Ho Ho Holy CRAP Talks #16 (Conversion Rate, Analytics, Product)

We welcome you to join the first every Ho Ho Holy CRAP Talks AND Drinks (aka CRAP Talks 16)! A couple of lightening talks on the topics of conversion rate optimisation, analytics, and product along with social drinks to follow.

Join us on 3rd December for an evening of informational, useful, practical, case studies, tips, tricks, followed by social drinks!

Agenda:

18:45: Arrival

18:55: Talk 1

19:25: Talk 2

19:50: Drinks and Networking

Speakers:

Dataform - Dan Lee: The Full Stack Analyst

Of all the roles within the data space, analytics is the one I find most rewarding. As an analyst, you have the opportunity to work on technical problems (grappling with huge datasets) as well as product and business problems (helping people make good decisions). In my experience, the most effective analysts are those that blend strong technical capabilities with a solid business or product sense.

Over the past year or so, a new role has started to appear - the “Analytics Engineer”. Analytics Engineers specialise in data preparation, modelling disparate source data into easy to consume datasets. They use software engineering best practices like version control and testing to add a level of rigour to data transformation. This frees up analysts to focus on business partnering, spending less time working on data preparation themselves.

But in my opinion, this is a backwards step. The rigour that analytics engineers bring is fantastic, but don’t isolate this into a specific role. Your entire analytics team should be working this way, whilst continuing with the partnering aspect of their role. In fact, with the technologies and tools available today, I propose that full stack analysts will be the ones that bring the most value to your teams. So:

What exactly does a full stack analyst do?

What tools and skills do they need?

What makes teams of full stack analysts so effective?

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Farfetch - Marketing Spend: Is it driving us Incremental?

Abstract:

Marketers will spend $333 billion on digital advertising in 2019 and this is growing year on year. Attribution and econometric models are commonly used to measure the effectiveness of this spend at driving business outcomes, but how can you differentiate between correlation and causation?

Incrementality testing allows marketers to measure the causal effect of ad exposure through experimentation. The ‘incremental metrics’ produced by these tests are critical for understanding the type of marketing your customers respond to and making decisions on capital allocation. Data silos, increasing consumer privacy and limitations of experimental design pose challenges when measuring incrementality that require creative solutions. We will run through the ins and outs of incrementality testing and how it can help your business acquire and retain customers in a more efficient way.

Owen Kinsella (Senior Analyst - Marketing Analytics):

Jan Gananathan (Senior Analyst - Marketing Analytics):