The triangle love: Product, Data and Growth

Everyone is often skeptical about the word “growth hacking”. However, when done right, the marriage of growth with product and data brings sustainable growth to business and meaningful user experience to the customers. How to approach product from a data-informed growth marketing point of view.

The love triangle: Product, Data and Growth.

Albert Mai: Head of Growth – Vero.

Keywords: product team, data team, growth team, prioritization, experimentation, metrics, PIE method.

TL;DR: Albert shares his growth strategy process called PIE (prioritization, impact, ease), an upgraded version of the commonly used RICE growth formula in which he replaces the commonly used Potential metric with a more robust, useful, and measurable Prioritization axis. Albert has implemented the PIE method at several companies he has worked for and achieved compelling results. Albert walks us through the PIE method step by step to break down the reasons it achieves results. By positioning the user at the core of the intersection between product, data, and growth, PIE circumvents common misunderstandings that hinder productive collaboration between these three functions and their teams.

Albert has worked around the world with various startups across a range of industries, from e-commerce to marketplace to mobile apps. He’s worked in Singapore, Vietnam, and Australia. His career history has given him the opportunity to hone various skill sets, from wireframing & landing to SEO to Marketing and product-led growth (the latter of which will be today’s focus).

Let’s start with a love story! There’s a saying: Three’s a crowd. In this case we’re referring to product, data, and growth. Given his background in tech and data and recent work in growth marketing, Albert has experienced a lot of pushback from product teams against growth and a lack of understanding or interest in data. They either aren’t tracking data at all or are doing so inadequately, and consequently missing important information. This means that product, data, and growth teams are not working well together, mostly due misconceptions about what growth actually is.

What Growth is not: Based on what Albert has experienced, growth is not about quick or overnight fixes, random tests that you find on the web or elsewhere, guesswork or Top of the Funnel/acquisition.

What Growth is: Growth is about long term sustainable strategy for the whole team – the product and the company. It should be based on calculated and prioritized experiments based on data. Over his four year tenure at Glamcorner Albert grew the company’s monthly revenue by 45 percent. This is an example of growth. Growth should focus on the whole content lifecycle and full funner user journey, not just acquisition but the process from activation to retention to referral to re-activation. Growth should be product-led and data-driven. Another example of growth Albert created at Glamcorner was growing the returning transaction rate by a factor of 5.4, meaning over half the monthly transactions were coming from returning customers. He achieved this through focusing on retention and UX, not only on the website but across all channels.

Common mistakes that Albert observes in the disconnect between product, data, and growth teams are that:

  • Decisions and priorities are based on opinions instead of facts/data; Best practice for someone else is blindly copied regardless of relevance to the product/industry at hand;
  • Product and Growth teams are siloed – growth teams are often excluded from monthly product discussions.
  • Product teams don’t emphasize data or tracking to the degree they need to from the outset, meaning that down the road when they need certain metrics they have no mechanism to capture them.
  • There is a tendency to ignore either or both qualitative and quantitative data.

Some examples: Albert worked for a client whose data files were incoherently organized, leading to a loss of trust in the information as it wasn’t clearly accessible. Another client had been signing users up for over a year into a database without tracking when they joined, missing valuable insight into user behavioural changes over time.

Three’s a crowd. Four’s a solution! If you insert your user at the core of the intersection between growth, data, and product, and centre your strategy around user needs, you can solve the disconnect issue.

Prioritization: How to prioritize features, projects and strategies from a user centred perspective? First, let’s look from a product perspective. Most companies are looking at objectives and the drivers that move them toward those objectives. They track these on various metrics of effort and impact. They use these to get a prioritization score. Productboard , airfocus, roadmunk, and craft are the most popular prioritization software providers. Where is growth being tracked? In Albert’s work, he frames growth in terms of product experiments that can be translated into formats that fit the software models.

Many companies use frameworks like ICE or RICE to come up with prioritization scores, but these are not data driven. They are based on assumptions of researchers, not users. They are also too generic and don’t account for differences between industries or individual companies within the same industry. How to customize this framework to make it more flexible and able to be tailored to various industries and business models?

