1. First 6 Months
KRISTINE MARSHALL
JUNE - NOVEMBER 2015
Small Things
1. Customer Reviews
2. Donations / Goodwill
3. Promotions
4. Upsell & Exit Intent
5. SEO Maintenance
6. Bot Filtering
7. KPI Improvements
Big Things
8. Daily Deal
9. Feature Store Preparation
10. Sales Forecast Model
11. Overstock / Short Expiration
Special Features
12. Holiday Store
13. Halloween Store
14. Black Friday / Cyber Monday Stores

2. Small Things
Customer Reviews | Donations / Goodwill | Promotions | Upsell & Exit Intent
SEO Maintenance | Bot Filtering | KPI Improvements

3. Customer Reviews
When I started, no one was managing reviews on EntirelyPets, HealthyPets, or EntirelyPetsPharmacy. I took them over.
● Created timetable to publish feedback on regular basis
● Defined KPI & created reporting templates
● Launched Review of the Month, tie-in with social media
● Initiated platform upgrade for SEO benefits
Result: Customer feedback feature supports other marketing efforts.
Special Circumstances
● Kept track of customer “pulse” through CET & Ora-Clens short out
● Designed & implemented solution to qualify PHS product line for NASC seal
Result: CET/Ora-Clens complaints minimized. PHS approved for NASC.
next up: 2 of 14 | Donations / Goodwill →

4. Donations / Goodwill
Donation requests are frequent and each is unique. Because they require extensive back-and-forth correspondence with
hard-to-measure ROI, they were largely left un-attended. I took over the task of coordinating answers.
● Learn details, agree on terms, & obtain 501C3#
○ Details: usage (event prize, gift bags); how many; type (one-off, recurring), etc.
○ Terms: backlink, article feature, social shoutout, promo distribution, etc.
● Coordinate with team to determine suitable products & obtain approval for amount/value required
● Place order & coordinate with warehouse to include promotional material
All donations selected from short expiration SKUs already selling at cost of goods and projected to NOT sell out (i.e. these
SKUs are a straight up loss).
Result: Partial value of short expiration items recouped as tax write off. Exchange low-value items for high-value SEO
benefits or promotions. Goodwill & word-of-mouth advertising generated as bonus.
next up: 3 of 14 | Promotions →

5. Promotions
Promotion management is very tedious and time-consuming. Adding my efforts to the team doubled the people able to get
the daily tasks done. I now manage 95% of promotions for EntirelyPets and HealthyPets.
● Weekly promos: Daily Deal, feature stores, newsletters
● Monthly promos: Affiliates, house specials, overstock, short expiration, vendor requests
● Standards created (names, duration setting)
● Out dated promos disabled, old promos cleaned up
Result: Regular management with a consistent approach leads to a reliable promotion set up.
next up: 4 of 14 | Upsell & Exit Intent →

6. Upsell & Exit Intent
I manage our upsell and exit intent platform on EntirelyPets. Because we have many promotions and other coupon sources,
it is a constant challenge to cut out the “noise” and ensure we get real results.
● Identified customer errors in spelling coupon codes
○ Notified platform, suggested alternatives to common alphanumeric mix ups (e.g. O, 0; 1, l, I; 5, S)
○ Created new codes
● Refined targeting to reduce contamination by acquisition platforms
● Assigned KPI for assessing performance
● Used platform insights to highlight low-converting search terms
Result: $8,000-$15,000 revenue generated per month from upselling customers with intent to buy and
salvaging some customers with intent to exit.
next up: 5 of 14 | SEO Maintenance →

7. SEO Maintenance
In the interim between SEO specialists, I kept an eye on the top trafficked product pages.
● Optimized titles & meta descriptions for top 50 products
● Tracked impact of optimization
I also initiated other upgrades.
● Breadcrumbs display in Google search results (SERP) instead of page URLS = higher clickthrough
● Reduction of page size by removing unnecessary content = faster page load times
Result: Reduced negative impact of going 9 months without SEO-dedicated individual.
next up: 6 of 14 | Bot Filtering →

8. Bot Filtering
Bot traffic skews reports on metrics like sessions, new visitors, avg. visit duration, page value, and conversion rate.
● Tracked down source
● Setup exclusion filters
● Corrected bloating in previous 2yrs
reporting
● Permanently killed bots from large sources
● Customized filter strains out small sources
from affected report segments
○ Catches ~100,000 bots/month
Result: Filter keeps key reporting accurate despite
growing bot problem. Accurate reporting enables
educated decisions.
next up: 7 of 14 | KPI Improvements →

9. KPI Improvements
When I started, it seemed analytics trends were being reviewed in response to concerns more often than being analyzed to
address concerning trends. This situation was due to time and manpower limitations.
I took over reporting to drive proactive analysis of month-to-month and year-to-year trends.
● Re-pulled 30 months of reports (x2) to filter bot activity
● Split major KPI by sources to drilldown top level trends
● Integrated with blog & newsletter for well-rounded data
● Began monthly team reports on trends, causes, insights, & actions
Result: Looking forward, keeping our “finger on the pulse of” trends creates realistic expectations. Looking backward,
performance differences signal that time spent investigating will be well-spent.
next up: Big Things →

10. Big Things
Daily Deal | Feature Store Preparation | Sales Forecast Model | Overstock / Short Expiration

11. Daily Deal
This feature was previously performing well, but abandoned due to planning time requirements. I took it over and re-
launched, multi-purposing it as a retention mechanic and testing ground.
● Built out automated performance analysis
● Created framework to structure data & generate actionable insights
● Coordinated with development team for advance planning API
Result: Time requirement greatly reduced. Testing ground generates key performance insights influencing newsletters and
feature stores.
● 30 days: 3% visitor growth rate
● 90 days: 20% conversion rate
● 120 days: 3.65% overall revenue
Result: We’re retaining more high-value shoppers. This strengthens the core group we market to most.
next up: 8 of 14 | Feature Store Preparation →

