Predictive Analytics for E-Commerce

If you’re like most online retailers, you face these common challenges:

  • Competition in similar verticals
  • High marketing budgets
  • High cost of customer acquisition
  • High Customer attrition
  • Low profit margins
Assess your situation by answering the following questions:
  • How do I lower my cost of acquisition?
  • Which factors can drive my customers towards repeat purchase?
  • How can I increase the effectiveness of marketing campaigns?
  • Which assortment sales can improve my margins?
  • How can I increase average order value (AOV)?
  • How do I personalize my campaigns for maximum response?
There are many types of data available to you to address these challenges. In order to effectively target your marketing campaigns, you must effectively collect, analyze, and apply these data. Here are some potential data sources that you can leverage:
  • Social Media Streams
  • Referring Websites
  • Customer Transactions
  • Web Logs
  • Clickstream Data
  • Customer Demographics
  • Customer Service Logs

How are you currently aggregating these disparate data sources in order to optimize for sales? It is not enough to simply ask the data “what happened?” or even “why did it happen?” The most value can be derived from asking “what will happen?” (prediction) and then making it happen (prescription). This approach will move your focus from hindsight to foresight.

With a predictive model, you will have increased confidence as to when customers will abandon a transaction, accept product recommendations, enroll in a continuity program, etc.

Prescriptive models, based on continued observations of predictive data, will allow you to tune your campaigns for maximum conversion.

Staples, for example, executes over 1,500 multichannel campaigns annually based on over 25 million customer records. Jim Foreman, Director of Circulation and Analytics at Staples, reports “We did a financial analysis of the implementation, and we found that we were getting an internal rate of return of 137%”.

How would you like an ROI like that?!

Here are the phases of the analytics life cycle, and some example models I can help you use to make better decisions:

Customer Acquisition
  • Acquisition Response Models
  • Customer Value Scoring Models
  • Customer Profiling & Segmentation
  • Campaign Analysis
Customer Management
  • Profitability Analysis
  • Channel Analysis
  • Call center analysis
  • Market Mix Modeling
Relationship Expansion
  • Loyalty (attrition) analysis
  • Cross-sell/Upsell Modeling
  • Campaign Effectiveness
  • Product Analysis
  • Propensity Modeling
Customer Retention
  • Personalization
  • Product Affinity
  • Satisfaction & Loyalty Indices
  • Lifetime Value (LTV) Prediction
Voice of the Customer Analytics
  • Survey Analytics
  • Social Media Analytics
  • Sentiment Analysis
  • Promoter/Detractor Analysis
  • Text Analytics
How can you get from descriptive to predictive analytics, and take your campaigns to the next level?


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