post

Footer About

Footer Get Involved

Footer Links

Mind the Product

General

Product Unplugged Episode 3 with Pierce Healy

Product Unplugged Episode 2 with Lorilyn McCue

Product Strategy

Building Product Teams

Diversity and Inclusion

Digital Transformation

Artificial Intelligence (AI) & Machine Learning (ML)

Product Leadership

Product Management Career

Product Ethics

Minimum Viable Product

Enterprise Product Management

OKRs

Customer Research

Hypothesis and Testing

Business Strategy

User Research

👩‍🚀🚀   Live now for MTP members: <a href="https://mtp2017stg.wpengine.com/membership/#events" style="color:#FFFFFF;text-decoration:underline;">Driving Innovation AMA with Tendayi Viki</a>

Roadmaps: Break the rules without breaking the principles

Got questions about your product career?

Product roadmaps vs data – Jeremy Levy on The Product Experience

From engineering to product management at Google

Mind the Product joins Pendo

Annie Witcombe produces editorial for Mind The Product on a freelance basis. She has experience in conducting client research and telephone interviews, and she keeps relevant information accessible and up to date. 

Annie wrote for five business publications during the final year of her undergraduate degree, and she has worked on various stages of the editorial process since. The experience has sparked her enthusiasm for journalism, which she hopes to continue in her future career.

Annie Witcombe

<p>Machine learning data products can create significant value for businesses and their customers. For example, data products can enable customers to engage with more relevant content, facilitating and supporting repeat custom to the business. In this ProductTank London talk <a href="https://twitter.com/aligulez?lang=en" target="_blank" rel="noopener noreferrer">Ali Gulez</a> discusses the critical intersection of data products and digital businesses, sharing his knowledge of machine learning models through his experiences with Trainline.</p>
<h2>Machine Learning Products</h2>
<p>Machine learning is a combination of data analytics and AI technology, available in a variety of products and software. A particularly well-known example of machine learning is the “Recommended For You” algorithm on Netflix, a feature which recommends relevant items to users based on their viewing behaviour. It acts as an automated shop assistant &#8211; you’re presented with the product you asked for, alongside some other related or similar products. The assistant is trained to cross-sell and upsell, maximising the value of service and value to business. The same principle applies to machine learning data products, and Trainline uses these products in several ways&#8230;</p>
<h2>Examples from Trainline</h2>
<p>Ali’s first major project when he joined Trainline took him straight to data science as he interpreted behavioral data from the Trainline website to understand user behaviours across the whole service. He worked in a data team who identified six categories of Trainline customers, but he was particularly interested by one group: the commuters with season tickets. Ali noticed this group using the Trainline app and website to merely look up train times, so he advocated monetising these customers in alternative revenue channels. The realisation was possible thanks to internal metrics and data products.</p>
<p>For Ali’s second project, he worked on price prediction &#8211; a customer-facing data product. Price prediction analyses billions of data points of price runs over time; then, it predicts the price users see against price increases in time. Crucially, the price predictor helped Trainline to bring information to its customers, so those informed customers felt empowered and saved money. It meant customers were more likely to use the company again.</p>
<p>Although machine learning models can add significant value to their business, they do not appear overnight and they aren’t made by “magic”, says Ali. They require time, effort, planning, and coordination across many departments, and this is why Ali believes in a “hierarchy of needs”, or required steps, to create effective data products. This spans the first stages of data collection, to moving and storing data so that data scientists can explore and transform it. This kind of data science lives hand-in-hand with machine learning, and businesses of all kinds can find ways to harness it.</p>
<div class="g g-21"><div class="g-single a-73"><a class="gofollow" data-track="NzMsMjEsMSw2MA==" href="https://community.mindtheproduct.com/c/general/"><img decoding="async" src="https://mtp2017stg.wpengine.com/wp-content/uploads/2021/10/Membership-Circle-banner-ad-scaled.jpg" /></a></div></div>

core/freeform

Solving Ethical AI Problems, When Algorithms Go Wrong, and the Skills Needs To Work on AI Solutions: Insights From Kriti Sharma

How AI Is Changing The Product Management Job Description by Mayukh Bhaowal

Designing Meaningful Human Experiences by Kate O’Neill

Can AI Create a Fairer World? By Kriti Sharma

They’re Just Fancy Averages by Ben Fields

Data Science and Digital Businesses by Ali Gulez

Talking About Data Science by Jenessa Lancaster

AI at an Early Stage Startup by Zara Tam

Building Search Products With Machine Learning – Shakhina Pulatova

Using Deep Learning to Find Beautiful Photos by Alex Miller

ProductTank Columbus

ProductTank Tauranga

ProductTank Edinburgh

Prioritised Member AMA - Perfecting product roadmaps

ProductTank San Diego

Navigating difficult relationships panel

ProductTank Philadelphia

ProductTank Bucharest

ProductTank NoVa

Remote Training Workshops

ProductTank London

ProductTank Birmingham

ProductTank Oslo

ProductTank Astana

ProductTank NoVA

ProductTank Bratislava

ProductTank New York

ProductTank Kansas City

ProductTank Orlando

ProductTank Dallas

ProductTank Tokyo

ProductTank Belgrade

ProductTank Madrid

ProductTank Leeds

ProductTank Zürich

ProductTank Perth

ProductTank Chicago

ProductTank Auckland

ProductTank Richmond

ProductTank Charlotte

ProductTank Reston

ProductTank Washington

ProductTank Sofia

ProductTank Raleigh

ProductTank Ljubljana

ProductTank Kraków

ProductTank Barcelona

ProductTank Dublin

ProductTank Montréal

ProductTank Bogotá

ProductTank Seattle

ProductTank Madison

ProductTank Singapore

ProductTank Portland

ProductTank Budapest

ProductTank Porto

ProductTank Austin

ProductTank Los Angeles

ProductTank Norwich

ProductTank Des Moines

ProductTank San Francisco

ProductTank Helsinki

ProductTank Reading

ProductTank Brighton

ProductTank Valencia

ProductTank Bielefeld

ProductTank Malmö

ProductTank Canberra

ProductTank Paris

ProductTank Manchester

#mtpcon SF+Americas 2022 Conference

#mtpEngage Hamburg 2022 Leadership Forum

#mtpEngage Hamburg 2022 Workshops

#mtpEngage Hamburg 2022 Conference

#mtpcon London+EMEA 2022 Conference