How to create a digital product that will prove to be an innovation and meet the client’s business expectations? Product Discovery for TUATARA￼
Creating digital products is not an easy task. Creating digital products that stand out from the competition and hit a business jackpot is hard work that requires extreme focus on the process, agility, and an open mind. Nonetheless, it’s the kind of challenge we love at Project: People. Therefore, it is with great delight that we provide you, dear reader, with extensive and detailed material that will help you understand:
- what Product Discovery is and how the process should be conducted under the conditions of extreme uncertainty,
- how to design quantitative and qualitative research to minimize the risk of cognitive errors,
- and finally… how to assess whether your idea for a digital product has a business and market justification.
TUATARA combines consulting with technology to create unique solutions for their clients – from back-end integration and process digitization to creating innovative business models. The company provides services to ensure digital excellence at every stage of the customer life cycle. TUATARA specializes in business areas such as marketing, sales digitalization, and digital customer care processes.
It is a company with roots in the services industry that also has a portfolio of ready-to-use and easily scalable products that are an important part of the organization’s growth strategy.
The company’s current portfolio includes products such as:
- ACTIONBOT – a digital assistant based on AI solutions that proactively responds to user behavior offering support in the implementation of purchasing, and post-sales processes,
- sensID which uses cognitive technologies such as NLP and machine learning to digitize document workflow and personal data management in accordance with the GDPR regulation,
- RETIXA – a tool for real-time implementation of marketing campaigns,
- TASIL – a solution for data monetization,
- and the FINTIN platform – a set of ready-to-use modules for the financial services sector.
However, the time to expand the offer has come. Expand by what exactly?
Our task was to find the answer to this question during an intensive 3-sprint collaboration with the client, which included a Product Discovery process. Read on if you want to know where we started, what the process looked like, and what we came up with.
Before we start – the goals and objectives of Product Discovery
Product Discovery is an initial stage of creating a product – most often a digital one. Its goal is to verify hypotheses on problems of a potential customer, gain new information, and define the stages of design work.
The Double Diamond model is used to visualize the whole process of product development. The first diamond illustrates the Product Discovery phase. It is the stage of gathering as much information as possible about the target group (discovery) followed by elaboration, validation, and conclusions (define).
Read more in the article on Product Discovery.
Sprint 1 – desk research, workshop and sprint planning, preparation of research tools
The TUATARA team had extensive materials, preliminary analyses, and concrete ideas for product development.
In such a situation, it is important to align knowledge at the lowest possible cost of transferring information between the teams. After all, there is no point in repeating the analysis that had already been done at a high level.
Therefore, we started with a thorough analysis of the extensive materials we had received from the client. It is worth mentioning that these materials enabled us to execute the plan in such a short time. The materials included:
- preliminary hypotheses regarding the vision of product development;
- preliminary assumptions regarding the target group and their problems;
- ideas for value proposition;
- ideas for implementing the business model;
- and an array of other design information.
The analyzed materials helped us “enter the client’s mind”, understand their thinking and decision-making process. They also constituted a crucial element in planning a kick-off workshop and a detailed workflow in each sprint.
Workshops and sprint planning
The kick-off workshop plays an extremely important role in the process. It is the moment when the client gets to know the project team. It is an opportunity to exchange knowledge, conduct some exercises (including integration exercises), and plan the next steps.
When designing this meeting, we always keep the main goal of the project in mind. In the case of the Product Discovery process, it is important to determine the appropriate research hypotheses and design the entire strategy for verifying them.
We divided the meeting into three main stages. After filling in the Team Canvas used to discuss project goals and establish the principles of cooperation, we moved straight to:
- Discussing the past
At this point, we talked about the context of the project. We focused on decision-making and the thinking process. It allowed us to make subsequent micro-decisions (there were plenty at each stage) more effectively and efficiently as we understood the project from the client’s perspective. It sped up the pace of work and minimized the risk of misunderstandings.
- Discussing the present
In this part of the workshop, we answered some questions about the current state of our knowledge: which information has been reliably verified? Which are merely hypotheses?
Given the high degree of uncertainty of the information we had to rely on in the successive stages of the process, we decided to prioritize them.
