It was December 2007, and Noah Kagan was facing a challenge. Recently fired from the marketing team at Facebook, where he had been the company’s thirtieth employee, he was keen to show his new boss that he could be an asset. But Aaron Pitzer wasn’t going to make it easy for him. The co-founder of new finance site Mint.com told Kagan that he had high expectations. Kagan would become the Marketing Director of the four-person start-up, and within six months, Kagan would bring the site 100,000 users.
Writing on his blog, Kagan recalls the moment: “I thought to myself as the nerves began to kick in. ‘How am I going to achieve that?’”
You can see the marketing plan that Kagan drew up for Mint here. It’s more modest than Pitzer had wanted. Kagan targets 80,000 users within six months of a public launch. He wants a write-up in a major publication, such as the Wall Street Journal or Wired. There’s also a long list of blogs and websites and forums which he believes the company should target in order to find users.
The technique that Kagan used though, he called “quant-based marketing.” He set a goal: 100,000 users. He built a timeframe: six months. Then he made a quant-based marketing spreadsheet that allowed him to “plan, track and measure” all of his marketing activity.
By following the figures, he would be able to see where he should put the company’s marketing resources. He would know which sites were delivering the best results and which were falling behind. He would understand what numbers he needed to improve and he could predict when he would get them. The data that he would receive from traffic flows and conversions would tell him whether he was on track and how he should adjust. Mint might have been offering a service that appealed to customers but Kagan’s marketing efforts were entirely number-based. Six months after launch, the company had a million users. Within two years of launch, Intuit paid $170 million to buy the company.
Data Mining is Also for Small Companies
The company that fired Kagan went on to become one of the biggest firms in the world, powered entirely by data. Facebook knows the lives of more than two billion people. It knows who their friends are, what they like to read and watch, their political opinions, and where they live. Facebook doesn’t produce content and it barely makes any changes to its platform. It’s a company that depends entirely on collecting and selling banks of information. That data gave it revenues of more than $70 billion last year.
Its biggest rival is Google, which tracks the location, Internet searches, contact lists, email, and photos of more than 2.5 billion people. Like Facebook, the company sells that data to advertisers who are looking for people who have expressed a particular interest, live in certain locations—or any other combination of different demographic data. Last year, that data brought Google more than $162 billion.
So data can be valuable and lots of data can be very valuable indeed. While tech firms like Apple have to design products and create supply chains, Google and Facebook need to do no more than collect information on servers then deliver it to advertisers. Companies can become multi-billion dollar firms solely on the basis of the information they collect about the people who use their services. Businesses will pay large sums to access that information.
Other big companies might not sell data but it’s still an essential part of their business plan. Apple doesn’t monetize its ability to track the movement of iPhones (and has designed its privacy policy to stand in opposition to Google’s data usage policy) but it certainly keeps track of the sales of iPhones in each region, of the numbers of chips rolling off its conveyor belts, and which apps are selling in its App Store. Apple doesn’t sell data but it does use the vast amount of data it collects to sell its own products more efficiently.
The ability of large firms to collect Big Data puts small companies at a disadvantage. Every start-up begins with no data at all. It has no customer lists. Its website begins with no clicks and no traffic sources. It has no sales to track and no upsell options to compare. The company’s first moves take place entirely in the dark.
The data does start flowing soon after launch though. Noah Kagan would have been able to look at the statistics delivered by Google Analytics to see fairly quickly which websites on his marketing plan were delivering the most traffic, the most clickthroughs, and the most conversions. Google might amass and sell its own data but its analytical tool is free for website owners to use, and is usually the first stop for new firms as they test out their marketing strategies.
As the company grows, it’s likely to start using more specialized software to keep track of leads and conversions. Tools like Salesforce allow sales staff to see who’s expressed an interest and how far a potential customer has progressed through the funnel. As the company continues to grow and make efforts, it starts to build its own numbers. It’s able to use that data to refine its efforts, perfecting its marketing message, choosing the right targets and improving its conversion rates.
Taking Data from Outside the Company
That process of collecting website and sales data will happen almost automatically. Some companies go further. It’s Fresh!, a British food technology company, wanted to reduce packaging waste in the production business. The company developed a new kind of filter that absorbs ethylene, a natural gas that hastens the decay of fruit and vegetables. To prove that its filter works, the company conducted multiple trials across the supply chain, measuring the rate of decay of the fruit and vegetables its product was protecting, and the conditions in which that decay happened. That data collection gave it the numbers to prove its business case. The trials would have been expensive. Data is valuable to sell but it also costs money to collect. But the figures and charts would have revealed exactly how the product was performing and proved that it works. It’s Fresh! found that its filter increased shelf life by two days, a different meaningful enough to supermarkets and delivery firms to win orders.
