We’ve all heard the buzzword “Big Data“ and frankly you maybe even a bit tired of hearing it. Although the term is too generic and often improperly used, it is not just a hype. It’s a quiet revolution. The age of data-driven management has already arrived and those that don’t adapt will be stomped out by competition. Let’s look at some of the industries which have already been transformed by the use of Big Data analytics.
The retail sector is fundamentally a B2C model and as such it is highly competitive. In the past having the right pricing and having the right kind of advertising was a winning combination to attract customers and generate sales. However, with the development of internet and mobile channels for sales and marketing, the industry has become more complex. This raises questions such as which channel to use to target certain customers, should the in-store and online store pricing be the same, which items should we have in stock to ensure we don’t miss out on opportunities, and other similar business issues.
Creating a seamless user experience and managing multiple-channel customer interaction is essential. For example, a consumer might begin researching a product on a mobile app, purchase it online and pick it up at a store. Coordinating this multi-channel shopping interaction requires a business to effectively manage, integrate and understand this vast array of data coming at a non-stop pace. For example, you may figure out that certain video game is extremely popular but which of your customers order it online and which ones prefer to go to the store is a key question that can drive personalized marketing campaigns with a greater ROI. The following infographic from business and technology consulting firm Wipro explains further.
The use of Big Data in the retail industry has 2 major applications: increase the revenue by creating personalized marketing offers (see earlier Customer Analytics article for more details) or by optimizing the inventory management and thus increasing profit margin by reducing the operational costs (i.e. Just-in-Time inventory management ). Ask any retailer what is the most expensive part of their business model and they will tell you – a sitting item on the shelf. Besides the expenses of having a retail space and this item occupying the precious physical space in a store, there is a cost of shipping the item to the store and its depreciating value over time. Which leads us to the next industry…
Supply chain industry is all about optimization – who can deliver the goods fastest at the lowest price possible. To get the business model right there are numerous logistics factors such as distribution channels, the geospatial positioning of warehouses, accuracy of delivery orders, etc. Because it is a multi-faceted industry involving many players that need to collaborate, optimization through technology yields amazing results. According to the Accenture Global operations Megatrends Study, “embedding big data analytics in operations leads to a 4.25x improvement in order-to-cycle delivery times, and a 2.6x improvement in supply chain efficiency of 10% or greater.”
Figuring out the shortest route from the distribution center to the store and having a balanced stock in each distribution center drives huge savings in operating costs. The Boston Consulting Group analyzes how big data is being used in supply chain management in the article “Making Big Data Work: Supply Chain Management“. One of the examples provided is how the merger of two delivery networks was orchestrated and optimized using geoanalytics. The following graphic is from that article.
Banking & Insurance
In both banking and insurance sector the name of the game is Risk Management. A bank issues you a loan or a credit card and they make money on the interest rate. Besides obvious risk of you not paying of your debt there is another risk which is you paying off your debt prematurely and thus generating less revenue for the bank.
Predictive analytics has been in use since the 90’s to identify the interest rates thresholds which result in early payoff / reduced loan interest rate income for the banks. In the financial world a single transaction is the key building block of huge amounts of data that are then analyzed with predictive models and based on trending on massive scale allow for categorization of customer profiles that can predict risk associated with individual users. Banks can model their clients’ financial performance on multiple data sources and scenarios. Data science can also help strengthen risk management in areas such as cards fraud detection, financial crime compliance, credit scoring, stress-testing and cyber analytics.
In the insurance world it also boils down to customer profiles – if the premium is too high (the offer is not a good fit to customer profile) they may switch to another insurance company. To contrast this, if you have a risky car driver your offering is costing your insurance company more in claims than it does in the insurance rate or premiums. Figuring out which customers are more risk-prone than others allows for custom tailored offers that mitigate the risk of losing a good customer or losing money on a bad customer. A good example of how technology is disrupting this field is the Snapshot device which transmits data about when customers drive, how often they drive, and how hard they brake.
It is not expensive and it is available now
According to the Accenture study the main reason why business owners aren’t implementing their Big Data ideas is the perception that it is very expensive. They would have been right 10 years ago. Not anymore.
Microsoft’s Power BI platform allows small and medium sized business owners to harvest the power of Big Data analytics without any technical expertise. Also, because it’s a platform it comes with insightful industry-specific BI tools – there’s no need to reinvent the wheel, you can start using the same reports that big players use, for a fraction of the cost. Using real-time business data, Power BI delivers crisp, clear dashboards that assist managers to comprehend where their business stands today, how it performed historically, and what can be done for future success.
Besides savings, on implementation costs (which can be tens or hundreds of thousands of dollars) your maintenance costs are virtually zero dollars. The Microsoft team not just keeps the platform running smooth, but improves and updates features as the market evolves, so you know that you will always get the latest industry-adopted reporting standards on your laptop, mobile or any other device anywhere you are.
We’ve entered the age of advanced data analytics where long-term business success hinges on leveraging data to develop insights and deliver solutions to customers. Act now to not be left behind in the race!