Product management as a Service. Data-driven approach for you company
The main task of our product is to create tools to solve company problems. By tools, we mean both data management software and organisational processes that allow efficient data handling.
We help form or adjust target metrics, the automation of which will be set up by us in analytical tools. We also develop the process of continuous use of these target metrics for decision-making, argumentation, hypothesis formation, and evaluation of changes within the company.
Analytics, specifically its usage according to the methodology we teach managers, will help highlight more effective development directions and reduce the volume of tasks that do not bring results to the company. Merely having data does not solve all business problems; the key to success is the ability to quickly and accurately interpret and use them. Correct interpretation can only arise from the joint work of client experts, who know the company's processes best, and our experts, who are well-versed in data and management methodologies.
The main part of the product consists of on-the-job training, where we help company managers work with analytics using specific company cases, like player-coaches who are invested in achieving goals. However, before starting such training, several technical and organisational steps need to be taken.
Analysis and Audit Stage
Step 0: Define the Goal and Its Importance for the Company
Formulating the goal is one of the main stages for starting the work.
It's important to jointly describe the "picture of the future result" so that we are aligned.
Together we will highlight specific changes in specific business processes that the company needs. Each company will have its own suitable goals and tasks.
Examples:
#retail #B2B #sales
Improving the B2B sales process through customer insights and assessing the efficiency of current processes.
#retail #B2C #offline #marketing
Enhancing customer loyalty, increasing the average check (customer spending) through customer insights and customer journey analysis (adding user experience improvements to marketing processes).
#retail #SaaS #marketing
Increasing the company's market share (adjusting the strategic development process) through customer insights.
#retail #B2C #online #marketing
Increasing conversion to purchase (without reducing overall traffic) through a well-understood funnel optimisation process and customer journey.
#retail #B2C #offline #marketing
Making the merchandising process more efficient (increasing the check with fewer changes) through accurate calculation of changes' impact on customer behavior.
#retail #B2C #online #marketing
Improving the influx of new users/customers (increasing the average check) through implementing marketing campaign effectiveness assessment.
#internal processes
Reducing the number of issues in the hiring process by understanding the problems in the current process.
GOAL = PROCESS + KPI + DATA
If there are multiple tasks, they can all be identified, but they should be tackled sequentially.
Work Result:
Formulated goals
Proposal for joint work with exact audit costs and preliminary work cost
Step 1: Audit and Data Verification - Determining the As-Is Situation
Regardless of the set goal, we need to analyze the current situation. The scope of work at this stage may vary depending on the goal.
The duration of this stage can vary from 1 (one) week to 2 (two) months, depending on the goal's complexity and the company's size.
Interviews with key company personnel (mandatory)
Who is currently handling this?
How does this process work now?
Who is needed to make decisions on adjusting the current business process?
Competitor analysis (if necessary)
If the task involves customer behavior (offline or online) and the client does not occupy the largest market share, it is necessary to understand what customer engagement strategies competitors use.
Market analysis (if necessary)
To form hypotheses about customer behavior, we need to talk to them and understand how they can be segmented (by JTBD, consumer segmentation, etc.) and what problems they currently face. Knowing which segments our customers come from helps better formulate product offerings or assortment.
Audit - Internal tools analysis (mandatory)
What tools/data are available?
How are the data and tools used?
What documentation is available?
How well are the data collected and how frequently updated?
Introduction to developers
Benchmarking tools to choose the most suitable ones for achieving the set goals (if necessary)
Research which analytical or administrative tools are needed for data management organization. Their verification, demonstration to the client.
Formation and discussion of the further plan for achieving the goal (mandatory)
Work Result:
Market and competitor analysis results (if conducted).
List of people actively participating in the project.
Business: decision-making, training.
Development: execution, adjustments, implementation.
List of tools to be implemented both analytical and organizational (if necessary)
Recommendations for improving the current data collection and usage infrastructure
Goal achievement plan. Plan of technical and organizational activities, tasks, and responsibilities, including addressing critical remarks on data quality or collection methods.
List of necessary workshops, training plan
Final work cost based on the plan
Step 2: Incorporating analytical tools
This involves interaction with developers, installation, and software integration. The duration can vary significantly depending on the volume of necessary modifications, the quality of current data, the number of required integrations, and the complexity and size of the client's infrastructure. This is a joint effort of the company's developers with our analysts and developers.
Duration of this stage is from 1 (one) month to 6 (six) months, or longer in case of force majeure within the client's infrastructure beyond the contractor's control.
Work Result:
Data collection setup. Necessary technical work on tool implementation and integrations with different data sources completed
Data verified and ready for further use
BI systems configured and ready for creating visualizations and dashboards
Step 3: Connecting Data with the Real World
Parallel to the technical plan implementation, integration setup, and infrastructure configuration, additional steps need to be taken to improve the quality of analytics and the impact of its implementation:
Step 3.1: Detailing Goals to Low-Level Leading Metrics that can be monitored. Data on these metrics should be collected or created.
