The global app market’s yearly revenue is projected to be around $500B in 2023.
Both the data and lived experience in actually using apps would lead one to believe that the lion’s share of this comes from advertising.
Therefore, it’s apparent that optimal attribution-or rather, lack thereof, is quite the big ticket issue.
Within the current paradigm, there are several issues that, while cause by organic changes cause by market and technological forces, can be ameliorated by strategic improvements on the part of analytics platforms’.
It’s arguably self-evident that as our aggregate computational abilities grow, being able to leverage personalized data will be of increasing importance. This is simply a question of capability-data is the digital commodity (a fact that is greatly underscored in the age of ‘scale is all you need’). So, given the synergistic elements of exponentially growing compute and connectivity, hyper-personalized data was simply a question of if and not when.
This works on both ends of the spectrum. On one hand, advertisers can obviously benefit from hyper-personalization. On the other, in an age of Spotify’s startlingly good recommendations, consumers expect experiences that are dynamically tailored to their preferences. Indeed, per Salesforce’s 2018 State Of The Conected Consumer report, more than 70% of consumers expect ‘vendors to personalize to my needs’-and this is from 5 years ago. How much better are Spotify's recommendations today?
This trend is set to go parabolic with the ascension of generative AI. If content is literally infinite (because of essentially zero marginal cost of production), it stands to reason that recreational entertainment and advertisement alike will not only be catered to particular individuals, but particular individuals at that exact moment. Each time you see an ad will be the only time that piece of content has ever existed.The point is, the amount of data marketers are going to have to deal with is going to continue go up-exponentially, and with increasing significance to the bottom line.
Of course, this is in addition to the fact that data has been the lifeblood of marketing for the past couple decades as it is. BCG puts it quite succinctly in this report.
However, this does not mean that all (or even most) companies have been adapting well to this change: According to a 2020 survey by Seagate and IDC, organizations said they only collect 56% of the data potentially available to them.
BCG defines ‘multimoment maturity’ as ‘the ability to deliver personalized content to consumers across the purchase journey’:
Evidently, there's a lot of scope for improvement in this realm.
The bigger the organization, the more the departments. While this obviously comes with the territory, this can also lead to the fragmentation of data that a given organization may collect-despite it being relevant across teams.
For example, suppose a SaaS company has a customer service team dealing with numerous tickets about a specific feature bug. If the product development team isn't looped into this data in real-time, they might spend time creating new features while a severe bug that causes customer churn remains unresolved. In this case, data siloing leads to the inefficient use of development resources and can hurt customer satisfaction and retention.
Now, how can this be fixed?
There is obviously the managerial and operational angle that each company will have to address individually. However, there is a great amount of value that value that analytics tools can unlock here as well.
Here are a few ways how:
Firstly, (as we have spoke about in our other blogs), they need to optimize for cross-team collaboration. A large amount of this has to do with simply reducing the friction it needs to implement new queries and functionality into the product-needing developers for every small update is a self-evident hindrance to well-oiled and snappy internal data management.
As we’ve spoken about , recent advances in AI unlock autotrack without the flaws that it would entail before. Because it enables on the fly iteration, (such as re-labeling) particular teams would be able to autonomously change what they deem fit without requiring intervention from other teams.
Furthermore, different kinds of employees think differently.
Aside from different data sources being pertinent for different roles, coders, marketers, and product managers likely wouldn’t want or need to utilize data in the same way.
Notion’s modularity is a huge part of its allure.
The same reason companies will have to grapple with a lot more data is the same reason the same kind of personalization and modularity will be possible on the user end of these analytics platforms. Because the AI models are capable of interpreting data, they are also capable of shaping it in whichever form is optimal to you.
This includes both what data you’ll see (dependent on your role), and how you’ll see it-depending on your own personal preferences.
The best part is that this won’t even need to be a deliberate and finicky process like Notion currently is (I can’t tell you how much time i’ve spent setting up Notion templates!), but rather something organic and iterative; growing as it learns from your workflow, and giving you back insights!
Think the modularity of Notion, with the recommendations of Spotify!
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