It is 8pm. You are trying to figure out how you can manage to deliver your due diligence report in time. It could be about the ‘pipe thermal insulation’ market, ‘orthopaedic braces’ or ‘do-it-yourself t-shirt printing’. Any market you have never heard of before. Any market without any pre-existing study you could buy online or get from your coworkers.
In fact, you have no market data at all, and you are running out of time.
What to do when there is no reliable market data.
There is no proper due diligence without a clear vision of the demand structure. That is the only way to provide unbiased conclusions to the perpetual question: “is the demand growing sufficiently to secure an attractive return for investors interested in this company?”.
You have only two options. And none of them sounds nice …
Fabric fake evidence to back your conclusions & potentially put your boutique in jeopardy – or start looking for alternative data sources to analyse overnight.
You start googling at potential solutions, but you feel like hitting a wall. All you can find seems far away from what you would expect and need.
Many alternative data sources look quite complex & expensive to collect – as they are 1st-party data, owned by private companies & not directly available on the market.
- Credit card transactions
- Point-of-sale transactions
- Loyalty card history
Some geolocated data sources seem quite unnecessary to collect & analyse for your analysis. You are not willing to understand people or merchandise flows, you want to know what consumers actually want.
- Satellite images
- Shipping trackers
- Smartphone local pings
Then you start looking at the galaxy of available Web data. So many of them, so many service providers … Welcome to the infinite galaxy!
- Web site usage
- App store analytics
- Social media posts
- Online browsing activity
- Product reviews
- Price trackers
Every time you visit a website from a different service provider, you get told data can save the world (and sometimes do the job better than you do!).
You start thinking you just found the ultimate alternative source of data, so you order some comfort food while taking a product tour on YouTube.
10 minutes later, you finally click on ‘start my 7-day trial’, you create your account with your company credit card & start setting up your dashboard.
And suddenly, all hope is gone.
Why most Web data are not relevant for due diligence analyses.
Most Web data seem unnecessary to buy given the value they can bring for strategy consultants. Either as they are just too expensive ($5k to collect 10M tweets) or because they just seem way too complex to be analysed in a short amount of time (“What can I do with 10M tweets?”).
Just think about it: most Web data are only harvested for advertising companies – or large corporations in endless quests for “disruptive market insights”.
Most Web data are meant to be analysed to build audiences – not to identify market trends.
All these data sources are essentially 1st-party data even though most of them are anonymised then sold to large advertising networks – so that these can help companies detect relevant audience / buyer personas with less effort. But data processing cycles are complex to implement by essence. It would require you to access all your target company internal systems and data warehouses. Not something you can afford when you need to deliver a due diligence in just a few days!
Sure, some big consulting firms & agencies have started using these data in their reports. Relax, that’s not for due diligence! Advanced Web data analytics are mostly useful when consultants need to re-define the core strategy, offer or pricing for large B2C corporations like Toyota, Nokia or Nike. Not when thinking about how to evaluate the demand for house thermal insulation in Southern Germany.
Is it the end? Are you doomed to only rely on some qualitative interviews you struggle to get with a few competitors? Is due diligence a practice that cannot benefit from modern technology & reliable alternative data?
The answer is no, obviously! That’s because we haven’t talk about search data yet.
Why due diligences can strongly benefit from search data.
Every second, Google processes over 80,000 search queries on average. Google is definitely the leading search engine, but there are others: Baidu (in China), Naver (in South Korea), Bing (mostly in North America) & a plenty of smaller players.
What if you could analyse what people search all over the world – to get answers to your strategic questions? When properly aggregated, these search queries reveal what consumers want, all the time, anywhere, on any topic.
Search data are unbiased, as they represent what real people are looking for. Not bots, media or paid influencers. With search data, you can easily validate very precise hypotheses such as “is the demand for attic thermal insulation growing in Southern German states?” or “what is the seasonality for demand in life insurance in Brazilian major cities?” or “what are the best cities to open new shops of design furniture in Mexico?”.
Still there are three major challenges when it comes to find answers in search data.
- Knowing where to start. There are so many ways to search about something. And it is not easy to get a comprehensive list of what consumers are actually searching for (the search queries), before starting to aggregate metrics about the search intensity
- Relying on a powerful Web infrastructure. To fetch & store a massive amount of data points, such as the search volume of all search queries in all country regions, every month
- Benefiting from advanced data analytics capabilities. To quickly detect trends & statistical outliers in all metrics. So that you know instantly what market segment tends to rise in Southern France since the covid-19 crisis, while being stable in all other regions.
Good news! That is exactly what we offer at Trajaan with our Search Listening Platform. From our dashboard, you can get precise insights about how all market segments generate interest for local consumers in less than 48 hours – so that you can quickly support your conclusions with reliable data points.