That sort of targeted data collection will be valuable but it’s limited. It still only reveals what’s happening inside your own company. What Google Analytics, Salesforce, and other data software record are accounts of the company’s interaction with the market. They show how what happens when the company meets potential buyers. But there’s also plenty of valuable data outside the company.
DataMiner, for example, is one of a number of tools that scrape data publicly available on websites and convert it into a Excel file that’s easy for a small firm to use. The tool doesn’t create data nor does it mine data that can’t be seen on a Web page. It simply collects and organizes information that other companies have shared online. So a seller of smartphone covers could scrape a list of the products available from a rival store, together with their prices, but they couldn’t see which items are selling the most or where the seller obtains its traffic if the website doesn’t publish that information.
“Sales people use Data Miner to get contact info from listings on networking sites or social media profiles,” explains Ben Dehghan, Data Miner’s co-founder. “Retailers might get product info and prices from marketplaces and other retailers. Real estate agents get house prices and addresses from Zillow or Redfin.”
Companies can create what Data Miner calls “recipes”—scripts that customize the way the tool extracts data. Companies have used them to pull email addresses and IDs from social media profiles; to obtain price and product information from retail sites; to list contact information displayed on websites; and even to track sentiments, likes, and connections in social media posts.
The software, the company says, is used by recruiters looking for leads on LinkedIn, Amazon sellers who want to understand the competition, and small business owners who want to track competition on Yelp or Ebay.
It sounds underhand. Ebay sellers and social media users aren’t making information available for other people, especially competitors, to use. But as long as the data is publicly available—and Data Miner and other Web scraping tools only record publicly available information—small business owners can see the scraping as research. They could write their own notes but scraping is easier, faster, and automatic. It won’t give a small ecommerce site the same kind of market information that Amazon can access—or anything like it—but it does increase the amount of data that a company can use beyond their own interactions with leads and customers.
“That is a huge game changer,” says Dehghan. “While large companies collect data from user accounts or large relationships with data providers, the smaller businesses can’t afford those measures. But there is public data available in many sources and if collecting clean, usable data is easy… the small guys can compete.”
Telling Data’s Story
So a small business will be able to collect its own data, and that data will grow as long as the company stays in business. The more data it collects, the more knowledgeable it will become and the better the owner’s decisions should perform. The company can also scrape data off the Internet, enlarging its knowledge of the market and gaining greater insight into the business environment. That data will be limited. Companies don’t usually share their most valuable information. Amazon shows which books are currently rising up the charts but it doesn’t tell people how many copies those books have sold. It shows the current price of products but not how those prices have changed over the previous months, nor how those price changes have affected demand.
But even when you can gather the data, they’re just columns of numbers. The data still needs to be interpreted and understood. It has to be visualized.
The simplest method is to extract the data from Excel and turn them into graphs and charts. Google Analytics now does a good job of turning figures into representations. You can follow users from page to page to see where they’re most likely to click away and where they show the most interest. Other companies provide a similar service with other kinds of data. What Excel lacks in detailed data visualization tools, Microsoft’s Power BI makes up for in fancy flowchart. It’s a much more powerful analysis tool that, of course, works well with the rest of Microsoft’s productivity suite. Although Microsoft targets the software to enterprises that have reams of their own data to analyze and visualize, it does also have both a free trial and a $10 per month option that’s ideal for small businesses.
Within a relatively short space of time, a small business can find that it has lots of its own data and plenty of market research all laid out in attractive pie charts and graphs. It might not be as knowledgeable as Google or Facebook but it does have enough information to make better decisions than it was making when it started its journey.
That creates a potential problem. Data is just numbers but a product or a service exists to serve customers. The goal of the company isn’t to keep a chart rising; it’s to make customers happy. Noah Kagan might have described the effectiveness of the marketing plan he used to generate a million users in six months but he also attributed the company’s success to its creation of a great product.
No matter how much data you collect or how well you visualize it, you’re still going to need a great product. It’s just much easier to get that product into people’s hands when you have data. As Ben Dehghan says: “Once you get a taste of good data, it’s hard to go back to the ‘dark ages.’”
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