First 6 MonthsKRISTINE MARSHALL

JUNE - NOVEMBER 2015

Small Things1. Customer Reviews2. Donations / Goodwill3. Promotions4. Upsell & Exit Intent5. SEO Maintenance6. Bot Filtering7. KPI Improvements

Big Things8. Daily Deal9. Feature Store Preparation

10. Sales Forecast Model11. Overstock / Short Expiration

Special Features12. Holiday Store13. Halloween Store14. Black Friday / Cyber Monday Stores

Small ThingsCustomer Reviews | Donations / Goodwill | Promotions | Upsell & Exit Intent

SEO Maintenance | Bot Filtering | KPI Improvements

Customer ReviewsWhen 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 →

Donations / GoodwillDonation 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 →

PromotionsPromotion 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 →

Upsell & Exit IntentI 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 →

SEO MaintenanceIn 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 →

Bot FilteringBot 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 →

KPI ImprovementsWhen 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 →

Big ThingsDaily Deal | Feature Store Preparation | Sales Forecast Model | Overstock / Short Expiration

Daily DealThis 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 →

Feature Store PreparationIn 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 →

Sales Forecast ModelI’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 →

Overstock / Short ExpirationI 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 →

Special FeaturesHoliday Store | Halloween Store | Black Friday / Cyber Monday Stores

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 →

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 →

Black Friday / Cyber Monday StoresI 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 →

Wishlist Projects1. Launch mystery box feature

a. Move short expiration & overstock productsb. Promote new brand or new product awarenessc. Link with customer loyalty program

2. A/B testing page layoutsa. Expand market segment by expanding demographic we appeal tob. Increase conversions by streamlining checkout funnelc. Improve user experience (UX) as well as search engine optimization (SEO)

3. Expand type of deals offered on Daily Deala. Buy all 3, get free shippingb. Buy [x-deal] & [x-brand], get [x-deal] free

4. Initiate creation of macro for promotion managementa. Increase efficiency

Fin.