From the SMX West Conference in San Jose, California, March 21-23, 2017. SESSION: Brick & Mortar's Secret Weapon: Search Data. PRESENTATION: Brick & Mortar's Secret Weapon: Search Data - Given by Andrew Ruegger, @aruegger - Catalyst, Senior Partner, Head of Data Science. #SMX #13C2
20. #SMX #13C2 @aruegger
Color Demand by Market
Source: Google Adwords + Keyword Planner API (MSv + Impressions)
21. #SMX #13C2 @aruegger
“So different markets are interested in
different colors at different times? I want
to apply this to our supply chain and in-
store creative”
22. #SMX #13C2 @aruegger
Search Queries Go Creative
Source: Search Query Report by Location
LA Market – Interest AroundWhite Nail Polish
23. #SMX #13C2 @aruegger
Source: Search Query Report by Location
LA Market – Interest AroundWhite Nail Polish
“Love neon, pink, and orange, on
white nail polish in the summer”
Search Queries Go Creative
24. #SMX #13C2 @aruegger
Source: Search Query Report by Location
LA Market – Interest AroundWhite Nail Polish
“Love neon, pink, and orange, on
white nail polish in the summer”
“shiny top coat that doesn’t chip”
Search Queries Go Creative
25. #SMX #13C2 @aruegger
Source: Search Query Report by Location
LA Market – Interest AroundWhite Nail Polish
“Love neon, pink, and orange, on
white nail polish in the summer”
“shiny top coat that doesn’t chip”
”beach and flip flop fantasy”
Search Queries Go Creative
26. #SMX #13C2 @aruegger
§ Created data feed to clients logistic analysts for “intent trends”
§ Areas over indexing in particular colors were anticipatorily shipped additional
palettes
§ Created visualizations for keyword relationships and geo locational
trends for creative design use instore.
§ Resulted in more frequent trade / shelf changes
§ Specific creative tailored to the market interest were implemented in LA, NYC, BOS, &
CHI for Winter, Summer, and Fall.
Turning an Insight into Actions
35. #SMX #13C2 @aruegger
§ Retailer used categories driving foot traffic and past sales to optimize in-
store product locations, in each market for participating locations.
§ An average of 4-8% increase in sales per category repositioned
§ An average of 1% decrease in sales for categories relegated from prime real-estate.
§ An average of 6% increase in net sales per participating store
Turning an Insight into Actions
43. #SMX #13C2 @aruegger
“Lets sell to the nearby partner stores.
Can we figure out what products to push?
I don’t know what inventory moved in
Amherst”
44. #SMX #13C2 @aruegger
Stores
Closing Stores
Retail Sales
Online Sales
Search Interest
Google Maps API + Google Adwords + Keyword Planner API + Internal
Maybe Search Interest Will Help
45. #SMX #13C2 @aruegger
Stores
Closing Stores
Retail Sales
Online Sales
Search Interest
Google Maps API + Google Adwords + Keyword Planner API + Internal
Maybe Search Interest Will Help
49. #SMX #13C2 @aruegger
What We Gave The Sales Team
Primary
Secondary
Tertiary
Amherst
Holyoke
Longmeadow
Ludlow
Priority Product Matrix Priority Location Guide
52. #SMX #13C2 @aruegger
§ Search interest can be a good proxy for consumer purchases in store.
§ There was a 0.67 Correlation between Search Volume + Impression #’s & Sales after
post analysis done on the Amherst store location.
§ The sales team now uses search interest of brand and competitors as a
factor for negotiating instore product location.
§ The brand implemented new systems to get inventory sales updates from
partners for marketing and sales strategy.
Turning an Insight into Actions