Albert’s method is to move away from a ‘best practice’ model to defining what each problem is that the product is trying to solve (hypothesis/problem), and a corresponding assumption on why the problem exists and what may solve it, including identifying the point of the process at which it occurs.

In deference to ICE and RICE, Albert calls this method: PIE: Potential x Impact x Ease. Each of these three factors are scored based on the data that specifically inform them:

  • Potential is scored according to data from stakeholders/market.
  • Impact is scored according to metrics on the platform/device being used, page groups, and user types and stats.
  • Ease is scored according to data from dev/design/content metrics.

Then all these scores are processed into a master score which offers a granular, rich, and data informed portrait of the product, instead of a more common story/guesswork approach.

Impact Value: How do you calculate these scores? First you need to: 1) Define Grouping.What group does your startup specifically seek to help, and through what media?

  • Page or screen group?
  • Device – desktop, tablet, or mobile? IOS, Android, or Web?
  • User type:supply vs demand typical of the marketplace? Or editor vs viewer like in SaaS?
  • User segment: new vs returning? Individual buyer vs brand? User group/status:
    paid vs free. Identify the buckets you want to divide your data into.

2) Decide Key Metrics:

  • Views
  • Unique visitors
  • Conversion
  • Revenue

Different companies will use different metrics. Try to get a year’s worth of data to ensure you have a full cycle that isn’t skewed according to seasonal upticks.

3) Calculate Impact Score.To do this, just take the score of each grouping and use the following formula: Value/ Total Value *10. To get the scoring for Potential and Ease values you can crowdsource from your team: Ex: Albert used a card asking employees across the company How would you rate the potential of the following growth ideas on a scale of 1-10? The card lists the following options to be scored:

  • Individual case study landing pages
  • Vertical/industry landing pages
  • ROI Interactive Calculator
  • Competitor Comparison landing pages

NB: Both the questions and the answers are generated across the whole company, not just the executive branch. This helps eliminate bias and ensure various teams are part of the process.

Where do ideas come from? How do you decide what you want scored? Define your key metrics. For Ex: Performance and Growth can be broken down into Lagging (ex: signups, form submission) or Leading (ex: Clicks/spend). Product and UX metrics can be quantitative (engagement, retention rate) or qualitative (session recordings, user interviews). Define which matters to you.

Data Maturity. There are a range of software products available to help with this.:

Feedback Flow: Think about feedback broadly. Many teams overly focus on Onboarding and Activation feedback, but tend to miss feedback you can glean from marketing sites that caused the activation in the first place. When product and growth teams are sharing feedback the result is a more cohesive experience for the user and minimizes potential for user overwhelm from repetitive pitches asking for feedback.

Crowdsource ideas: At Vero, Albert made a simple form to send to teams and users soliciting their ideas. This data can then be taken and put into plotted charts accessible to the whole team so that they can see where priorities are for testing

Results: Over the past six months, Albert has implemented weekly testing to try various strategies and achieved excellent growth results, tripling the number of user sign ups. The speed of the experiments was key here; the data shows a high correlation between signups and the number of experiments being run. This is a sustainable strategy for long term growth.

The Love Triangle (Revised): The takeaway here is that all companies should put the user at the core of what they are trying to do. Doing this allows product, data, and growth teams to align their strategies around user impact across all stages, from acquisition to activation to retention to referral.

To Summarize: Start with objectives. Where is the company heading and what do you want to achieve this quarter? From here, determine: what qualitative or quantitative insights you need to capture to achieve this objective? If you don’t have the data to track and validate your assumptions, implement it. From that data you can plot a product roadmap that will inform future growth projects and initiatives. Once these are mapped you can start running growth experiments as a regular habit which in turn generate a feedback loop back to the insights phase. All of this should be framed against the user journey and user needs/impact. Regularly revisit and fine-tune your ops, processes and frameworks according to the insights you glean from experiments to ensure your processes are aligned with core mission. This is particularly relevant for startups: the results of your data will lead you to create the right framework. Ultimately your success will be determined by your team, your processes and the models you are using and how well they align.