If there is one sector that has been massively affected by the onslaught of e-commerce retailers, it is the Consumer Packaged Goods industry dominated by the likes of leading FMCG companies such as Johnson & Johnson, Unilever, Kimberly Clark and Procter & Gamble. Now that the likes of Amazon and Flipkart have become mainstream, big box stores and CPGS are battling an uphill battle to better understand their customers. The profound disruption wrought by e-tailers and the rise of millennials have created significant opportunities for CPGs to take steps to transform into digital first and customer centric businesses. While CPG companies are battling a loss of customers on one hand, they are also faced with questions who are my customers and how we reach them better.

Seeking customer -centricity: Microtargeting the right demographic

However, seeking customer centricity starts by understanding the customer base better. More and more consumer packaged goods manufacturers and retailers are finding that micro-targeting strategies (usually advanced predictive analytics) applied by ecommerce businesses —are unleashing new, deeper insights into their targeted customer segments and allowing them them to grow brands in a profitable way. Putting a laser focus on customers starts with leveraging predictive analytics that is turning into game-changer for businesses.

According to global information, data and measurement company Nielsen, in times of this challenging economy and intense market competition, an increasing number of mainstream consumer packaged goods (CPG) companies and retailers have dug deeper into their petabytes of data to develop a clear understanding of consumers in their categories and sub-categories. So what are companies doing to gain deeper customer knowledge – turning to analytics technologies. A recent KPMG survey points out that 30% of CPG companies are deploying cutting-edge data analytics tools for accelerated growth. Over the years, the use of predictive analytics has doubled from 24 per cent to 49 per cent. Some of the analytical tools used increasingly are real-time tracking systems, scenario modeling and micro-targeting capabilities.

Analytics India Magazine lists down ways to micro-target consumers

Begin by creating your own dataset: Most big companies have datasets in-house collected through  online surveys or subscriber email lists. Datasets can also be bought from companies that specialize in selling certain data to be loaded in the analytics database.

Once the dataset loaded, one can analyze consumer’s buying behavior and find hidden patterns and insights: The company can build a model accordingly based on consumer’s similar traits

Create a dependent variable: Variables may vary from gender type to residence to users who share the same devices or interest. By creating a variable, you can club the targeted segment under the same umbrella.

Further on, build a predictive model with the same variable:  It is this statistical process for determining the strength of a relationship between one dependent variable and a series of other variables that is known as regression modeling.

Power to predict valued shoppers

Today, retailers and CPGs spend huge amounts of money in promotions to draw more customers and many CPGs leverage consumer response models to help retailers understand how users will respond to marketing activities. Understanding the user’s needs and preferences forms the basis of better business. A model can be built to accurately identify patterns and  classify  based on  certain  static  parameters  using  Support  Vector  Machine  algorithm  of Machine Learning on a set of user data. It enables  to  provide  predictive  insights  based  on behavioral data leading to efficiency and responsiveness to marketing efforts.

Use Case: See how Mahindra Holidays & Resorts has created micro-segments of customers to better engage users

For Mahindra Holidays & Resorts, one-to-one engagement with customers was the key to retaining memberships. The company wanted to better understand its individual members and their preferences to create highly relevant engagement platforms. The key challenges for the organization were:

• Identify all member data  sources  to build a better understanding  of members
• Optimize the big data platform to create micro segments of members to drive relevant communication

The solution framework involved leveraging collaborative filtering model to identify the travelers who were most likely to visit the resort during the monsoon period. The big data solution further helped divide the members into micro-segments based on the resort they were most likely to visit and send targeted promotions. Based on the insights, the company sent customized emails and SMSes to members and recorded a 24% open rates for campaigns.

Outlook

As new channels emerge – etailers, niche ecommerce specialists and third party aggregators such as Urban Clap, CPG companies need to fundamentally reimagine and restructure their selling process and engage consumers better. They also need to better understand the users’ consumption patterns and leverage the right tools and technologies to understand how the consumers’ purchase decisions are getting disrupted with the rise of new-age providers. As the share of consumption rises, they need to understand how premium products are taking off across categories. In a hyper-connected world wherein internet connects billions of devices, having a laser focus on the customers have become a sort of corporate blind spot. It’s here that advanced analytics techniques and machine learning algorithms are applied to on micro-segments of users who can drive further conversions.