Ecommerce & Attribution Case Study

Posted inCase Studies

The Challenge

An eCommerce coffee company was experiencing stagnant sales (at approx. $20M) and increasing customer acquisition costs as a result of rising competition.

The Solution

PIN provided support in the following areas:

Target Audience Analysis

Subscribers vs Non-Subscribers

We began by focusing on new, cold audience identification and activation through novel attributes producing attitudes, motivations and affinities in addition to standard demographics. We accomplished this through data enrichment and audience DNA Reporting, in addition to Principal Component Analyses (PCA) and this resulted in audiences featuring Crossfitters, DIYers, Hunters, Snow Sport Enthusiasts and more. 

Home Owning American Providers

  • 31-35 year old male of American descent
  • Married with children
  • Owns a single family home
  • Lives in a large metropolitan area
  • Has some college
  • Blue collar Republican earning $50K-$75K
  • More likely to own a used truck than new mid range vehicle
  • Interested in Health & Fitness program
  • High interest in DIY home renovation and invests in home improvement
  • Unlikely to be a deal seeker or recreational shopper
  • More likely to stream TV than have cable subscription
  • More likely to utilize digital newspaper than traditional newspaper

Multi-Channel Attribution Modeling

The client relied heavily on last touch reporting models through disparate sources. In doing so, they experienced jaded results and “double counting” as the big tech companies aimed to take credit for producing a desired action. In building first touch, last touch and multi touch models, we were able to illuminate the divergence occurring throughout a given conversion pathway and better allocate marketing dollars to create efficiencies and decrease the customer acquisition cost.

Marketing Deployment

After identifying new audience segments and determining the ideal marketing channel mix, we built campaigns and messaging designed to drive net new customers who were previously neglected with the products that they were most likely to purchase. This resulted in substantial increases in sales from new customers and growth in market share. 

Current Customer Nurture Strategy

The client was manually deploying email campaigns multiple times each month and was not seeing a strong return. PIN monitored user journeys and failed conversion pathways to identify triggers on the site that could be tied to automated email and SMS outreach. This included new campaign logic and overlays of machine learning. This process was designed to more effectively engage our recipients and present them with product offerings when they had the highest propensity to engage. Some examples can be seen below:

  • Second Purchase Series
  • Product Replenishment
  • Predictive Winback
  • Gift Purchase Reminder
  • First Purchase
  • Emerging Best Customer
  • Best Customer (Frequency of 5) – Social Push

The Results

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