About the Client
Major consumer goods provider
Retail Stores
Online presence with broad retail product placement
CASE STUDY
Enabled the development and maintenance of loyal brand relationship using TeraCrunch's Predictive Marketing Analytics Product.
Overview
An enterprise in the consumer goods business desired a back end software system to understand consumer behavior and improve loyalty. TeraCrunch's Predictive Marketing Analytics Suite was integral in helping the client determine its relationship with poten- tial and existing customers throughout the consumer lifecycle. With two customized analytics products from the TeraCrunch Socratez™ Insightz Software Suite, Customer Acquisition Analytics and Customer Expansion Analytics, TeraCrunch helped the client develop and maintain a loyal brand relationship across a range of products.
1. Business Challenges The client wanted to develop and maintain loyal brand relationships over a wide range of products offered by it. The client needed to identify the next likely customer and its relationship and engagement with existing customer.
2. Strategic Issues Within the highly competitive consumer relationship goods industry, the client wanted to know how to acquire new customers to make products and businesses work and engage and retain existing customers to build a loyal relationship with them.
>> Target Prospects
Identify prospective customers matching business objectives using specialized research, customer psychographic science and analytics tools to segment consumers.>> Customer (Loyalty) Expansion
Building a base of loyal and retained customers by using business capabilities, skillsets, practices and processes to move business forward.
Challenges
METHODOLOGY AND TOOLS
TeraCrunch created a multi-dimensional approach that drew data from different sources including past purchase data, customer profile data, market research data and customer behavior data.
TOOLS
TeraCrunch customized two analytics products from our Socratez™ Insightz suite to cater to the needs of the client
>> Customer Acquisition
TeraCrunch customer acquisition analytics transforms data into actionable insights so as to anticipate what customers want and determine the most effective ways to improve customer acquisition. The customer acquisition analytics solution enhances the ability to translate insights from data into actions that ulti- mately leads to significant business benefits for your consumer-facing marketing organization. It helps to translate data into actions that guide customers through the affinity building process, and matching them with the most satisfying products and services offered by you. The analytics product is based on:
1. Profiling customer-related data and clustering
2. Attitudnal and behavioral segmentation using Machine Learning
3. Acquisition response models
4. Customer value scoring and targeting models
5. Predictive modeling
>> Customer Relationship (Loyalty) Expansion
TeraCrunch customer relationship expansion analytics determines your relationship with existing customers. It predicts how to retain valued customers and expand the value of their relationships with you. The analytics product strategically drives business function to further improve each customer's experience with your brand. It helps you to discover how to drive a better customer experience so as to optimize every interaction and drive positive word-of-mouth momentum. The analytics product works by:
1. Analyzing customer loyalty
2. Developing propensity models to anticipate customers future behavior
3. Developing collaborative filtering models in order to provide cross-sell recommendation and up-sell modeling recommendation
THE APPROACH
TeraCrunch worked with the client to develop and maintain a loyal brand relationship with its customers in five stages:
Stage 1: Structuring data
In this stage, TeraCrunch structured the unstructured client data related to customer profiles and past pur- chases and customer behavior. We then developed dictionaries and ontologies based on the structured information that can be processed through predictive models.
Stage 2: Customer Profiling and Segmentation
During this stage, we segmented and profiled data of existing and potential customers. We started with the client's own customer data, such as location, purchases, spending volume and also appended additional consumer data through our strategic partnership. We then grouped the data into segments that share similar characteristics. Profiling process was followed by segmentation step. We analyzed how these distinct profiling attributes inter-relate to form important patterns and groupings among your customers.
Stage 3: Determining Relationship between Customer Experience and Products
We systematically gathered and used data related to consumer likes and interests and characteristics of their relationships to match with most likely client products in this stage. We analyzed customers' likelihood of responding to a particular product or service on a variety of ocassions.
Stage 4: Knowing the purchase context
We used purchase history, in conjunction with a graph of their social relationships, to anticipate likely purchase behaviors. We developed cross-purchase correlations for every customer cluster. We also analyzed interests of customers towards potentially effective offers offered by the client.
Stage 5: Understanding the purchasing behavior
We analyzed associated purchase decisions with triggering environmental characteristics that may be personal, social, cultural or psychological. We determined the factors that affected consumer demands and behavior.
IMPACT ON THE BUSINESS
TeraCrunch increased revenue by 3-5%. Our customized analytics products had an enormous impact on the business. With the insights gleaned through the data, the client was able to develop and maintain a loyal brand relationship over a wide range of products. As an added benefit going forward, the company was also able to use the enhanced analytics capabilities to acquire new customers and engage more customers by providing up-sell and cross-sell recommendations.
