Don’t Believe the Hype: How Startups can Avoid the Pitfalls of Misleading User Data
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Imploring data analytics is common among startups. Whether the company is in its conceptual stage or making the transition into becoming a leading name in an industry, collecting data provides helpful information on decision-making and boosts business intelligence.
According to a Deloitte survey, nearly 50 percent of surveyors reported that data helps them make better decisions. However, for early startups, some data can be misleading, thus giving the company the wrong impression of success and customer population.
Depending on data collecting methods or misinterpretation of a target audience, early company data can be tricky. Here are a few ways startups fall prey to faulty data:
Small Sample Size
During the data collection phase of data analysis, it is important to have an adequate sample size. For example, a survey of 100 customers might look different than a survey of 1000 customers. The power and validity of a study lie in its ability to incorporate the masses. The goal is to reveal a general trend within your startup. Don’t make the mistake of assuming growth based on a small sample size.
Misinterpreting Your Target Audience
Identifying and reaching your target audience may present some challenges. HuffPost spotlighted the Marketing Got Complicated study, which revealed that 72 percent of marketers reported difficulties identifying their target audience online.
When faced with the hardships of reaching your target market, be sure not to misinterpret it. For example, if an e-commerce company sells men’s ties, they could see an exponential boost in sales around Father’s Day. Raw and early data will lead the company to assume men are buying their ties, when in fact, they’ve misinterpreted a sub-sector of the target audience, spouse, or daughters/sons, for their main target audience.
Making up Trends
When examining collected data, many startups turn to charts and line graphs to help better see the projection of the success of the company. According to MarketWatch, revenue of the global data visualization market will reach $7.76 billion in value by 2023.
Data visualization involves grouping a set of numbers into categories that will make formatting a chart or bar graph easier. For example, a graph of sales for May might show a skewed upward trend when neglecting correlating factors. Is your product summertime friendly or was there a viral meme that pushed customers to your site? These outside factors can contribute to an upward slope that isn’t truly a trend, but rather a one-hit-wonder.
Friends and Family Polling
Sure, Auntie May supported you, and your friends got together to buy some stuff from your site, but these aren’t true users. To avoid misleading data, it’s probably best to exclude these sells from your analysis so you can gain a better idea of actual customers you want to retain. A Harvard Business School review shows that a 5 percent increase in customer retention can boost sells by 25 percent to 95 percent.
Overly Depending on Early Customer Feedback
According to your customer feedback, your customer service is excellent! However, you’re only three months post-launch, and your customer base is still small. Of course, you have time to cater to each customer and provide one-on-one customer support like returning a customer’s email in 2.5 seconds. However, when a business starts to boom, 2.5 seconds might turn into an automated email that says, “you’ll hear from us in 5-7 business days.”
Relying on Easy new Customers
What happens when you’ve advertised at all the spots you had in mind and gained all the new shoo-in customers? To avoid seeing a temporary and false spike in sales data, focus on converting customers. Many companies spend the majority of their efforts and budget on attracting new customers instead of conversion. An Adobe survey reported that 53 percent of digital marketers spent less than 5 percent of their budget on conversion optimization.