We work together with the client's team to develop a metric tree that will help us create analytical tools for monitoring and achieving the goal.
Examples:
Client Task: B2B business → improve sales process
Goals: Reduced average sales cycle time, improved win rate
Leading Metrics: Number of leads from inbound and outbound sources, percentage of qualified leads, number of first meetings, lead response time, number of follow-ups, percentage of lost opportunities with identified reasons.
Client Task: B2C → improve user flow to purchase (website or app)
Goals: Increase conversion rate, average order value
Leading Metrics: Entry pages with the highest drop-off rate, time spent on the site, number of sessions, average time to conversion, percentage of returning visitors, bounce rate.
Client Task: B2C → improve brand loyalty and recognition
Goals: Increase NPS, number of positive reviews
Leading Metrics: Retention, Repeated purchases, NPS by segment (geographical, demographical, behavioral), number of user-generated content, number of customer interactions on social media.
Client Task: Marketing campaign
Goals: Increase the number of unique users
Leading Metrics: Average click-through rate, cost per lead, number of campaign impressions, cost per thousand impressions (CPM), customer acquisition cost (CAC), customer lifetime value (CLTV).
This work will allow us to gather more detailed analytics requirements, align expectations, and ensure the entire team understands the significance of various metrics.
This involves active collaboration with the client's team over several iterations and workshops. At least two workshops are needed, but for larger teams, 3-4 workshops may be necessary. Each workshop lasts at least 1.5 hours and is conducted in a tool like MIRO where everyone can contribute. The team will receive separate assignments to establish metric relationships and evaluate their activities using metrics.
Overall duration of this step: 1.5-2 weeks
Step 3.2: Client's Company Organizational Processes Preparation
This is an important collaborative effort combining expertise in business processes, technical infrastructure, and data utilization.
We draw parallels between real-world events and specific data and tools, enhancing data interpretability and usability.
Examples:
We fully analyze the customer's journey from brand introduction to purchase and loyalty. We identify where these steps are reflected in various systems and how to digitise/evaluate the customer's journey using data. The task is similar for online, offline, B2C, and B2B environments, but the form and tools that collect data will differ.
Overall duration of this step: 2-3 weeks
Work Result:
Taxonomy. A document showing the relationship between real events and metrics (this could be a CJM, data and event matching file, etc., depending on the goal and task)
Technical specification for analytics development.
- Description of metric calculations (methodological and technical) for use by analysts, developers, and business teams for accurate data interpretation.
- Description of required visualizations/dashboards for work.
Metric tree. The relationship between key company metrics following the North Star Metrics methodology
Education and Collaboration
After the three foundational steps, we move to the main phase: an iterative and flexible approach to integrating data into the company's business processes, during which the team learns to interpret data and works closely with our experts.
Before training begins, we prepare the necessary foundation:
Create a basic set of visualizations for assessing the metric tree with real data and building dashboards.
Set up the infrastructure for collaborative work (MIRO, task planning tools, space for training materials, etc.)
We then develop four key skills through real projects and tasks:
I understand and interpret data.
I can make decisions based on data.
I can argue and justify my opinion using data.
I can request additional data.
The main goal of these iterations is not only to understand the data but, ultimately, to learn how to influence metrics by implementing changes. It's essential to see which changes best impact the company's target metrics. This requires a process of trial and error. Our experts will make this process more controlled and straightforward using their experience.
We work in weekly iterations. Every week, we hold a meeting to summarise the past week and plan the next.
Weekly Meeting Plan:
Assess the results of the past week's work.
Develop a set of hypotheses for achieving company goals based on the existing data. (Learning to interpret data)
Update and prioritize the plan (backlog) for achieving the goal, consisting of data-based hypotheses. Prioritization is done using the RICE method. This method adds objectivity and simplifies the process of choosing operational and tactical plans. (Learning to justify positions and make decisions based on data)
Form a work plan for the next week and assign tasks to each participant.
Determine the data needed to evaluate the effectiveness of the work done and monitor goal achievement. Set tasks for analytics implementation if it does not exist. (Learning to request data)
The team needs to get used to the routine of consistently using data, always looking at the numbers, and seeking explanations. The skill of data interpretation develops over time and requires consistent effort. It's like developing a good habit of exercising or following a healthy diet.
Overall duration of this phase: at least 6 months.
Work Result:
Internal decision-making processes using data are established. The team consistently refers to data for task evaluation and planning within regulated activities.
Project or hypothesis management and knowledge work tools are implemented and configured.
The team's work is documented in project planning and hypothesis management tools.
Ready dashboards and visualizations. Metric calculations descriptions, metric trees, and dashboards are used.
A team that can work with data and knows how to influence company metrics
Summary
Data alone doesn't solve business problems; success lies in quick and accurate interpretation and usage.
Correct interpretation requires collaboration between client experts, who know company processes, and our experts, who know data management and methodologies.
The core of the product is practical training, helping company managers work with analytics through real company cases.