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Increasing Sales through

Recommendation Systems

Strategy and Customization(Creating a Personalized Online Shopping Experience)

Presented by

Dart Marketing, LLC

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Industry research indicates that successful online retailers are generating as much as 35% of their business from recommendations. Are you leaving money on the table?

Meanwhile, the race is on because once someone invests the time to provide feedback to a website, he/she will prefer to shop there versus somewhere else. Personalization increases the amount of time people spend at the site and increases repeat visits, growing loyalty and sales.

Impact of Recommendations Systems

Current YR1 YR2 YR3 YR4 YR5

AssumptionsRevenue 100,000$ (thousands)Lift From Reco's 15.0% 22.5% 28.1% 31.6% 34.2%Profit Margin 10.0%

ResultsGain in Revenue 15,000$ 22,500$ 28,125$ 31,641$ 34,172$ Cum 15,000$ 37,500$ 65,625$ 97,266$ 131,438$

Gain in Profit 1,500$ 2,250$ 2,813$ 3,164$ 3,417$ Cum 1,500$ 3,750$ 6,563$ 9,727$ 13,144$

3Scrollable

Recommendations

Strategy

Displays Community Ratings

Styled to conform to your site:

Remove Unwanted Items

Collect Ratings If Logged-In

New Shoppers Scrollable, community recommendations Checkout – Accessories, cross-sells

Repeat Buyers (Personal Shopping Assistant) Scrollable, personalized recommendations Profile Building: Collect product ratings Smart Search tied to user preferences so it

shows items customers will like Can be linked to existing search, or Used on it’s own. Ours displays everything the

customer needs (not just list of products). Sort by popularity, price, etc.

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Product Ratings Data: Customer feedback improves quality of recommendations. Collecting this data builds a relationship.

Sales Data: Most useful after ratings data. Removing gift purchases ensures matches are based on customers’ personal tastes.

Click Data: Used as a last resort. A poor predictor of customer purchases. Does nothing to build a relationship.

It’s All About the Data

The better the data, the better the recommendations. It’s that simple.

Philosophy: Collect the best data, derive a custom solution, then validate initial results by hand. Once foundation is set, profiles update in real time.

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A different approach: map product affinities in multi-dimensional space. 

Successfully applied to Gevalia coffees and teas, and re-validated with Netflix movie data.

Extraordinary recommendations accuracy for new shoppers - even better for returning customers.

Results are applicable to product and pricing strategies, offline merchandising, cross- & up-selling

Consultative/Analytic Approach

The “Also Bought” method shows what’s popular, but not what’s most relevant. Our affinity-based approach is much more effective.

Overview of Coffee Varieties

FRTE

S6

S5L5

S3

S2S1

R8

R6

R5

R4R3R2

L4

C7

S7

F9 F8 F6F5

F4

F3 F2F1F0

I2C6I1

C4C3C2

C1

B2

B1S4

Complexity of Flavor

Exo

ticn

ess

/ Pri

ce

Flavored

Exotic

Popular Roasts and Blends

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Affinity Maps & Recommendations: The bubbles above represent products sized by sales volume. Products close to each other are recommended to each other.

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Customization Dashboard

Add product details such as… Sku, Type, Sub-type, Category, Brand,

etc. Pricing strategy Dates

Customize recommendations by… Business rules

Profit margin

Inventory availability

On-sale items and other criteria

Reports include… Sales stats from recommendations Web stats relating to recommendations Date periods Custom reports available

SKU SUBTYPE TYPE CATEGORY DATEKN121 DVD PLAYER PORTABLE ELECTRONICS 11/12/2007NJ441 CD COUNTRY MUSIC 1/18/2008TY552 DVD MOVIE VIDEO 11/12/2007TX551 DVD CONCERT VIDEO 11/12/2007KN121 DVD PLAYER PORTABLE ELECTRONICS 11/12/2007NJ041 CD ROCK MUSIC 1/18/2008TY552 DVD MOVIE VIDEO 1/18/2008TR466 DVD SHOW VIDEO 11/12/2007TX551 DVD CONCERT VIDEO 11/12/2007KN121 DVD PLAYER STANDARD ELECTRONICS 11/12/2007NJ997 CD POP MUSIC 1/18/2008

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Secondary Strategies

Other uses for customer profile data …

Product/Pricing Strategies

My-Gift Store (Recommend Gifts) Recommendations for when bill-to and ship-to don’t match Email gift reminders with recommendations Encourage customers to add new gift recipients

New Reasons to Email Post-sale requests for product ratings Personalized promotions

Telemarketing Up-Selling

Direct Mail: Highly recommended products

Catalog Merchandising

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Collect Data Gather transactions data, then refine to exclude

gifts, ship-to’s, etc.

Analyze Data Begin by mining and mapping affinities. Create demo to compare new recommendations

with current ones.

Further Personalize and Enhance Initial Solution

Motivate shoppers to share ratings to further personalize their recommendations.