- Discussing the future
In this part, we focused on constructing a preliminary research plan and on ideas for verifying hypotheses in line with the Product Discovery process.
At the end of the workshop, we traditionally asked all participants for honest feedback. It allowed us to organize better meetings and account for the specifics of the client and their expectations. We ask for feedback at the end of practically every workshop.
- Communication and collaboration: Google Meet + Miro
- Organizational arrangements: Team Canvas
- Talking about the past: casual interview and focus group + mind-mapping
- Talking about the present: Lean Canvas + knowns/unknowns matrix – important/unimportant
Organizational arrangements (i.e., what we based the collaboration on):
- Asynchronous communication on a dedicated Slack channel
- All project documentation was developed using Notion, which can be accessed in real-time by all project stakeholders
- Daily in a written form – a plan for a specific day
- Weekly meetings with a summary of sprint deliverables
Starting point: hypotheses and research persona
Apart from integrating the project team, the kick-off workshop was the starting point for the research tool development. The participants were asked to place the hypotheses and other information on a knowns-unknowns/important-unimportant matrix. After the workshop, we analyzed them in detail.
The first step was to export the acquired data to the appropriate tool. Miro is a great platform for collaboration, but it is not suitable for data analysis or automated work on figures. Therefore, all topics were transferred to a database in Notion and then quantified. What exactly does this mean? We are going to explain it in a moment.
Sorting and quantifying the hypotheses involved transferring the values from the known-unknown/important-unimportant matrix onto an appropriate scale. Next, we calculated the average for each area. The value referred to as “the overall score” is the ratio of the hypothesis’s significance to its uncertainty (the higher the score, the higher the significance and uncertainty of the hypothesis). This procedure allowed for an error-free prioritization of research areas – it helped us decide which hypotheses needed further verification.
Some hypotheses had to be deepened and systematized. Therefore, we conducted further exploration intending to gain a more profound understanding or concretize the area under study.
We assigned hypotheses to the various segments of the Lean Canvas and categorized them, which proved to be an invaluable aid in constructing the research tools.
The refined hypotheses were the starting point for further work – constructing the research persona and clarifying the overall research strategy.
- Communication and collaboration: Google Meet + Miro
- Analysis of results: Notion database
- Hypothesis segmentation: Lean Canvas
- Hypothesis categorization: mind mapping + Lean Canvas + Notion database
When creating a research strategy, start by setting research objectives. These may but do not have to correspond to the overarching project goals identified during the kick-off workshop as purpose and mission:
Although the strategic goal of the entire project was to verify if the product would find recognition in the market, when constructing the research objectives, we decided to take a low-level approach to this topic (according to the SMART goal planning method).
Together with the client, we decided that the research must help us with two major issues:
- Determine general direction: which way do we want to go with the product?
- Validate the target audience: who sees value in a particular solution?
In other words, we decided to validate two areas of the Lean Canvas that are the absolute foundation of any successful digital product – the customer segment and their problem. Of course, we were aware that concrete answers to such questions would not be possible after only 3 weeks of work (the standard Product Discovery process takes much longer, at least 6-10 sprints). However, following the agile approach, we believed that each day of work would bring us closer to the right solution.
Given the scope of issues, we found it necessary to introduce several complementary research tools. Namely:
- quantitative research, which was to help clarify and narrow the hypotheses and target group, obtain insights directly from the market, as well as study the “language of the target audience”;
- in-depth netnography, which we treated as a supplement to the questionnaire – it organized and deepened the acquired data;
- hypothesis dry-testing: individual interviews with people working in e-commerce (additional validation, exploration, using the “leverage effect”);
- ongoing research that deepened and organized the results of surveys and netnography (e.g., detailed analysis of a particular e-commerce segment or a selected competitor’s solution – depending on current needs);
qualitative research (individual in-depth interviews with a particular research persona).
