Big Data for Small Businesses? Not so Fast

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Big data. Machine learning. These are all buzzwords du jour — and for good reason, since many of today’s biggest technological innovations revolve around these topics. Tech giants like Google, Facebook, and Amazon use these technologies to improve their ad targeting, search results, and recommendation engines. For these companies, innovations in machine learning can result in small percentages of improvement compared to previous models. This may not sound like a lot, but when you’re operating at such large scale, a small increase in revenue is a massive amount of money.

With all of the buzz surrounding big data, many small business owners naturally wonder if big data techniques could help optimize their business. The truth is, most small businesses would sooner benefit by leveraging “small data” techniques, such as customer segmentation, in order to identify areas of improvement for their business.

In this article, we’ll explain how small businesses can benefit from “small data” techniques, which can often yield the largest insights.

Customer segmentation: What small businesses really want.
Many small businesses are already generating valuable data about their business, potentially without even realizing it. Software for small businesses typically tracks important information about your business, but many small business owners neglect to look at this data, and analyze it. For example, in dentistry, practice management software, which is widely used in dental practices, is a type of software that keeps track of all patient data (previous appointments, prescribed treatments, overdue payments, etc.) While this information isn’t considered big data, it’s certainly valuable data that can help provide important insights. The good news is that most small businesses use software that collects helpful data (and if you’re not using such software, you may want to strongly consider it as a worthwhile investment).

The first step in leveraging this data is to start looking at it. Simply by looking at the data, you’ll develop an intuition about your business and your customer base, and you’ll be able to make data-driven decisions to improve your bottom line.

The simplest, yet most valuable type of data analysis that you can perform is customer segmentation analysis. This consists of breaking down your customer base into different categories (segments). These categories can be as broad or specific as you’d like. Examples of such segments are “males,” “males 25-30 years old,” “males 25-30 years old who were brought in by marketing channel X,” etc.

Once you’ve created these segments, you can perform different types of analysis to understand your business’s performance. For example, you may be trying to attract new customers by offering a first-time discount for new customers. However, without doing customer segmentation analysis to understand the ROI of such a marketing campaign, it’s difficult to know if such efforts are profitable. You may be attracting many new customers, but they may have a low Customer Lifetime Value (CLV), if they don’t return. By analyzing this specific customer segment, you can understand how this marketing campaign is performing. Without conducting this analysis, you’ll only have your prior beliefs to depend on, and you’ll largely be guessing if this marketing campaign is bringing in profitable customers for your business.

Big data & machine learning: Why is it even necessary?
Machine learning and big data go hand in hand. The advantage that machine learning offers over customer segmentation analysis is that machine learning models can help identify more complex patterns in your data, in a more scalable fashion. The downside is that doing this well requires a substantial amount of expertise and time, which falls outside of the scope for most small businesses.

The good news is that by measuring your performance in the first place, and by doing simple customer segmentation analysis, you’ll be able to reap most of the rewards that you can get from performing more complicated machine learning. It may not be purely optimal like an advanced machine learning model, but you can get “close enough.” For larger companies with a massive customer base, it’s worth the extra effort to apply machine learning techniques to arrive at mathematically optimal solutions, since a 1% increase in additional revenue can be a substantial dollar amount. But for most small businesses, the marginal gain just isn’t worth the additional time and investment.

Before you get ahead of yourself, first make sure that you’re collecting and analyzing your data. This alone will help you identify inefficiencies in your business, and help you discover areas of improvement. By the time you’re ready to really benefit from exploring big data and machine learning techniques in your business, your business will already have grown substantially.

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