How we took a NEW Client spending $200k/m on FB to RECORD sales in 48 Hours. πŸš€πŸ‘€

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So, we took this client onboard on Christmas eve, and began handling their ads in the last couple of days. In between, we worked to ensure a smooth transition.

After 2 days of running their ads, we gave them a record day of sales volume.
Technically, yesterday was our first full day of running their ads.

Here's how we did it:

1. Client On-Boarding
β†’ Understand KPI's & processes
β†’ Direction of business and branding feel
β†’ What success looks like
2. Review and Audit
β†’ Audited everything from paid, to organic, to email, to customer experience.
β†’ Researched market and customer reviews/feedback (help with ad copy and creatives)
β†’ Had 10+ calls with client to understand each part fully.
3. Identifying Bottlenecks
β†’ Saw where money was spent poorly and how to improve
β†’ Recognized reliance on FB
β†’ Found areas for expansion
β†’ Saw drop-off areas on site
4. Holistic Improvement:
β†’ Drafted and approved new ad copy/creatives
β†’ Tweaked website to improve CR
β†’ Made suggestions overall to help KPI's (CAC, LTV, Retention, CR etc.)
5. Diversifying Platforms
β†’ Turned back on Google Ad campaigns but with tweaks
β†’ Took over Snapchat and began optimising

WHY?

To bring in new, fresh traffic for prospecting while expanding the FB remarketing audience pool. This was almost immediately beneficial.
6. Took over FB
β†’ Mainly focused on maintaining performance
β†’ Consolidated account (2 campaigns - ToF, Remarketing)
β†’ Incorporated new creatives & ad-copy
β†’ Saw what was working and ramped up

Kept eyes on it regularly throughout the day to make small tweaks.
At the end of our first day, we'd hit a record day for volume.

After checking, this was higher than their recent BFCM week.

A MASSIVE successful first day that was the fruits of a week's worth of proper research and set-up over the holidays.
What's next?

β†’ Further optimise FB for long-term growth.
β†’ Expand Snapchat and Google (just search atm)
β†’ Introduce TikTok, Google Shopping, and potentially Amazon
β†’ Work with client on new offerings & angles

More from All

How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

πŸ§΅πŸ‘‡(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)

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