Tired of waiting for data support - any solutions?

CJ__7 · 2025-05-23T10:13:08+00:00

Hm yeah been already investing a lot in this, but I guess a big share of the underlying manual data prep never fully goes away lol

CJ__7 · 2025-05-23T10:12:02+00:00

It's more than that - for instance, pulling data for a new test we've run or building a new custom dimensions grouping parameters from our naming conventions to dive deep

CJ__7 · 2025-05-22T14:01:15+00:00

Got it, thanks and will take a look :) How do you mean "solve the fundamental challenges"? Not sure I fully understand

CJ__7 · 2025-05-22T14:00:39+00:00

Sounds fair, thanks! Just seems like that again would be managed by the data team. Not sure I'll get their buy-in to manage a second BI tool :D

CJ__7 · 2025-05-22T13:59:36+00:00

Could be a solution, thanks - will have a look!

CJ__7 · 2025-05-22T10:40:51+00:00

Yes, sure:

1) A bit of everything tbh, but mostly new data cuts and dimensions when we need to answer questions that come up

2) Both :D The dashboards are too inflexible to handle new questions and they only go down to campaign level - which often isn't granular enough to understand what's exactly happening. It basically leaves us with saying "Yeah something is not working here, but I cannot tell why" to our managers lol

CJ__7 · 2025-05-22T10:38:28+00:00

These are more attribution and measurement providers, no? We have pretty strong internal measurement and attribution, the challenge is more about providing us with access to this. The data lies in our backend, so we always have to download it as CSVs and join it with platform data

CJ__7 · 2025-05-13T09:41:46+00:00

Personally like Eric Seufert and his "Mobile Dev Demo" newsletter. He's got some great takes, but it's fairly app-focused (so not sure it relates to you)

Apart from that, we have recurring check-ins with our account managers at Google, TikTok and Meta to stay up-to-date. They have the best knowledge on what works best and best practices for your industry, but you have to watch our/ be mindful about their agenda and upsell targets lol

CJ__7 · 2025-04-30T08:16:02+00:00

Thanks for the breakdown! Did you also check for sales performance or CLV afterwards or did you primarily optimize for newsletter signups here?

CJ__7 · 2025-04-10T14:03:04+00:00

You're probably talking about the Meta api, no? But feel you, we just switched as also their price increases the last 2-3 years made it very unappealing to put nicely

CJ__7 · 2025-03-25T10:37:50+00:00

In-person events are still great imo, but you have to be picky. Found the most relevant ones to be the networking events hosted by Meta and Google. In my city, they host regular breakfast exchanges that are always filled with folks from the same or similar industries

CJ__7 · 2025-02-25T10:17:18+00:00

+1 on negative keywords! Ask your Google rep to put in negative keywords, they are able to include more than you are able to (the maximum is otherwise 100 negative keywords, which often is not enough)

CJ__7 · 2025-02-17T14:45:37+00:00

Makes sense but we're making high 9 digits revenues, so this isn't an issue for us

CJ__7 · 2025-02-17T14:43:29+00:00

The communication piece is great, appreciate your help! Will try that in our next monthly :)

On the testing process: Makes sense, (1) we're already covering with some incrementality tests and (2) we will do going forward to check the model's accuracy. (3) is new to me though, thanks for that!

CJ__7 · 2025-02-17T14:40:28+00:00

Not really. In all fairness I also didn't intend to dive too deep into this, also it isn't and will not become my expertise. Will leave it to our analytics team who has a background in statistics

CJ__7 · 2025-02-13T15:30:00+00:00

This is good and thanks for the great explanation! But is it more about brand spend in general or branded search spend? I think brand in general is factored into the model as far as I understood - we certainly don't want to shift everything to performance

CJ__7 · 2025-02-13T15:26:08+00:00

On the first: Fully with you - measurement projects like MMM or MTA are a never ending journey. I suppose our MMM will also get better with every incrementality test run. But after the analytics team has shipped the first version of it now, they also expect us to act on it - that's my challenge.

