“Big data” is a simple name for a complex topic. While data has been around long before the digital age, computers have changed the way we produce and consume data.
Before the digitization of data, most datasets consisted of fairly simple lists and spreadsheets that were compiled into charts and graphs. For market research purposes, companies might have data that included information such as the age, preferences, and geography of a small set of target consumers.
Today, computers have made it possible to gather data in much larger amounts and broader scopes. The data we’re collecting is complex, layered, expansive, varied, and constantly changing. It can't be contained by graphs or spreadsheets or written down in a list. It is big data.
Smart companies are leveraging big data in a variety of ways to help them predict their customers’ needs, and one technique for accessing the power of big data is with data-driven product development. In this article, we’ll explore ways companies are using big data to develop products, and how you can get in on the action.
Although there are lots of businesses and organizations using big data, one of the key players taking advantage of big data is Consumer Packaged Goods (CPGs). CPGs are exactly as they sound: packaged goods sold to consumers. M&Ms, sliced bread, sneakers, toothpaste- anything that you can find at your local supermarket is more than likely a CPG. When used in conversation, "CPG" can also refer to the businesses behind these products. PepsiCo, for example, manufactures a hefty segment of the CPG industry and could be referred to as a CPG.
A final defining characteristic of CPGs is that they are generally used up pretty quickly. They're the sorts of things that consumers buy periodically when they run out of them. In contrast, products that customers buy just once are called DGs, or Durable Goods. This includes things like furniture, vehicles, and the like.
The primary use for big data by CPGs is in product development. Customer behaviors, preferences, and trends are used to decide which products to manufacture and which to cut, how to package these products and how to market them, where to sell them and who to sell them to.
Using big data for decision making isn't a perfect science, but it can greatly reduce the amount of trial and error that typically takes place in product development. It allows businesses to make more accurate decisions the first time around. And when they do make mistakes, big data makes it easy to see how to best correct the error.
An important component of big data is that it's gathered and analyzed in real-time. This is incredibly useful for CPGs, as they don't have to sit on ever-growing datasets. They can look at the most current slice of data available and use it to their advantage.
Below are three companies that you are probably familiar with who make excellent use of big data.
A Seattle native, it's no wonder that Starbucks is equal parts tech-savvy and coffee-obsessed. The company has been leveraging big data for a few years now in very successful ways. Starbucks started collecting big data with the launch of its rewards app in 2015.
The 13 million active users on the app provide Starbucks with information like what times of day people order coffee, what coffee is most ordered during cold or warm weather, and which holiday promotions bring in the most traffic. This data enables Starbucks to decisively craft a personalized experience for all of its customers.
Like most companies at the time, Macy's was still relying on spreadsheets to keep track of its customers' data in 2010. Realizing how much data they were losing, however, the retail giant decided to implement big data into its workflow in 2012.
Since then, Macy's has been gathering and using big data, such as how frequently shoppers visit, what areas of the store attract the most attention, what paths people take as they walk through the store, and more. This data is then used to create more effective sales campaigns and make decisions based on predictable consumer behavior.
For instance, Macy's knows that the longer a customer thinks about a purchase, the less likely they are to make it. So, to improve the odds of someone deciding to purchase, Macy's sends personalized coupons to customers based on which areas of the store they frequent the most.
Of course, no one is better at leveraging data for retail than Amazon. They likely have more data on their customers than any other retailer today, and they use that data more effectively than any other company in history.
The clearest way that Amazon uses big data is in its recommendations to customers. When browsing through Amazon, you'll probably notice that you find exactly what you're looking for within the first few clicks. And if you don't, you'll probably find the product you really want as a suggestion on another product's page. Amazon is incredibly efficient at putting exactly what the customer wants right in front of them, which is one of the reasons they're on top.
Data-driven product development is a product management strategy that uses big data to its fullest extent. The major difference between data-driven product development and traditional development is that data-driven strategies are iterative, meaning that a product release is just an early step in a product's development.
A helpful metaphor for understanding product development is to look at the way apps are released. They start with Version 1 and deliver it to the market. Consumers then test, use, break, complain, and love on the product while the team behind it steadily makes changes based on this feedback (a.k.a. data). They then launch Version 2, which has less of what people hated and more of what they loved.