Such a construction of the research plan gave us a guarantee that:
- quantitative research in the form of “wide net casting” would refine the hypotheses and help discover preliminary patterns in the target group resulting in a higher quality of the collected data;
- hypothesis dry-testing allowed us to refine assumptions, goals, and better plan the course of the actual qualitative research;
- in-depth netnography as a complementary study helped us better understand and organize the results from the quantitative and qualitative research;
- a proper qualitative study was the culmination and final validation of the individual hypotheses.
The modularity of the plan was crucial to the success of the research – such a construction of research tools allows for working in specific research loops.
We discussed the strategy in detail in a separate meeting with the TUATARA team. After presenting the strategy, we moved to the workshop part. Each participant in the meeting had an opportunity to share their doubts and point to the elements of the upcoming Product Discovery process that they found critical.
It gave us a chance to work through the plan together, make sure everyone understood the scope and potential risks (which are never in short supply), and get unanimous approval on further steps.
The plan was well received. We immediately set about implementing it as it was ambitious and the schedule was tight.
- Communication and collaboration: Google Meet + Miro
- Sprint planning: Notion timeline + Notion boards
- Workshop part: mind mapping + “cool/not cool” matrix
For me, cooperation with Project: People means flexibility, high quality of reports, extensive summary materials, and process transparency. We worked in sprints, we knew the scope of cooperation perfectly well and used tested tools (sprint planning, weekly, retrospective, etc.).
Moreover, the complementarity and logic of actions and the workshop style of working on the materials. We had constant access to materials developed by the Project: People, and we could make changes to them if necessary.
It felt like we created one harmonious team for the duration of the project!
Sprint 2 and 3 – research, analysis of results, conclusions, and recommendations
The logical division of hypotheses we made during the analysis proved invaluable when developing the quantitative research tool. We arranged the hypotheses in an order corresponding to the information structure and the natural process of obtaining data.
Apart from the hypotheses developed during the kick-off workshop, we also created the so-called superior hypotheses to build the research tool. Why?
As it turned out during the logical analysis, some of our hypotheses were low-level and referred to very detailed aspects. However, we aimed at the exploratory function of the survey, so excessively detailed questions would have distorted the results.
The research questions were divided into the following modules:
- screening questions – the purpose was to reject the answers of people who did not match the research persona;
- target group classification – the purpose was to identify particular patterns in the target group to narrow down the research persona;
- exploratory questions regarding the possible directions of the tool development.
Each question was prioritized and included information regarding the hypothesis it referred to – it facilitated the subsequent analysis of the results.
The questions were reviewed with the TUATARA team, who provided feedback and ideas for improvements. Next, we translated the questionnaire – the plan was to distribute it in both Polish- and English-speaking communities.
After the final approval of the questionnaire, we proceeded to the distribution. It proved to be more challenging than we had initially anticipated.
We distributed the questionnaire at regular intervals in nearly 100 places on the Internet. We were continuously controlling the Click Rate (percentage of people who opened the questionnaire) and Fill Rate (percentage of people who completed it). When the feedback suggested that some people resigned from completing the form due to its length, we optimized the wording and the scope of the questionnaire.
In the end, we ended the survey with fewer responses than we anticipated, but the data we gained was more than enough to identify distinct patterns in the areas we cared about most.
- Questionnaire design: Notion database
- Questionnaire feedback: Notion
- Research tools: Google Forms
- Questionnaires analysis: Notion database
- Distribution: Facebook groups, Linkedin, private channels, discussion forums, communities gathered around specific e-commerce engines, etc.
What I particularly remember about the cooperation with Project: People, is the insight into the various stages of the project and the possibility to make changes on the fly. I have never encountered such transparency of work or communication (“daily” written in Slack channel, weekly meetings, additional workshop meetings if something needed to be processed collectively, etc).
It is also worth mentioning the extensive summary materials, which included lots of quotes from qualitative research – they not only supported the results but also gave additional insight into how our target group thinks and acts.
While working with Project: People, I learned a lot about the research process – both on a strategic and operational level (e.g. recruitment – how much time should we allocate). For me, the whole project was a great adventure!
As researchers we greatly appreciate the potential inherent in talking directly to representatives of a target group. However, in the case of this project, before starting the actual research, we decided to talk to the so-called “aggregators”, i.e. people who “aggregate” collective information about the target group we wanted to reach. In this case, they were people who work with online store owners and managers.
