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Product Analytics Meetup Takeaways

Ashwin Rajan  & Marjukka Niinioja

· Product Analytics,Behaviour Design

Product Tank recently held a meetup on Product Analytics in Helsinki. On invitation of co-host Antti Suvanto from Contribyte, Petri Mertanen kicked off with 'Digital Analytics and Machine Learning Tips for Sales Optimization'.

An enthusiastic discussion followed with participants ranging from startups to established firms. We’ll just touch upon a few areas here.
 

(All images in this article are from Petri’s presentation, which can be found here: https://www.slideshare.net/mertanen)

Digital Analytics and Machine Learning tips for Sales Optimization

With growing numbers of tools, adopting analytics always is getting easier. But the large market of available tools makes it more complex for business to choose their analytics toolset.  In a way, we see this scaling to fit the needs of marketing, sales and product management.

Google Marketing Platform

Google Analytics was discussed in most depth. Petri reminded us that even a good tool doesn’t track everything out of the box. The issues listed in the picture below were related to the beginning of the customer journey. Even more needs to be added when talking about tracking the use of a digital service during trial period or life-time use.

Website visitors, so what?

What matters ? - A search for decisional metrics
The slide below from the presentation reflects the two common approaches to choosing any analytics tool. Let the tool drive what is measured, or create metrics and then configure the tool to measure them.

Google Analytics implementation

Can you separate descriptive metrics from ones that drive decisions? How will you find the metrics that matter for decision-making? What influences the user experience down the growth funnel?
 

See more examples of behaviour-related analytics in this recent article. 

New Cross Device reports in Google Analytics

The presentation concluded with a summary of AI and machine learning in analytics. 

Possibilities of AI and Machine Learning

The conversation then moved towards the challenge of tracking cross-device user behaviour. Integrating data sources between digital and physical, in this case in-store, inspired conversation. Marjukka used examples from retail and SaaS to get answers to the great questions from participants.

The discussion touched the importance of the behavioural element in analytics definition and deployment. The answer to the question ‘what matters’ depends on what matters to the user where they are. And where they are at a point can be a metric of:

  • The business perspective: where they are in the growth funnel / channel / customer journey / stage of the transaction.
  • The user perspective: where they are in terms of a life experience, such as ‘taking care of the kids’ or ‘watching a movie.’

We see the scope of analytics in the future as being much broader than it is currently. Key questions remain such as: how do we strike a balance between user goals and the goals of the organisation? What analytics tool could reveal the challenges that users face with a product? What is the core value of the product and for whom? How does this evolve with user behaviour over time? What are the reasons for user retention over time?

If you’d like to learn more, check out upcoming trainings by Osaango here. We’ll be mapping behavioural design with product analytics and APIs.

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