User generated content at scale with Stackla
Overview —
In just 6 years since launching in 2012, Stackla transformed from a simple product that published "Tweets on a page" to an enterprise level offering used by over 500 customers including Apple, Disney, McDonald’s, Toyota and more.
Stackla could leverage AI to build rich libraries of visual content by actively discovering, recommending and rights managing content from across the social web to fuel personalised content experiences at scale. However, the Stackla app was plagued with product features that were difficult to discover, complex to understand, and often under-utilised.
Problem statement
A large portion of new product features were difficult to discover, complex to understand, and generally under-utilised. How might we enable customers to harness the power of UGC at scale, maximise their return on investment and minimise customer churn?
Our goal for the project was to unify the term creation process and streamline the first-time-user-experience.
The original premise was simple: Connect the brand's social accounts, adjust a few settings, get content at scale. However, we didn't want to miss the opportunity to allow more experienced customers to leverage advanced product capabilities within Stackla.
We aimed to create a solid foundation for first time customers to achieve success with UGC, and set them up to adopt more advanced product features as their uses-cases evolved over time.
Mission statement
Unify the term creation process and streamline the first time user experience
Read the full case study below, including snapshots of the product design process, artefacts and deliverables.
My contribution
UX strategy and research Design thinking and workshops User Interface and interaction design QA testing and acceptance
The team
1 × Co-founder 1 x Product Director 1 × Product designer (Me) 1 x Back-end Developer
Date
2018
Process —
With existing data around term creation and usage, we had some understanding of where customers were able to set themselves up for success, and where there was room for improvement. What we did not understand, however, was existing customer sentiment around term creation and where the gaps were between customer expectations and product capability.
Understanding customer sentiment and pain points. What do customers really want and need?
We mapped out the existing customer journey vs the ideal customer journey. Our goal was to identify any gaps between the two flows. By engaging the customer success team, we were able to overlay general customer sentiment and concerns at each step in the customer journey. Subsequently, we were able to use these insights to explore how we might minimise friction in the flow and explore opportunities to delight the customer.
Map existing user journey vs ideal customer journey
- Identify significant gaps
- How might we bridge these gaps?
Overlay customer emotions at each step of the journey
- What are users feeling at each step in the flow?
- How might we leverage positive feelings?
- How might we mitigate negative feelings?
Identify opportunities to minimise friction and create delight
- What questions do users have at each step in the flow?
- What do users think they need to minimise effort at each step in the flow?
Key takeaways
- Term creation is a complex concept for new customers to understand
- It's difficult for users to understand how different term creation settings affect content aggregated (until the content is actually aggregated)
- Setting up multiple terms with similar parameters across different social networks is clunky and repetitive
Mission statement
Simplify the term creation process for first time users and streamline the journey for existing customers, with a focus on increasing the volume of brand content aggregated.
Prioritising insights, forming a strategy and presenting to stakeholders for review.
By overlaying customer sentiment, questions and concerns on the existing customer journey map, it was clear that customers expected the initial term creation experience to just work with minimal effort. This led to an exploration of potential assumptions that could be made in the back-end (to minimise user input) and delight the customer (by potentially displaying a live stream of content based on the term settings).
Customers expected the initial term creation experience to just work with minimal effort.
We gathered these insights and began considering potential product features that could provide value to the customer journey and categorised them in an affinity map. After refining this, we came up with a list of product features and user stories that addressed the customer pain points and aligned with their wants and needs. These were then plotted on an effort vs impact matrix and prioritised using the MoSCoW prioritisation method.
Commencing the overall design and focussing on specific feature/flow highlights
Three key questions informed my design strategy:
- How do we make term creation simple for first time users?
- How do we avoid oversimplifying term creation for power users?
- How do we minimise unpredictable content results?
The goal was to create a process that would serve first time users and power users alike.
Based on insights from the research phase, I proposed a single entry point to creating multiple terms at once (where term creation was previously undertaken on a network by network, keyword by keyword approach). Central to this feature, was the ability to unify the creation of terms across multiple social networks by:
- Leveraging AI to identify hashtag and keyword density associated with a brand (this would allow customers to simply connect their social accounts, and AI would do the rest).
- Generate a live stream of preview content based on the suggested term settings (and display that to the user in a way that looks and feels similar to a brand's end-display).
For example, if a customer connected their brand's social accounts, AI could identify the most popular hashtags, keywords, pages and follower posts etc associated with that brand and suggest the creation of multiple terms based upon that data. And we could generate a live stream of preview content and display that to the user in a way that looks and feels similar to a brand's end-display. This would allow the customer to enable or disable suggested terms visually.
Outcome —
In addition to harnessing large volumes of user generated content and improving customer onboarding, this product release paved the way for two additional product opportunities.
- Rights Management at scale - How might we automate the process of requesting rights for content at scale?
- Visual Asset Manager - How might we enable customers to organise and manage content at scale?
Once launched, this trio of product features played an important role in the eventual acquisition of Stackla in 2021 by Nosto.
In 2021, Stackla was acquired by Nosto - a Commerce Experience Platform providing brands with the data, content and deliverability capabilities they need to create truly personalised, authentic shopping experiences at the scale.