We commenced recruitment and prepared a research tool in the form of a scenario. Using private channels and the Linkedin portal, we reached out to consultants and suppliers of technologies for e-commerce stores. During the interviews, we wanted to:
- specify the hypotheses on which we worked;
- pre-validate the patterns identified during the quantitative study;
- obtain information on the vocabulary used by the target group.
The scenario for this study consisted almost exclusively of open-ended exploratory questions and was narrowed down thematically.
In retrospect, we can say that this form of research was vital for the course of the project – it provided a lot of valuable information. However, we must not forget that these interviews were not interviews with the target group. It should always be taken into account when analyzing the results and utilizing the conclusions of such research.
Based on all the previously obtained information, we were able to start the actual qualitative research in the form of in-depth individual interviews with people who matched the research persona.
Constructing the relevant persona is the first step in the recruitment process. Thus, the data obtained from the questionnaire and hypotheses dry-testing proved invaluable. Furthermore, netnography, which was carried out all the time, turned out to be very helpful in structuring the data on the persona and placing it in the market context.
As the people we were trying to reach held high managerial positions, recruitment was not easy. Arranging appointments took more time than originally planned. Eventually, everything worked out and the quantitative (as well as qualitative) goals were met.
The research scenario included questions taking into account issues that emerged from previous research (quantitative and netnographic).
What is equally important, the scenario included information on:
- the priority of the question,
- the purpose of the question,
- the hypothesis that the question referred to,
- and the area to which the question was related.
Given the flexibility that the researcher needs during the interviews, such information was of utmost importance. It often happens that an interviewee spends a great deal of time on one particular question, but the information you obtain is too valuable to interrupt them. In such a situation, due to time constraints, it is unlikely that an interviewer will manage to ask all the questions. Therefore, prioritizing questions allows the interviewer to quickly ascertain which ones can be skipped without harming the project.
The element that tied all the other research tools into one was the netnography conducted throughout the project. Netnography applies ethnographic research to the digital world. It is an analysis of behavior within a community. While ethnography is used to study ethnic groups and occurs in the real world, netnography has almost unlimited possibilities, better suited to today’s digital reality.
In practice, it can entail the analysis of places on the Internet where representatives of the target group are present. In this project, it was easy as people managing online stores are often active on Internet forums and Facebook or Linkedin groups.
Netnography played both an exploratory role (allowing us to discover the most burning topics and problems our target group is facing) and a verifying role (it helped us to better understand the acquired information).
The verified information was aggregated in the database, assigned to appropriate categories, and then subjected to scoring. This enabled faster analysis.
Summary of activities and recommendations
After the research was completed, the most important thing was to summarize the findings.
Using this many research tools resulted in collecting an incredible amount of data. The exploratory nature of the research yielded a wide range of topics that required synthesis.
We started at the stage of extreme uncertainty, which we gradually leveled out. We had an enormous amount of new information requiring analysis, deepening, and synthesis – depending on the context.
Nevertheless, we succeeded! The presentation prepared at the end of the project included:
- general information about the hypotheses developed at the beginning of the project;
- patterns identified in the target group;
- high-level recommendations for further steps;
- low-level suggestions on how to implement these actions;
- and all sorts of tidbits that might prove invaluable going forward.
We know that for TUATARA this is just the beginning of the journey. However, we believe that the first steps we helped them take will turn out to be a good investment in the product they are building.
We keep our fingers crossed for the process that the client will continue with their internal resources. We hope we will soon hear about a product that will shake up the e-commerce market!
During the cooperation with Project: People, I learned a lot – both about the Product Discovery process and about the scope and the ways of using tools like Notion or Miro. The cooperation was very flexible, transparent, and agile – we were discussing problems and challenges on a current basis and considering possible solutions having the project’s best interest in mind.
The Project: People team was constantly asking for feedback and reacting based on the acquired information.
I am also satisfied with the effects of cooperation (e.g. number of interviews conducted in a group that was really difficult to recruit – especially in such a short time). Let’s remember that this project was not easy.
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