By: Srikant Sastri, Founder, Crayon Data
The idea that enterprises must delight their customers is a given. Most often, since this idea is so entrenched in their processes it tends to hit a blind spot. But ask yourself this: How often does someone praise a company because of its extraordinary service? The chances are that you cannot think of too many.
Now ask yourself: How often do consumers cut companies loose because of poor service? All the time. They exact vengeance on mobile phone companies whose reps keep them waiting, on broad band service providers who deprive them of internet access for weeks, or on airlines that lose their bags. Poor service can make customers angry. And these new-age frustrated customers waste no time in talking about an enterprise’s poor customer service on social media channels and other online forums.
Why don’t companies anticipate and act before their customers reach the point of no return? Could it be that enterprises don’t really understand their customers’ needs and preferences? By mining and analyzing data, enterprises can gain better insights into their customers’ needs, preferences and likely behavior. This valuable information enables the development of a strategy. A strategy that enables clear, compelling communication, offers and deals that customers will ultimately value, leading to happy, satisfied and loyal customers
Here are some examples of how big data analytics can aid in reducing customer dissatisfaction.
How banking industry can improve customer experience using big data analytics: Millions of products and services are bought using a debit or credit card. It is now possible to find patterns in consumer behavior based on where the cards are used, transaction amounts and the purpose or nature of the products purchased. By monitoring this, banks can anticipate consumer behavior and offer relevant additional products at the right time to the right customer.
The banking industry is also known for its large complex IT systems. It is possible to move these IT systems to new big data platforms, which can add valuable data to the analysis. This data can deliver new insights, which in turn will lead to new revenue opportunities and even reduction in operation costs. Operational efficiencies can further be improved by analyzing transaction and unstructured data, such as that collected from voice recognition, social comments, and emails. This data can then be monitored and analyzed to anticipate future workloads and change staffing needs accordingly in call centers and branches. If all customer contact points are collected and displayed through one platform, service executives will be able to help customers quicker and better as well.
Big data makes it possible to monitor activities of clients that can alert churn. If a customer, say X suddenly reduces activities/transactions with a bank, it can be due to customer dissatisfaction and indicate that the bank may lose customer X soon. In addition, if the bank knows who the influencers are within their market segments, it can ensure that they do not lose those influencers due to churn. If they do, it is possible that followers of the influencers will also leave. If these patterns are identified, companies can take preventive actions to ensure customer loyalty.
Real-time analytics can improve customer experience
Social media analytics makes it possible to understand the behavior of customers in real time, by providing information on how they think about or use new products and services or react to advertisements. Social media analytics can be used to identify influencers and their opinions on products or services. An analysis of how products are used can give insights into how they need to improve. For example, a company can analyze how their mobile application is used based on location, time of day, where people click, how they search for items within the app and how long they use the app. This can indicate areas that need improvement. Instead of asking customers for feedback, using long and expensive surveys, the feedback here is instantaneous, and can be used to optimize the product quickly.
Let’s say that you run an online retail store. Based on the shopping cart data, you may be able to say “Customers who bought the Xiaomi mi4 also bought the Xiaomi band.” If the associations capture the consumer preferences as expected, it can increase revenues from cross selling. It can also enhance the consumer experience, and thus leverage data sets to create additional customer loyalty.
In data science this is called as co-occurrence grouping, defined as: “If A occurs then B is likely to occur as well.” In this case, A is Xiaomi mi4 and B is Xiaomi band. This is how companies like Amazon, Flipkart, Snap Deal show recommendations based on the customers purchase history and online behavior.
Another good example of co-occurrence grouping is the famous beer and diaper case study:
Hermiz and Manganaris (1999) stated “One of the most repeated data mining stories is the discovery that beer and diapers frequently appear together in a shopping basket. The explanation is that when fathers are sent out on an errand to buy diapers, they often purchase a six pack of their favorite beer as reward”.
Enterprises can use data mining techniques like co-occurrence grouping to understand the purchase behavior of their customers and sell relevant products.
There is no denying that customer dissatisfaction can slow the growth of an enterprise over time. It’s a problem that every enterprise struggles with on regular basis. However, with big data analytics in place, it is possible to minimize customer dissatisfaction to a great degree.
On an average, loyal customers are worth up to 10 times as much as their first purchase. [Source: White House office of consumer affairs]
Transforming the information you have about your customers into actionable knowledge, can grow your business, and create a loyal customer base. Big data solutions can ensure that with every customer interaction, an enterprise serves up relevant and personalized choices.