Vision, Teamwork, Good Hires: How Technology Startups Succeed
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The gestation period for tech startups and other businesses is an exciting time for innovators who are developing “the next great product.” At the same time, it can be a period fraught with peril due to uncertainties that include the lack of clarity about how the product will evolve and how it will change five years down the road.
Initially, there is a ramping-up phase that requires those involved to get past the Minimum Viable Product (MVP) period and start to build the team. That means determining who to hire, when, and how many. Another challenge for technology startups is the tendency to focus on profitability in the early stages of development. This is a mistake. Alternatively, find the right investors who share the entrepreneur’s vision, and take the time to plan. Be patient! Failure to make the right decisions at the right time can derail a project or end its life cycle early. That’s why it’s important to go through the process—from ideation to testing to MVP and on to full production—and to do so methodically, making adjustments as necessary, along the way.
Growth over profitability
Business startups can take a while to evolve. Starbucks did not become profitable for almost a decade, but the iconic gourmet coffee giant took the long view as it developed a solid game plan. Uber evaluated its transportation model in the San Francisco Bay area initially. Once leadership saw the path to profitability they began to expand. YouTube—after launching initially as a video dating site about twenty years ago and failing to gain user traction—didn’t make money for 15 years. Google CEO Eric Schmidt saw the future, however, mandating that YouTube focus on aggressively growing the site, aiming “to grow playbacks to 1b/day [one billion per day].” It was four years or more before Facebook turned a profit. Adding features to keep users engaged will foster loyalty long term for a digital platform.
Do the legwork first
All startups, large or small, should incorporate these key steps:
Conduct extensive research first. Map out the features your product will include. Assess the value of those features to potential users/buyers. Make sure there is a marketplace need.
Raise the seed money for the implementation of the proposed MVP. Sketch out both the short-and long-term vision for potential investors. Once the MVP has proven to be successful, seek additional funds for expansion.
Evaluate the competition. Identify a special or unique niche your product and business model can fill. Then, be the first to market.
Start with a small team. Conserve resources and add more talent during the scale-up phase. Review data metrics on a consistent basis to help plan any course corrections.
Make sure users are happy using your product. That leads to good word of mouth, favorable reviews, and, in some cases, exponential growth.
Concentrate on the long game. Create a well-thought-out methodical expansion plan that allows employees, investors, and maybe even customers to get excited about the future.
Clarity in the startup phase is crucial. It is also where a neophyte business can pivot and go back to the drawing board if it turns out that the product is headed in the wrong direction. Using feedback from MVP testing and data metrics, developers can tweak products in the early stages before spending too much and prematurely hiring top-flight talent.
Employ a lean team early on, typically programming engineers and those on the business side of operations. Recruit product managers when there is a viable product. In the scalable, growth phase, bring in a leadership team that can leverage what was developed as an MVP. Topflight management is a major asset when progressing through the various stages of funding needed to ensure specific growth milestones. That may also require leadership to create new iterations of a product until the absolute best results are achieved and everyone involved gives their buy-in.
Bottom line: recruit those who understand the process and fit seamlessly with the company culture. Remember, in the current hiring environment, the demand for topflight tech talent (data analysts for example) is high. Once the right people come aboard, leadership must develop a company culture that encourages employees to stay on the team. Constantly having to bring in new hires and then train them is an expensive proposition.
Adapt to marketplace changes
Uber started out Uber Taxi, an app that helped users connect with a traditional taxi service. By morphing into the Uber of today, the company was able to avoid some regulations traditional taxi companies must conform to. The Uber business model also provided thousands of people with part-time, side income opportunities. During the height of the pandemic in 2020, the Uber Eats home delivery service, which had been rolled out earlier, found its niche and became highly successful. The lesson here is that Uber adapted to market conditions. It kept their drivers working. In early 2020 (pre-COVID) Uber had revenues of over $11 billion and a marketing capitalization of $74 billion, not to mention over 19,000 employees according to thestreet.com.
Trust the data
YouTube thought the video dating site model was a winner. When data metrics showed that it didn’t work the founders spent a year retooling it, turning the new MVP into a highly scalable platform that is now a $500 billion company. The YouTube Founders turnaround was so successful that Google bought the company for $1.65 billion. The secret? They were able to put egos aside and let go of their original strategy that envisioned a video dating site.
Data science including data mining, the interdisciplinary field that uses scientific methods, algorithms, and processes to extract information, has become easier in today’s digital world. It is important to have a data science team on board early in the process, working with business operation teams, helping those units make more educated, data-driven decisions. Crunching the numbers to extract information helps keep a project on course. “Regardless of industry, data science teams need to be strong in three core areas: mathematical, technology, and business acumen,” says John Bottega of the EDM Council.
While vision is the foundation, it is important to make plans for an IPO during the growth phase – even an exit strategy that involves selling the company down the road. It is important to make money, but equally important to consider that a more established brand can take that startup to the next level. Witness Facebook acquiring Instagram shortly after that platform showed up in the Apple Store. Then focus on the next great vision for a startup – if willing to leverage the previous success to create capital for the new project.
Move on to the next iteration if the initial or subsequent MVP doesn’t work. In the early stages of product development map out what scaling up would look like. That is the best time to make any changes and eliminate the “pinch points” necessary to scale and keep costs in check. In the end, successful technology startups come from widespread user acceptance and satisfaction. Review data captured from users on a regular basis to look for trends that need to be addressed. If there’s a market need evident, tap into it. Build loyalty and add new features as needed to retain customers and add new ones. That’s the recipe for scaling a business to maximum growth.
About the Author:
Raghu Krisnegowda is a senior engineering manager based in Southern California. He has served as a chief technology officer for several start-ups. Krisnegowda has almost two decades of experience in leading and developing high-scale products. For more information, please contact firstname.lastname@example.org