Making Sense of Facebook for Online Retailers

@commercebrain #irce2013

Making $ense of Facebook Search

Type to search for people, places and

things


Facebook -‐‑ Goldmine


Facebook data + features = Amplifiers


Same data pools (actions + objects)

•  Act as multipliers

Your model

Timeline

Newsfeed


① Unique Data


①  “We mapped item taxonomy to FB interests

“… people bought more unrelated events and upsell skyrocketed.”


①  “We also mixed activity data

•  …we learned so much “

•  Activity more

accurate, up to date, matches real life experience

•  next gen


①  “Future in social

…we are prepared.” – Vipul Sharma, Director of Data

Engineering

discovery”


②  Product Traffic Only


②  to Sales Heaven


② Highway speed Sales


② Qualified HOV lane


② Zapwow gets 3K monthly

2,718

4,797

250

1,063

3,393

Nov-‐‑12 Dec-‐‑12 Jan-‐‑13 Feb-‐‑13 Mar-‐‑13 Apr-‐‑13


② Zapwow gets 3K monthly

• 37.22 % • 56.47% back to

Buy


② Zappos confirms Uplift •  “Your sample is less than 1% and we

get 4X more…”

• 1.2M


② How’s the HOV sales?

• 86 % of shopping sessions

• 2.6 % in same session CR

• 30K users (1.2 M X 2.6 %)

• $2.1M if average $70


② Zappos

1.  “Converts like email”

2.  “80% paid, 20% owned”


② Zappos

3.  “We do shared interest studies and social listening…surprising insights for DM”

4.  “We want to be real &deep with our customers…”


③ Who else stroke Riches?


③ Who else stroke Riches? 1.  Petflow makes 10M from $200K

(28% traffic)

2.  Blitzmetrics customers - Rosetta Stone $6.5 for $1 spent

3.  Badgeville (partner data expands data solutions)

4.  Wonder app, Yandex, RIP


What else I can do NOW?


“You Might Like These Customers “and INTEL


Viral Anything


Help


Models of FB $-‐‑verse Direct sell

Indirect sell

Unique offers

Genius -‐‑ like recommendations


Hierarchy of Sophistication 1 Ed

ge Ran

k Look alike

audiences

Custom audiences ads

Killer apps

Promoted posts

Page with engaging content

search

feed


Hierarchy of Sophistication 2 I read newsfeed Relevant ads to me

Groups and what I engage with

Shared public photos, video, places

Interests

Likes

I search

I discover

I eng

age


Facebook data + features = Amplifiers


Yes, Yulia but….


We are being trained NOW ①  Suggestive

prompts ②  Classify your subs

lists ③  Mobile sleek ④  Photos auto sync ⑤  Classify emotions

123

5


We are trained to search… ①  You might also like ②  Related photos ③  Incentivized check-ins ④  Suggested posts ⑤  Recommended ads ⑥  Ratings ⑦  New savvy search


We are trained to discover…

1

We are trained to add data…


Social Science confirms the trend ①  Tech makes social capital easy to share ② We will share whole a lot more (cheap) ③ We will do so in structured ways ④ “We will be experts, locals everywhere …

info will travel with us everywhere we go…”, Sam Lessin, Facebook


Making $ense from Facebook search ①  SEO more complex

②  Integrate with paid to max owned and earned

③  Power tools required

④  Eventually, cheap or no ads needed


Which team YOU?


Bonus = (email + like) ①  Extra on best Facebook marketing

(tools, pages, top 10 strategies, costs) and optional free 20 min Skype call with Yulia on your questions

②  Expedited invite to Graph search

③ Free upcoming ecommerce Kindle book (publishing in October 2013 and free limited to special audiences)


CommerceBrain flips online stores to profit

Yulia V Smirnova CEO and Founder commercebrain.com [email protected] c: 415 666 6002 skype: commercebrain read our blog hsp://commercebrain.com/blog stalk us on Facebook hsp://www.facebook.com/Commercebrain follow us on LinkedIn hsp://www.linkedin.com/company/commercebrain tweet with us hsps://twiser.com/commercebrain google +1 us hsp://gplus.to/commercebrain

Making Sense of Facebook for Online Retailers

Technology

Transcript of Making Sense of Facebook for Online Retailers