R Systems’ C2C solution is geared towards organizational growth by increasing the effectiveness of their omni-channel marketing efforts, generating more sales, tracking compliance and creating customer loyalties by aiding customer service across various stages of the buyer journey.
The first step involves understanding the overall customer base via Customer Segmentation. This is a process of dividing a broad customer-base/business market, normally consisting of existing & potential customers, into sub-groups based on some type of shared characteristics.
Based on the type of business, the two broad categories of segmentation -
Now that we looked at how Campaign to Closure helps segment the customer base, the next step is to identify which of these segments to target for marketing – the use case that is popularly known as Lead Generation or LeadGen in short. There are various ways to target a population to generate leads – can be based on age, occupation, gender etc.
But, the reality is that there’s no “one-size fits all” model for LeadGen, or for any one of the other C2C use cases for that matter. And just like the other use cases, C2C provides multiple models/options in LeadGen too. When we prepare to deploy, we analyze the best fit model for our clients.
The next step after determining who to contact, is How to contact them! Enter, Channel Analytics – C2C’s next key use case!
The target market for any B2B product or service is not one homogeneous mass. Rather, it can be divided into several distinct groups based on who they are, how they behave, what they want or what they think. In the Campaign to Closure solution, we have 3 main approaches to B2B segmentation:
Similarly, we have four basic segmentation approaches for B2C Segmentation -
In this option C2C incorporates customer channel preferences through which they would want to be contacted. These channels typically include phone call, text message, email, direct mail etc. Essentially this is a rule based model to define a set of channels of each customer.
As the name suggests, in this approach, we typically design and conduct a market research surveys in which a uniform mix of candidates are chosen to take the survey. The advantage with this method is that we can control the representation of certain sections of the population by oversampling or under sampling techniques.
However, this approach heavily relies on assumptions around the absence of any kind of bias in the survey questions and sample. Also, since this data is self-reported, there might not be a concrete method to verify the actual truth.
This is the flagship solution offered by C2C. In this method, we identify similar customers and determine the most appropriate channel based on this similarity. We use multiple techniques like Euclidean distance, Cosine and Jaccard similarity coefficient to measure the similarity between customers. Then we build a collaborative filtering based recommendation engine to essentially recommend the best channels for marketing. This helps offer personalized experience to customers.