Meet the Space307 Strategy team — Head of Strategy Kirill Shikhanov and Strategy Analyst Artyom Khanganu. In this series of articles, we’ll talk about competitive intelligence (CI). Today’s piece touches upon CI projects developed by our team to meet the specific needs of Space307.
Our team defines intelligence as processed, structured and analyzed information used to make business decisions. Competitive intelligence involves acquiring, analyzing, and utilizing information about your competitors, such as their market position and target audience. CI helps identify the advantages of a business’s rivals and develop strategies that can strengthen a business’s position. Together with business intelligence, CI is a crucial aspect of market analysis, evolving alongside competitive market analysis tools.
Marketing intelligence vs. competitive intelligence
Competitive intelligence is a fundamental component of marketing intelligence, which encompasses tools for collecting and analyzing data relevant to a company’s marketing efforts, such as marketing landscape, target audience behavior or market trends.
The four vital elements of marketing intelligence are:
- Competitor information: Analyzing your rivals’ business details to improve organizational strategy.
- Product information: Understanding product and pricing differences and marginality.
- Market information: Calculating total market size, defining niche audiences and predicting market trends.
- User information: Collecting audience feedback to understand product and brand preferences.
To keep up with the constantly evolving industry, it’s essential to determine the markets of interest and evaluate competitors’ positions. At Space307, we introduced the SAURON project to help us with this.
SAURON = Substitute Analytics + User Research + Ongoing Niching
Before SAURON, manual market research and data gathering were the norm. Typically, online CI projects use one or several of the following tools and sources:
- Data AI (ex-AppAnnie)
- SensorTower
- SimilarWeb
- Semrush
- Klue
- Google Alerts
- Google Trends
- Statista
- Competitors’ news, press releases or social media pages
These tools and sources mainly provide ballpark figures, but that’s enough to get an understanding of how the leading market players are performing. They can help assess competitors’ audiences, check their regions of operation or find out what keywords they use for promotion.
To conduct a comprehensive competition analysis based on this data, it’s essential to:
- Develop a clear definition of the market of interest.
- Figure out the best approach to automating the collection, aggregation and analysis of data, and drawing data-driven conclusions.
We wanted to develop a project that would help businesses streamline this process. Meet SAURON — not the bad guy from Lord of the Rings but rather a credible approach to quality CI.
SAURON = Substitute Analytics | User Research | Ongoing Niching
SA = Substitute Analytics. Analyzing competing solutions for the user problems our products solve is essential. Those substitute solutions are basically our direct and indirect competitors. It’s equally important to perform a quantitative analysis of active users, new users (or app installs) and transaction volume (if possible).
UR = User Research. This part answers the question of how users perceive the solutions they’re offered and what associations they have with them.
ON = Ongoing Niching. For better analysis, we continuously compare our services with what niche players offer. This way, we can make sure that we’re offering a competitive solution.
The SAURON project empowered us to analyze, explore, and identify growth areas for our products, enhancing our positioning. Let’s explore the expert and technical approaches that ensured the successful implementation of the SAURON project.
Weekly competitive intelligence reports
We operate in the mobile app market, so we decided to integrate Data AI (formerly AppAnnie) into our data warehouse. We also subscribed to API 1.3 and API 2.0, which had already promised better performance at the time.
With the Data Platform team, we implemented necessary ETL procedures to load the researched data by key players in the market. The following steps ensure incremental updates and help avoid possible failures:
- Step 1: Extracting statistics by requesting over a thousand app IDs from the DataAI API.
- Step 2: Transforming the resulting data array into our storage format.
- Step 3: Loading the raw data into the analytics repository.
The next step was to present data to the stakeholders. We wanted to share not just numbers but also the entire business context. So, we grouped raw data into showcases using dbt (data build tool).
Visualizing and showcasing numbers is usually associated with creating dashboards for managers and analysts. But different people interpret graphs differently, so our team wanted to break free of the confusing chart pattern and present the CI findings in a more accessible way.
“We turned our competitors’ logos into emojis and added them to Slack. Research data and metrics now can also be represented by emojis. Our CI reports now look more like infographics than typical dashboards or tedious longreads.”
Kirill Shikhanov, Head of Strategy
We then took this a step further and developed a Slack bot that published regular CI reports in the relevant channels. These reports contained regional market insights, featuring both overall figures and competition-specific data (market share, audience, etc.).
We configured the bot to send weekly reports on more than ten regions to relevant channels. This way, our team could easily keep up with market trends and discuss them right in the app.
From the technical standpoint, this was a static version of the bot. We wrote a Python script that was launched from Apache AirFlow on a schedule similar to cron. This simple approach allowed us to get the hang of markdown and send messages to Slack channels using webhooks.
We launched the MVP version right away. The entire team could now access figures provided by Data AI because we eliminated the burden of visualization tools or accounts. Thanks to the script configuration, we could quickly add our bot to a new channel in case of expansion.
Enhancing the strategy bot: Dialogs
Our bot needed to evolve as we received numerous requests to make reports more comprehensive and add data granularity options. So we developed a dialog solution.
First, we needed to construct a backend by employing a service that registers actions from users in Slack and processes them. The Slack Bolt framework helped us quickly design related functions and connect the service with the corporate messenger.
“Our bot can now respond to interaction attempts from the users. We employed basic markup skills and used Block Kit so our team could easily interact with the bot through the menu or request specific reports. When you text the bot, it responds with the menu propmt with navigation buttons.”
Artyom Khanganu, Strategy Analyst
The interactive version of the Strategy SAURON bot with basic functionality was deployed in the Data Platform team’s infrastructure. The project received a repository and versioning support. The deployment scenario is as follows: BitBucket — > TeamCity — > Kubernetes. This scenario allows merging new branches into the master and deploying new versions in just a few minutes.
Throughout 2023, we were adding more features and data to the SAURON analytical service. The tool now splits data into two segments: Competitive Landscape and Competitor Profiling. Each segment has its unique features and helps complete specific tasks.
To fully cover the features of our project and approaches to CI at Space307, we’re launching a series of three articles. This article discusses the project background and MVP deployment. The two future pieces will focus on how we improved market statistics reports and automated the evaluation process. Oh, and we’ll share our plans for the Sauron bot, of course. Buckle up — exciting details are coming!