SKU SUBTYPE TYPE CATEGORY DATEKN121 DVD PLAYER PORTABLE ELECTRONICS 11/12/2007NJ441 CD COUNTRY MUSIC 1/18/2008TY552 DVD MOVIE VIDEO 11/12/2007TX551 DVD CONCERT VIDEO 11/12/2007KN121 DVD PLAYER PORTABLE ELECTRONICS 11/12/2007NJ041 CD ROCK MUSIC 1/18/2008TY552 DVD MOVIE VIDEO 1/18/2008TR466 DVD SHOW VIDEO 11/12/2007TX551 DVD CONCERT VIDEO 11/12/2007KN121 DVD PLAYER STANDARD ELECTRONICS 11/12/2007NJ997 CD POP MUSIC 1/18/2008

Overview of Coffee Varieties

FRTE

S6

S5L5

S3

S2S1

R8

R6

R5

R4R3R2

L4

C7

S7

F9 F8 F6F5

F4

F3 F2F1F0

I2C6I1

C4C3C2

C1

B2

B1S4

Com plexity of Flavor

Exo

ticn

ess

/ Pri

ce

Flavored

Exotic

Popular Roasts and Blends

a1

Next Steps

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You will have a business relationship with experts in the industry…

Craig Tomarkin, President (Affinity mapping, modeling, research, analysis)Craig has spent his career converting ideas into profit. He helped GM design and launch the world’s first free “rewards” credit card, resulting in 5 million accounts in the first year. For Gevalia Coffee, he developed an innovative product mapping technique that optimized cross-selling, pricing, and new-product strategies – a precursor of his current eCommerce recommendations strategy. Craig holds a BSM from the A.B. Freeman School of Business at Tulane.

Paul Delano, Technology Expert (Java, eCommerce, SEO, hosted solutions)Paul’s innovations in artificial intelligence and collaborative search have led to his being awarded four patents. He created the first Internet commerce site for PC Flowers.com as well as the infrastructure for a nationwide interactive television system. He has taught Java courses at companies like JPMorganChase and Hewlett Packard. Paul received a MS in Computer & Systems Engineering from Rensselaer Polytechnic Institute and a BS from Carnegie Mellon.

Phil Goodhart, Direct Response Marketing Expert (Client support, eCommerce)Phil is a veteran of the Danbury Mint, a leading direct marketer of consumer merchandise. He was recognized as a premiere marketing strategist, as well as an innovator in identifying new product opportunities. He managed the development of the Danbury Mint’s first eCommerce site. Phil earned his MBA at Harvard and BA at Princeton.

Why Dart? Its People

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Dart Vs. The Competition

Distinguishing Benefits DartOther

s

Interactive, scrollable recommendations ( √ ) ( )

Collect product ratings to enable customers to train site ( √ ) ( )

Extraordinary accuracy through Affinity Mapping techniques ( √ ) ( )

Solution based on sales data and customer product ratings ( √ ) ( )

Analytical, human-based approach tailored to each client ( √ ) ( )

Mapping, pricing strategy, and other analytic consulting services ( √ ) ( )

Other Benefits

Easy to integrate

Hosted solution

Client can customize recommendations

Search engine included

Optional performance based pricing

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Example: Movie Recommendations I

Caddyshack: Slapstick Comedy. Chevy Chase, Bill Murray, Rodney Dangerfield. 1980.

Key Findings: Dart’s system doesn’t recommend Caddyshack II, even though it’s a sequel. Since it had a different cast, and customers did not rated it as highly as the original, it is not as relevant a match.

Our recommendations stand up to the world’s best.

DARTM Netflix Blockbuster Best Buy WalmartCaddyshack Caddyshack Caddyshack Caddyshack Caddyshack

Nat Lamp.'s Animal House Tommy Boy NL's Van Wilder Caddyshack II Natural Born KillersNat Lamp.'s Vacation NL's Animal House NL's Christmas Vacation DC CabStripes N's Vacation Anchorman Bio DomeAirplane! Airplane! Blues Brothers Back to School

Note: There are thousands of examples on our web site. Click on dvd demo.

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Example: Movie Recommendations II

Sleeper: Slapstick Romantic Comedy. Woody Allen, Diane Keaton. 1973.

Key Findings: Dart’s system recommended Woody Allen romantic comedies exclusively and chose titles from the matching time period. His movies match very closely, indicating he is a genre unto himself (i.e. a “Woody Allen” movie).

Our recommendations stand up to the world’s best.

DARTM Netflix Blockbuster Best Buy WalmartSleeper Sleeper Sleeper Sleeper Sleeper

Bananas Annie Hall Bananas The Lonely Guy Star Trek IVLove and Death The Producers Hardware Wars Modern Romance The Court JesterEverything You Always Wanted to Know About Sex Bananas Love and Death What's New Pussycat? The Time MachineTake the Money and Run Take the Money and Run The President's Analyst Hannah and Her Sisters

Note: There are thousands of examples on our web site. Click on dvd demo.

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Contact Info

DART Marketing, LLC

Craig Tomarkin, President

CTomarkin@DartM.net

203-259-0676

Phil Goodhart, VP Business Development

PGoodhart@DartM.net

203-261-4731

http://Dartm.net