Lately, you may have been wondering “How do I get big data”?
Hold on, full stop please. Your brand is big data rich already.
Don’t believe me? Then ask yourself:
- What were December top selling products in Nebraska vs. Florida?
- Did we sell more products designed for men or women?
- What is our average order value (AOV)?
- What is the average time period between return visits?
If you don’t know the answers immediately, what would you do, where would you look? To your analytics package of course. Regardless of your brand’s product of choice, SiteCatalyst, Webtrends, Google Analytics or any other, the data already exists (while perhaps untapped).
The only question remaining: Will you let the data sit docile in 2013?
During my tenure with Adobe/Omniture I had a unique vantage point to monitor the health of all optimization engagements. I observed that most marketers struggle with translating heaps of data into actionable to-dos.
I realized that, retailers especially, would leave valuable mounds of data untapped. To overcome data-paralysis, I aimed to instill a culture of data-artists.
A data-artist is similar to a data scientist, but more of a creative thinker. Combining curiosity and tact, the purpose of a data-artist is to construct new strategies that exploit insights leading to profitable outcomes.
The first principle: ask great questions to isolate great insights. For example, the questions above generated big data, before asking them only uncategorized and unexplored data sets existed.
Secondly a data-artist should strive to deliver relevant experiences. Increased relevancy drives ROI for the retail marketer. I encourage you to seek-out the micro-segment, and then cater to what they are in-market for.
Turn data into cash
For onsite traffic, the retail marketer could experiment with targeting visitors based on their wallet. Marketing merchandize based on their spending habits is 1 approach to deliver a more intimate experience, reflective of the shoppers’ budgets.
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.