12. Feature Store Preparation
In August I became involved in feature stores. As part of the team doing the heavy lifting, I added my own processes to
save time and improve efficiency of each store preparation. I’ve added in stages over 3 months to allow learning time.
● ARRAYFORMULA() Fast formula population
● Master list & _ref Fast updates to multiple sheets, one parent many children
● Protected ranges No accidental edits or deletion of critical data
● Pivot tables Quick numerical analysis of data
● Standard formatting Quick visual analysis of data
● Standard columns Quick referencing for information
● Demand-to-inventory Out of stock risk projection for long-term or high-traffic features
Result: Faster selection of items based on margins, inventory, brand, category, pet type, etc. Designers have easier time
generating pages. Purchasing and Merchandising collaborate for heads up on out-of-stock risk items.
next up: 9 of 14 | Sales Forecast Model →

13. Sales Forecast Model
I’ve worked to make Daily Deal and Feature Stores heavily data-based, using inventory, demand, and performance metrics
in our selection process. Goal: to maximize weekly sale revenue while reducing “money left on the table.”
These developments fed the idea to build a sales forecast model.
● Built forecast model to predict EntirelyPets sales Black Friday to Cyber Monday
● Sent prediction to Gopi week prior to sales to authenticate it (no after-the-fact tweaks)
● Prediction fell within margin--model works!
● Predicts revenue on assumption we do nothing different & trends continue
○ Knowing what will happen if we do same ol’ same creates comparison baseline
Result: Having a comparison baseline lets us measure the effectiveness of choices to do things different. We can set
growth goals and focus on what works, improving over previous years. In the future, tying in more performance data can
help us determine what should go on sale to meet growth targets.
next up: 10 of 14 | Overstock / Short Expiration →

14. Overstock / Short Expiration
I recently took over management of liquidating overstock and short expiration products. But I’ve been involved for a while.
I helped build out sales projection algorithms implemented in early November, based on my work streamlining feature
stores.
● Designed algorithm for sales projections based on demand change
● Collaborated on algorithm identifying trend changes
○ Compares trends before/after discount applied
○ Determines products selling better on promotion… and products not selling better
○ Recommends course of action based on trends
Results: We can difinitively tell what sells better, what doesn’t sell better, and what doesn’t sell at all when overstock/short
expiration promotions are applied. Products that don’t sell better should be taken off promotion. Products not selling at all
should be donated.
next up: Special Features →

15. Special Features
Holiday Store | Halloween Store | Black Friday / Cyber Monday Stores

16. Halloween Store
● Created 400 missing data points
● 6 SKUs predicted as high-popularity based on keyword search trends
○ Insights provided to Purchasing/Merchandising departments to increase stock
○ We sold out of all predicted--success!
● Tested alpha version of sales analysis
○ Focused on perceived value vs. expected value of product categories
○ Included method for projecting lost sales opportunity ($) based on sales predictions
○ First crack at analyzing breakdown of customer demand vs. satisfaction with item availability
○ Supported development of sales forecast 2 months later
Result: Customer buying trends insights applied to future stores. Laid groundwork for sales analysis development.
Revenue directly responsible for:
● $99,000 from Halloween Store featured SKUs
● $551,000 from visitors browsing & buying Halloween Store featured SKUs, plus other SKUs
next up: 13 of 14 | Holiday Store →

17. Holiday Store
● Created 1,005 missing data points
● Tested formula for identifying SKUs at risk of going out of stock
○ 50% of original predicted have gone OOS since store launch
○ Improved formula carried over to Black Friday store preparation
● Tested beta version of sales analysis
○ Focused on expected customer demand vs. actual customer demand
○ Supported development of sales forecast 1 month later
Result: Valuable testing ground for improvements applied to future stores.
Revenue directly responsible for:
● $283,000 from Holiday Store featured SKUs
● $850,000 from visitors browsing & buying Holiday Store featured SKUs, plus other SKUs
next up: 14 of 14 | Black Friday / Cyber Monday Stores →

18. Black Friday / Cyber Monday Stores
I refined the Master List template, making it more robust. I also introduce the use of a _ref sheet to support sales
calculations based on inventory changes.
● Started 14 weeks in advance
○ Requested promo checker script to identify loopholes pre-launch
○ Performed analysis of 2014’s BF/CM sales breakdown
○ Notified Merchandising & Purchasing departments of out of stock at-risk items
● Created 2,320 missing data points
● Created 70 unique promotions
Result: All loopholes fixed. Smooth feature store launch.
Revenue directly responsible for:
● $222,000 from BF/CM Store featured SKUs directly
● $760,000 from visitors browsing & buying BF/CM Store featured SKUs, plus other SKUs
next up: Wishlist Projects →

19. Wishlist Projects
1. Launch mystery box feature
a. Move short expiration & overstock products
b. Promote new brand or new product awareness
c. Link with customer loyalty program
2. A/B testing page layouts
a. Expand market segment by expanding demographic we appeal to
b. Increase conversions by streamlining checkout funnel
c. Improve user experience (UX) as well as search engine optimization (SEO)
3. Expand type of deals offered on Daily Deal
a. Buy all 3, get free shipping
b. Buy [x-deal] & [x-brand], get [x-deal] free
4. Initiate creation of macro for promotion management
a. Increase efficiency
Fin.