Google, Microsoft, and Facebook all lean on big data to build their market share and keep them ahead of the competition. They have learned from companies that allowed themselves to be overwhelmed by adaptable businesses that were better able to fulfill customer expectations. One of the biggest users of data analytics and crowd sourcing is Amazon.com, who has crushed the competition in the online retail sector. So what do a search engine, a software company, a social network and a retail juggernaut have in common? They thrive off of data. This, more than anything, shows that big data can be used to achieve success in many different sectors. A recent article from business intelligence research company, Software Advice, suggests there are industries that need to use big data and data analytics. Here’s a look at some of those industries.
Although it may seem strange jumping from the success of Fortune 500 companies to the realm of nonprofits, but the techniques that make a for-profit business successful are not necessarily mutually exclusive. One big nonprofit to consider is Wikipedia.org. It is one of the top websites in the world, and so indispensable that the internet would be a much different place if it ever closed up. “Wikipedia is totally nonprofit and depends on donors to operate a tremendous amount of bandwidth.” says Scott Snyder, a Wikipedia editor who has been working on the site for 5 years “Guys like me work as editors for free because we love it, but there is still a whole lot of expense they incur for the high level of use.” It also has more data at its disposal than any other nonprofit company. The potential is amazing, but things that it could do to cover its costs include: Boosting fundraising efforts, adjusting marketing to suit their demographic, and isolate users who are most likely to renew donations. Richard Becker, president of Target Analytics, seems to agree, suggesting organizations are using advanced analytics to boost fundraising efforts. When nonprofits engage Big Data they “can better understand how to acquire, renew, convert and upgrade prospects.”
The big data techniques that worked for a nonprofit to acquire donations are, if anything, more impressive when applied to sales. In fact, if you are in a sales-supported industry and you are not using big data you are doing yourself a disservice. The challenge of sales is not as much who to target as much as how to present the sales experience. Unlike nonprofits, your sales department has a good or service that can only be acquired through the financial transaction, and most buyers are already motivated. It becomes a process of facilitating the transaction, and this is where the data comes in. Brian Wilson, an automobile salesman from Redmond, WA says that analytics play a huge part in how he pursues his leads “I was able to use data on how long it has been since a customer bought a vehicle and how often they have needed to get it fixed to calculate a percentage of them that were likely to be unhappy with their vehicle. By focusing direct marketing on this segment I actually saw a huge jump in sales from those who were in the market for a new car without even realizing it. This data gave me jump on the competition.”
Big Data can also help sales teams determine what to concentrate on. Joe Brooks COO of Zapoint, a company that uses data to help companies engage, retain and develop the workforce talks about a study that asked employees to rank the importance of sales skills. After comparing employee data with the real data, Zapoint found that focus on “value creation” was the most important aspect to the sales process and not “sales presentation.” “In this case, the analysis showed that investing time and money on emphasizing value creation would likely have the biggest impact on sales revenue,” explains Brooks.
The insurance industry is one of the most data driven industries on the planet, so it is amazing how few of them use crowd sourcing solutions to enhance their business. Joe Martinez, an Insurance Adjuster from Austin, TX outlines the problem. “Most insurance companies are content to stand on the shoulders of experts. Adjusters like me, accountants, and actuaries. They miss out on a big opportunity every year. Big data would allow the insurance companies to determine variables that they have not been looking at, like the link between mobile phone usage and accidents. “There are data points other than demographics that can more accurately assess risk than demographic data points like age or sex.”
One of the hardest hit sectors of the economy is also one of the ones to utilize big data the least. There are many ways that a manufacturer can use big data to get an edge on their competition, but it remains a mystery why so few in the business leverage data. Rick, a manager of a snowboard manufacturer in Colorado sees this as his companies best weapon. “A lot of businesses that compete with us are set in their ways. They have a way of doing business that has helped them stay on top for decades. Meanwhile, we aren’t just putting out the same product we have for years. We are scouring social media to take the initiative in finding our customer base and finding out what they want. A 20 year old who is getting on the board for the first time is looking for an entirely different image than a vet who has been boarding for 20 years.” By using the data provided by social media, the company has started a line based on popular internet memes that resonate with a younger demographic. “While the competition is paying huge amounts of money for celebrity endorsements, we are getting our hands on brands that are just as popular with younger boarders at a fraction of the cost.”
The Agricultural Industry may be one of our oldest but today it has become a complex interconnected network of farmers and food processors. Charles Linville, Ph.D., is president and founder of Ploughman Analytics, a company that helps farmers and processors with analytics surrounding the value chain. He suggests key analytics for this industry include:
- Where crops are processed;
- How much of each crop is processed;
- Where processing or transportation facilities are located;
- The price of each crop offered at each of these facilities; and
- The cost of transporting the crop.
The other key is to analyze this data not as separate sets of data but one. When you combine multiple sets of data like this with a common purpose you can maximize where a facility is built. One model showed that changing an offered price by $0.05 resulted in a 50 percent shift in grain volume. Results like this are significant and show how big data and analytics can be used in the planning process of new facilities in any industry where here is a supply value chain.
Software and algorithms help refine interpreting this data. Each industry has different need but a similar starting point; loads of data…however independent sources of data alone do not tell the whole picture. The more companies tie this data together and refine the algorithms they use to interpret it the better off they are to stay ahead in their respective industries.
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Authored by Joe Artese, Business Analyst