 360 view.
1. Identify features
The first step involved in building this view is to identify the different customer touchpoints. Here is a list of attributes that would be required in the retail industry:
7. Customer support and other features more specific to the brands.
The features & attributes are tightly coupled to the business model and vary depending on the type of business and the domain.
2. Collect and consolidate
Given the nature of the business and its underlying architecture of implementation there could be several software and tools a company would be using to solve the specific IT needs.
For example: CRM, SQL & No SQL databases, blob stores, web traffic and analytical data systems, etc. Such varied tools pose a challenge in collecting and consolidating the data. While data collection is one aspect of the problem, what data to be collected is all together a different one and is the most important factor that determines the quality of the feature market. The data should contain hard and soft information.
Hard data is the information that is readily available within the datasets.
Example: Customer’s age, gender, location, support requests, emails sent, discounts applied, etc.
Soft data is the information which needs to be extracted from the given data set.
Example: Customer’s purchase value in the last 3 months, last interaction to an email campaign, preferred shopping segment based on previous transactions, number of purchases through social media ads, number of purchases vs browsed & saved products.
3. Create a Feature Market
A data lake is where all the raw information collected is stored and processed to create reusable features stored within a data warehouse. This activity is engineering heavy and is solved through big data solution implementations.
The reusable features to be created are driven by business problems and the ML models that need to be built to address the same.
Customer 360 view is often built using several variables created from these features and that is what makes it rich and highly reusable.
A Customer 360 degree-view is built over a period of time through constant feedback fed from the business teams, data from the customer interactions and through new channels which is what makes it a progressive implementation for a business.