Thursday, January 3, 2013

Retail marketers: how to turn data into cash

Most retailer marketers are sitting on a mine of unused big data. This kicks off a series of how-to-guides for constructing agnostic strategies around big data for the purpose of improving conversion.
Big Data has saturated the news cycle in 2012. But what exactly is big data, who is using it and how can your brand apply it?

A. Set up fundamentals to track as the shopper checks out

Step 1. Store these, upon checkout

Store the total transaction amount within the shopper’s cookie. If possible do a real-time lookup to your analytics package to call the average order value for the last 14 days, store this in the shopper’s cookie as a separate variable.  

Step 2. Distance to AOV

Run an equation to calculate the margin of difference between the shopper’s total transaction value and the 14 day AOV. Store the outcome in the cookie as well. 

Step 3. Define buckets for different budgets

Are there three common total transaction values which your shoppers cluster around? Perhaps a Low, Mid, High? The quantity of budget buckets you define will vary on the nature of your product offerings and their price points. 
For example if your AOV is $80 perhaps setup three buckets such as:
  • DistanceToAOV = "Ontarget" // if shopper’s total transaction is between $65-95.
  • DistanceToAOV = "BelowAOV" //  if shopper’s total transaction is less than $65.
  • DistanceToAOV =  "AboveAOV" //  if shopper’s total transaction is above $95.
Early on, keep it simple, you can always add more buckets later to segment further.

B. Target the shopper with relevant experiences during future visits

Step 4. Track category affinity

Using a propensity score, identify which product category a unique visitor is interested in.
Perhaps the simplest propensity setup should merely total which category the visitor generates the greatest amount of page views. Last category viewed is also a worthwhile approch to begin with, ie: Handbags. 

Step 5. Display relevant merchandize, within shopper’s budget

Combining what is being tracked between step three and four, on the homepage you could promote (inject) relevant merchandize from the category of greatest interest. 
Based on the DistanceToAOV variable, promote merchandize on the higher/lower end of the pricing spectrum, whichever is appropriate to the unique visitor. 
With tools like Monetate or Adobe’s Test and Target, if you were Kate Spade’s ecommerce manager, you could predefine creative for each bucket, ie a handbag on sale, a moderately priced handbag, and a premium handbag. 
Based on the setup described above, the visitor would be targeted with handbags reflective of their spending history.
The outline above is simply a starting point to inspire creativity. Please elaborate on it. I’ve intentionally painted with broad strokes to maintain a vendor agnostic prescription.  
Yes! There are an indefinite amount of profiles and methods to target visitors with This is not 100% fool-proof.  I would recommend you work with a landing page optimization consultant to transcribe the above concept into a suitable solution for your business.
The biggest takeaways are:
  • Relevancy lifts conversion rates. 
  • Retail marketers have a gold mine of unstructured big data, use it.
In the continuation of this series I will discuss how to drive social influence and audience modeling by big data for the purpose of generating conversions.


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