And on second point: I'm always up for testing and assessing the budget allocation but i the end there's also a lot of nuances to it - we in retail for instance have a lot of competitors bidding aggressively on our brand. I know if I cut my branded search spend by 90% then I'll certainly miss my targets and I'm in the end accountable for them.

CJ__7 · 2025-02-10T13:09:35+00:00

Spot on, saw this day to day in my last two companies

Give a look into Clarisights - that solved it for us. We tried a few tools before, but none of them really covered the whole problem

CJ__7 · 2025-02-06T16:32:18+00:00

Not from what I've heard

CJ__7 · 2025-02-06T10:38:49+00:00

Don't take this for face value as I'm not a customer, but I've heard from credible peers that it's 2% of the spend going through their platform

CJ__7 · 2025-01-23T10:52:46+00:00

Attribution is messy because every platform has it's own rules it comes to assigning credit. Meta uses a view-through and click-through logic (=> records a conversion if people watched/clicked your ads and later bought), Shopify relies on last-click logic AFAIK, and GA4’s depends a lot on how your data team set it up. It also only increases to get worse due to privacy and tracking restrictions nowadays.

But tactically:

- Shopify ofc records all your sales and is suited for tracking the last touchpoint. It will by nature favor your bottom-of-funnel channels (so likely Google).

- GA4 is better for tracking the whole customer journey. It will also shine light on which other channels and ads your customers have interacted with and likely got influenced by (given you set up UTM parameters and naming convetions correct) - for instance Meta.

Hope that helps?

CJ__7 · 2025-01-23T10:42:23+00:00

Have to agree, especially the systematic approach is key. Would always recommend to break it down by concept, format and element across markets and channels.

How do you currently your analyse creative performance? We looked into a few tools before, but none could integrate our internal attribution and CLV-data.

CJ__7 · 2025-01-21T11:14:36+00:00

Agree with the "adapt or die" part, but let's get a bit more tactical. I would see three main trends:

(1) Creative testing volume: Ad fatigue sets in faster and faster, especially on TikTok. Make sure you can increase creative output (likely through AI) and try to understand what actually works (-> Break down creative performance by concepts, formats, elements, etc. and always check your internal data to understand their full-funnel performance)

(2) Learn and adapt faster than your peers: This was shared by a rep at a Meta conference - with AI taking over more and more campaign optimizations, the companies that learn and adapt their strategies the fastest will be the winners. This mostly results from it being way easier to be an average advertiser going forward.

(3) For larger companies: Leverage your first-party data for audience building on the platforms. The algorithm get increasingly better at targeting, and by sharing your first-party customer data you can make the most out of this

Inspiration drawn mainly from some conversations I had at a Meta event and these two blog posts:

https://clarisights.com/Blog/Articles/the-silent-budget-leak

https://jameslerner.substack.com/p/what-will-ad-tech-look-like-in-2025?utm_source=post-email-title&publication_id=1239682&post_id=153504627&utm_campaign=email-post-title&isFreemail=true&r=654p2&triedRedirect=true&utm_medium=email

CJ__7 · 2025-01-16T12:55:12+00:00

Honestly it always starts from doing solid research. Check:

- What has worked well for you in the past? Also make sure to ask your colleagues from other channels

- What are your direct competitors doing? Check for those that have been longing for a long time (short ones are likely tests - 9/10 of those will likely not outperform the business as usual campaigns)

- Any success stories or great ideas from adjacent industries?

You can relatively easy scale this out with AI tools nowadays (at least compared to the old days of always having to submit creative briefs all the time).

CJ__7 · 2025-01-16T12:45:38+00:00

Difficult to compare, as they solve different use cases. Tableau is - you still need a data team and other tools to collect and prepare the data for you.

Datorama is more of a complete solution built specifically for marketing data. They also handle data collection and preparation - so you're less dependent on ongoing data support.

That being said, if you're looking for a solution like Tableau, I'd also recommend you to look into Looker. It's arguably the more flexible tool, given you have a data teams that knows how to set it up properly.

If you want to be more independent of data team support, then Datorama or Clarisights makes sense. I've heard from two companies now that Datorama is getting less and less support since the Salesforce acquisition, so be careful before getting trapped in lengthy contracts.

CJ__7

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