There’s a New ‘King’ Among StartupsBy Guillermo Ruiz Ayllón | Fri, 08/20/2021 - 12:58
Like a big wave, technology is rushing unceasingly toward the actual state of play of entrepreneurship. The current scenario for startups is shaped by the revolutionary uses of techniques and methods like deep and machine learning, blockchain or mobile connected devices. This creates a unique landscape where customer-behavior analysis is king.
The millennial generation is now accessing executive roles, while generation Z looks promising, with better jobs and salaries than previous generations at the same age.
This perspective based on generations can demonstrate numerically how important this change is, but the revolution doesn’t stop there. Think for a second about how technology has also changed baby boomers. Isn't it true that their access to technology and how they find, compare and buy products has changed dramatically? Their behaviors are now different and the available information that companies can analyze to understand them better has increased exponentially.
Here, in customer behavior, is where the battle is taking place (and always has been) in this new era of marketing and entrepreneurship. To understand its importance, let's analyze two concepts: assumptions and authority.
In different areas and along the various steps in any company, assumptions are key to developing competitive advantages. Great ideas, mostly a result of out-of-the-box thinking, are normally the starting point to develop products, services and new features.
Someone enters the office and says to the team: “My neighbor just bought a lock for her house and now she can open it without keys. What if we do this with our cars?” The assumption here that a lock that can be opened via an app can be applied to a car seems logical, but where are the clues?
Let’s continue with the last example: If the person that entered the office is an intern, the assumption will be seen very differently than if that person is the CEO. This also has some logic, since the concept of “role” is an efficient way to identify authority, but, of course, it is not a predictive way to measure how effective the idea can be. Where is the real evidence that indicates that cars will also be a good place to install those locks?
The hierarchy of the structure dramatically affects the way in which authority is managed in a company, creating different environments where decisions are made. During the last few decades, we assisted the objectification of this authority to accept more assumptions from within the company, changing the core of the decision from personal authority to what we can call “data authority.”
This correlation between the number of assumptions tested and the number of data collected and analyzed is clear: the more data collected, the more experimentation and, if we expand this correlation to the number of new features or products launched in recent years, we can also conclude that the “more data collected, the more successful experiments and, subsequently, better results.”
With this in mind, the technification of customer behavior is now a need, not only for marketing, but for all the areas in a company. As Philip Kotler said one time: “Marketing is too important to be in the hands of the department of marketing.” We can rephrase the quote to, “Customer behavior is too important to be in the hands of the department of marketing.” And here is where the challenge comes.
To be really data-oriented is a hard job for any company; this includes an architecture that allows predictive models as the final step, with crucial steps between where insights are captured, modelized and analyzed. Based on this, assumptions like that which states that any user with this data is a good user a priori are changing some industries. Fintech is one of these places where we can see at a glance that new models are changing the landscape.
Finding Relevant Data for Fintech
Competing with big financial companies is not a task that startups can do in easy mode. Even with this new orientation to data, final decisions like disbursing a loan or accepting a credit line for a new user, are complex and uncertain. Fintechs now can work with two types of data to make better decisions: external data (sociodemographic, previous experiences with other companies) and product data (type of use of the product/service, accurate information provided, needs identified, for example). In both categories, there are terrific solutions that didn’t even exist five or 10 years ago but which are now in mass use by startups. Let’s see some examples:
- Technology for identifying and acquiring new customers: Platforms like Facebook Ads, Google Adwords or Amazon Ads have hundreds of attributes to target our ideal users. Even if we are looking for concrete information, such as which fintech brands the user liked, we will be able to address ads to the specific audience.
- Technology for collecting and unifying data: As we said, collecting data is a must for fintechs. Platforms like Segment.io or C3.AI let you unify all your data and build AI models. In the same way, CRMs are widely used to unify information about users, mostly in B2B.
- Technology for data visualization: Data also needs to be communicated in an efficient way. To do that, platforms like Google Analytics or Amplitude are normally used by fintechs to obtain charts and graphs with KPIs and crucial information for the company. Business Intelligence platforms go a step further and allow users to create complex dashboards: Tableau, Clickview or Data Studio are the big players in this sector.
- Technology for specific analysis: Fintechs can now do anonymous screen recording of their users to better understand the customer journey and detect UX/UI issues. Hotjar is the platform leader and there are other software solutions for capturing bugs automatically, analyzing the speed of any feature and so on.
- Technology for customer success: The communication with users is one of the better sources for relevant insights in any fintech. Live Chats and bots are growing very fast among startups and it seems that will be (if it is not yet) the standard in the next few years. This means that these software platforms won’t be just a way to manage conversations, but also the place where important insights are collected.
This technological revolution offers great opportunities to fill the gap where financial needs are still uncovered or at least to experiment with solutions based on data to do this. And this exploration is probably just the beginning, given the speed at which technology has been moving in recent years. Investors like Cathie Wood are betting on AI and deep learning and tech leaders like Jack Dorsey and Elon Musk are supporting these technologies. Meanwhile, fintechs are awaiting the next feature that will allow them to improve the virtuous circle — assumptions-experimentation-results — with the certainty that investments in customer behavior have been among the most fruitful so far to develop products that solve users’ needs.
Customer behavior is now affected by data more than ever and the discipline will evolve faster thanks to technology like AI or deep learning. In this context, financial decisions are dramatically affected, creating a perfect storm to develop products oriented to target markets that, traditionally, have been ignored.