Data-driven product development takes this approach and applies it to packaged goods. This approach is sometimes criticized as being too reactive, but with big data, you can flip this criticism on its head. See, as we've mentioned a few times now, big data happens in real-time. It provides you with clear insights into the consumer journey at every stage, as it's happening, allowing you to make better predictions and (ultimately) better products.
Business people like Steve Jobs are hailed for their ability to make visionary products a massive success. They can seemingly pluck the next big thing out of thin air. And while lots of businesses and product teams try to replicate this, the hard truth of life is that this is an impossibly difficult feat to accomplish once, let alone consistently.
At least, it used to be an extremely difficult feat. Although it still isn't easy to do today, it can be done with much greater accuracy than it used to be, and that's all thanks to the advent of big data. Big data gives businesses access to trending internet searches, social media trends, customer behaviors, competitor patterns— the list goes on.
Businesses that implement data-driven product development can use this data to determine what products are about to become a big hit. Companies can also make better decisions about which products have the potential to become a hit and which will likely never take off.
A natural part of the customer journey is disinterest. The majority of consumers will gradually lose interest in a product or service over time, and potentially even an entire brand. These customers usually fall into a business's churn rate as accepted losses.
With big data, businesses don't have to lose these customers, at least not as quickly. Big data can show you exactly what factors disinterested customers have in common, which allows you to build up a map of a customer's journey from engagement to disenchantment.
Businesses can use this data to not only predict when a customer is close to disengaging with their brand but resolve the issues leading to disinterest as well. This lengthens the customer lifecycle and increases the value of every customer that a business interacts with.
Lastly, big data is helpful for realizing when a product is underperforming. Most retail managers can tell that a product isn't selling when they look at their figures or even just take a walk through their warehouse.
Oftentimes, however, the effects of an underperforming product aren't so clear. The result is that you end up realizing too late that a product or service isn't gaining the traction it used to, and now there's no way to re-engage customers with it.
Big data enables you to be proactive about products that aren't reaching their full potential. You can see what people do and don't like about the product, determine what you can do about it, and in a worst-case scenario, cut the product now to minimize your losses.
Throughout this article, we've talked about how large corporations like Amazon and PepsiCo can use big data to their advantage. If you're a smaller business, however, you may be feeling like a lot of this doesn't apply to you. How are you, the little guy, supposed to gather data on millions of customers, or compile this data in a meaningful way?
The truth is that most small businesses lack the resources to take action on big data by themselves. But that doesn't mean you're completely out of the big data game. There are SaaS products available that give you access to big data without having to be a big business. We happen to make such a product, and it's called Peek.
Peek gives small businesses access to big data pulled from Google and Amazon searches and structures it in a way that's easy to understand. You can view search results from both websites, see how popular different searches are, and see how these search results collate with products on Amazon.
Though this is especially helpful for Amazon sellers, small eCommerce businesses of all kinds can use Peek to their advantage. It's a great way to see which markets are doing the best on Amazon, which are oversaturated, and which are about to take off. Below are a few ways that your small business can use big data through Peek.
One of the most valuable ways that you can use Peek is to find market gaps. Market gaps are areas where a product should exist, but doesn't. Locating a market gap is a way for you to launch successful products with greater accuracy, making it much easier to release a hit product.
To find a gap with Peek, look for searches that are high in popularity on Amazon and Google, but that don't have many products or product reviews available. This tells you that people want this product to exist. All you need to do is bring it to life.
Another way you can use Peek to your advantage is by searching through customer reviews on Amazon. Without Peek, small businesses can only search through Amazon reviews by clicking on a product on Amazon and scrolling through the ratings at the bottom of the page.
Peek streamlines this process by combining Amazon reviews with Amazon search results. You can sort through the reviews across multiple products at once, seeing what people do and don't like. You can then use this information to avoid making the same mistakes as other Amazon sellers, improving your product and your standing on the platform.
Big data has quickly become a cornerstone of modern business, especially when it comes to eCommerce. It's essential for understanding consumer behavior and is the foundation of data-driven product development. If you're interested in implementing big data into your small business strategy, consider adding Peek to your workflow.