Customer segmentation is not a new concept for businesses—they have been doing it for decades.
However, big data has turned this idea, which was all about gathering customer information, into one that begs instant action on the available data and near-perfect understanding of customer needs.
Every executive agrees that understanding customers is what makes businesses successful. However, it is unfortunate that still many businesses use segmentation data only as a starting point for their strategies and forget about it after that. However, big data is getting ‘bigger’ with time as more and more companies around the world realize its importance.
Customers aren’t What Their Past Behaviors Make Them Look Like
The problem with data collection and customer segmentation in the past was that it always looked at customer behavior and interests in retrospective. What interested the same customer now or how a customer’s preferences had changed over time was not taken into account. Big data certification addresses this missing connection today. For example, it won’t let you treat a young executive as a young executive even ten years later, because in ten years’ time this same person will have become an experienced executive with different preferences and priorities.
Data Collection beyond Conventions
Big data is growing at an unprecedented rate today, especially unstructured data which accounts for the biggest chunk of big data.
Gauging a customer’s intention out of it is the new challenge today. Businesses are collecting customer data from multiple sources today, and the volume and depth of this data bring businesses intimately closer to their customers. In the world of big data, you cannot afford to wait for the customer to call your sales or customer service department to find out this customer’s intentions. If that’s the case, your strategies and technological implementations are outdated.
A customer is not a voice on the phone or a person who purchased a certain product from you yesterday. Social CRM, social networks, mobile applications, etc. provide much more information about a customer today than any traditional means of the past.
Other third-party platforms such as forums, blogs and even your competitors’ websites are also customer touchpoints for you to collect data about customers. The good news here is that this data should be sufficient to gain valuable insights. The bad news, most businesses still don’t have the system and technological equipment to make sense of that data and take productive actions in time.
Big Data for More Focused Marketing and Better ROI
In the past, when businesses segmented their customers, they didn’t distinguish between profitable and non-profitable customers. Big data focuses on providing enough information so that businesses can focus their marketing energies and efforts on customers who are interested in purchasing. Companies spend thousands and millions of dollars on advertising to masses without knowing how many people have no interest in their products/services.
However, with big data, the segmentation of customers is much more logical, sensible and profitable. Data visualization has also come a long way, so you can now get quick and actionable insights by creating attractive visuals. You can even use mapping software to turn big data into maps.
Based on customers’ shared qualities, businesses can now decide which marketing strategy will best suit their customers, e.g., email marketing, social media marketing, etc.
Descriptive vs Predictive vs Prescriptive Analytics
According to Markets and Markets forecast report, by 2020 the predictive big data analytics market will be worth $9.2 billion. Realizing how important big data is, businesses are fast shifting from descriptive analytics of the past to predictive and prescriptive analytics of the present.
Descriptive analytics focuses on customers’ retrospective behavioral data. This was really helpful in describing the customer type. However, the need of today is not to describe customers but to predict what actions and decisions they are going to take in the future. Based on their real-time data, businesses have to make future-oriented decisions and predict what customers want and expect.
For example, data collected from Facebook interactions and a customer’s mood should help a business recommend a suitable movie to that customer. This same person, let’s call him John, might have always searched and downloaded comedy movies in the past. However, his real-time data might show that he feels down and is looking for something uplifting and inspirational. Recommending such a movie based on real-time data analytics and making prescriptive decisions can help customers become loyal and increase conversion rates.
Here are 3 main building blocks of predictive analytics:
- Predictive modeling
- Optimization of decisions through analytical data
- Customer profiling based on transactional data
Making Big Data Work: Case Studies
Large enterprises with great resources have started to capitalize on big data through the use of software and hardware technologies that help them better profile their customers and create more logical segments. Some examples include:
Amazon is known for suggesting the most relevant products to customers based on their searches, but the company has gone beyond this. It uses its Kindle product to know what words customers are highlighting in the books and sharing with each other to make further recommendations. In a similar way, the company uses anticipatory shipping wherein it already ships the products you might be interested in the future to your nearest distribution centers and warehouses. Here is where you can get more detailed information on how big data coming from IoT is transforming the retail industry.
· Time Warner
The company customizes its advertisements based on its predictions of what customers will want. The data is collected through set-top boxes and used in combination with other data of the customer from various platforms, including the social networks.
Walmart is collecting near real-time data from its POS systems across all of its branches to make predictions about its inventories. Walmart collects nearly 2.5 petabytes of data every hour just from its customer transactions. This helps Walmart to fill its inventories through prediction by avoiding out-of-stocks, which of course, can be costly. Such predictive analytics have also helped Walmart with its advertising, which is now more precisely targeted to customers who are likely to purchase the product shown in advertisements.
Customer segmentation has been changed completely by big data, and the competitive edge now lies in how accurately and precisely companies can predict customer intentions. To achieve the highest levels of accuracy with your big data analytics, you can take advantage of big data consulting services from Itransition.