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The title explains it all. No intrigue. We are going to describe big data use cases, and we’ve summed up twenty for our industry-neutral overview. So, let’s talk business value.
1.Creating 360-degree customer view
Big data analytics enables companies to collect customer data from multiple channels and create a single view. Naturally, if a customer shops both online and in physical stores, uses a mobile app and contacts a call-center for support from time to time, they expect a company to recognize them in each of the channels and provide the same seamless experience.
2.Delivering personalized customer experience
Personalized customer experience is the logical follow-up of 360-degree customer view. When a company clearly understands their customers’ journeys across all channels, they can send targeted promotions and individual coupons, offer personalized services or website content depending on a customer’s profile. A widely used you-may-also-like-this feature that recommends products or services based on customers’ surfing and purchasing histories also belongs to this big data use case.
3.Segmenting customers based on behavior patterns
Thanks to big data analytics, companies can segment customers not just based on social and demographic factors such as age, family status or income level, but also based on customer behavior and preferences. This kind of segmentation is extremely insightful and much more valuable, as it contributes to optimizing company’s product or service portfolio, as well as to developing a marketing strategy so that they both comply with customer needs.
4.Delivering top-notch customer service in real time
The companies that have mastered processing customer, location, as well as product and promotion big data in real time, can deliver superb customer service. Imagine a visitor who enters a shopping mall and immediately receives a notification to their mobile with product offers they find tempting and with discount coupons. The visitor can easily find their way to the required store, as their app navigates them. Once in the store, the salespeople know the visitor’s preferences and their advice is always relevant. If something goes wrong after the purchase, the customer will have no trouble with returning it, as the company can easily check that the customer is not a troublemaker and the return rate for this particular product is above average.
5.Preventing customer churn
Companies can use predictive churn modeling to recognize the customers who are about to switch to competitors. The model identifies the behavior pattern of the customers whom the company has lost, as well as checks active customers’ activities against it. If the system finds a match to the pattern, it informs which customers are at risk, timely enough for a company to take remedial actions, for example by offering personalized coupons. Besides, companies can understand the root causes of churn, as well as customer’s value, which allows the company to prioritize their efforts.
6.Analyzing customer sentiments
Big data analytics includes text analytics that enables recognizing and analyzing the comments that customers share on the internet. The insights that a company can get may range from a general understanding of customer sentiments (whether they like or dislike a product, a service or a brand) to a detailed list of problem issues with the ability to drill-down to individual comments.
With big data analytics, companies can optimize the prices based on competitor’s monitoring, growing or declining demand, customer profiles, etc. The examples are numerous: ecommerce retailers that provide a customer with a discount to avoid them abandoning their cart; airline companies that vary ticket prices based on how many customers are interested in the flight or hotels near airports that change their prices if weather conditions are bad.
When forecasting demand, companies can go beyond just historical figures and growth assumptions. Thanks to big data analytics, they can make more accurate and meaningful predictions. For example, deciphering their customers’ journeys gives a clear idea about customer preferences, while analyzing external data such as weather forecasts, social media trends and upcoming important events allows adjusting to a changing environment.
9.Optimizing product/service portfolio
A company may look beyond sales figures to understand whether they offer a product/service portfolio that meets customer needs. By analyzing customer sentiments, requests for support, returned products or abandoned services, companies can get precious customer feedback. To get additional insights, a company can monitor the feedback that refers to their competitors.
10.Developing a new product/service that will be a success
A company can identify hundreds and thousands of key attributes and run a complex analysis to find out how product success correlates with them. Based on the analysis results, a company can understand what attributes are likely to influence the success. With these insights, a company can develop a new product that is destined to appear among best-performers. Also, with the help of online media analysis, companies can recognize trends growing in popularity and be among the first to satisfy customer untapped needs.
11.Creating efficient marketing mix
Big data analytics helps companies assess the response rate for each marketing channel. For this, a company needs to analyze the behavior of every customer and identify the patterns. For example, 20% of customers saw but ignored ad banner on the internet, though they opened their personalized e-mail with the same product offer and went to the company’s website. These and similar insights help a company to prioritize marketing channels and distribute the budget accordingly.
12.Catching the opportunities for business growth
If a company has expansion plans, big data analytics will bring valuable insights about the markets or business directions that will deliver the highest profits. Let’s say, a new service is consistent with the existing ones and a company knows their customers’ preferences, so they can forecast how demanded this service will be. This is exactly what Etihad Airways did when they were contemplating some new destinations for their flights. They finally selected those directions that met the needs of their target audience best and provided maximum revenue.
13.Improving warehouse processes
Thanks to sensors and wearables, companies can improve operational efficiency. For example, the pickers or forklift drivers at a warehouse can have a wearable that calculates an approximate time needed to pick the whole order, shows the optimal route to pick all the items for a certain order, as well as scans each picked item to double check it against the order and avoid human mistakes.
14.Optimizing delivery and distribution
By processing GPS and telemetry data coupled with real-time traffic and weather conditions, companies can identify the optimal routes for delivery. Additionally, demand forecasting, which we described above, allows companies to significantly reduce order-to-delivery time or even anticipate orders (the thing that Amazon is trying to do with their Anticipatory Shipping) and distribute the goods among regional warehouses even before customers really placed their orders.
15.Managing inventory efficiently
Big data-based demand forecasting and delivery optimization (we described both above) eventually enable a company to manage their inventory efficiently. For retailers, for example, this means no overstocks or out-of-stocks either in the stores or at the warehouse. For manufacturers, this gives an opportunity to timely get raw materials in the volume that is sufficient to fulfill their orders completely.
Big data analytics can help manufacturers increase the yield with no extra costs incurred. To make this possible, a manufacturer should identify hundreds of variables that may affect the yield. Porcelain tile producers, for example, can pick moisture content of the raw powder, the thickness after pressing, the temperature and time required for frying and many more. After that, the manufacturer should create multiple models of the production process to quantify how a particular variable influences the yield, gather sensor readings that represent the variables, as well as run a statistical analysis to identify interdependencies among them.
17.Fostering preventive maintenance
Companies can identify a possible malfunction before it develops into machinery breakdown and ruins normal operations. This is possible thanks to sensors installed to different machinery parts. At set time intervals, sensors send temperature, vibration, pressure and other readings to the centralized system. The latter analyzes them to identify a pattern that is likely to lead to a failure. If the newly ingested data match a failure pattern, the system sends an alert to a maintenance team.
18.Managing product quality
By analyzing multiple external and internal big data sources, a company can manage product quality. For instance, Coca-Cola resorts to environmental data, sweetness and acidity indicators, satellite images, etc. to ensure that their orange juice always tastes the same. Thanks to analysis results, their production process is always under control. If the acidity of different batches is different, they can mix them to get the required one.
19.Strengthening quality assurance
By gathering real-time sensor readings and analyzing them against models created on the historical big data, a company can recognize manufacturing flaws at early stages of production or situations that are likely to cause defects. However, sometimes problems can be found only after a product have been in use. In this case, the manufacturer can perform forensic tests on the returned product, and analyze the sensor data at the moment of production. This will provide extra insights into the factors that influence product quality.
Big data analytics is helpful when companies want to identify and fight fraud. To recognize suspicious activities, a company should understand every client’s behavior pattern, monitor their behavior in real time and check it against the patterns that may be or proved to be fraudulent. As the issue is very delicate and companies are not willing to cause customer dissatisfaction with delays in service rendering, payment rejections, etc., they also turn to geolocation or social media analysis or other intricate big data techniques to double check suspicious activities in practically no time.
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