Customers are a major component of any business. No one can claim to be running a successful business without the need for a strong customer base. But there is always competition in business. If you are lax in the delivery of services or begin supplying your customers with bad quality products, you will lose them to the competition. Client loss, however negligible, may have a negative general impact on the future success of the business. You need to understand them to retain your existing customers. Thus, will be able to deliver precisely what they want.
Therefore, businesses should strive to retain their customers at all costs. That’s not always simple, but the arrival of big data and business intelligence have made it easier for businesses to understand their customers and learn new methods to keep them coming back.
To understand big data, you first need to understand what data is. It is the quantities, characters, or symbols on which a computer performs operations that can be collected and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.
Big Data is also data, but it has an enormous size. Big Data is a word used to define a collection of data that is huge in size yet grows exponentially over time. In simple terms, such data is so large and complex that it cannot be stored or processed effectively by any of the traditional data management tools.
BI (Business Intelligence) is a collection of procedures, architectures, and technologies that turn raw data into meaningful information that drives profitable business actions. It is a suite of software and services that convert data into actionable intelligence and knowledge. BI has a direct effect on the strategy, tactical and operational company decisions of the organization. BI promotes fact-based decision-making using historical data rather than assumptions.
BI tools undertake data analysis and create reports, summaries, dashboards, maps, graphs, and charts which provide users with detailed intelligence about the nature of the business.
Statisticians and data scientists have relied on predictive analytics to help them make daily business decisions. However, Big Data has changed this whole scenario. More and more data continue to stream online and is incorporated into current Business Intelligence (BI), CRM, ERP, and other major business systems. This sets the customer’s single view into focus. Although most customer service and field sales officials have not felt the impact, some businesses are striving to see how that can come to play soon.
Big data has created a tool mostly accessible to line-level executives for cohort and regression analysis. This has helped executives use non-transactional data to make strategic and long-term company decisions. Big data, however, isn’t about to overshadow traditional BI tools. It will actually make BI a more valuable and helpful tool for many businesses. You will always have to look into the past to be able to predict the future, but with big data, you will have to do more than that. Business intelligence will never go away, but it will be strengthened by big data.
How can you understand if the results you are obtaining in the initial stages of exploration will actually bear out in the future? For instance, many women love high-heel shoes than conventional heel shoes. So, preliminary data analysis may suggest that more high-heel shoes sell better than conventional heel shoes, and that implies that high-heel shoes sell better.
This is, therefore, a correlation, not a cause. If you closely study historical transaction data collected from your business intelligence tools, you may understand that, for example, because retailers put high-heel shoes at eye level your recent marketing campaigns are paying off. Social media is always going to have its importance. It lets distributors understand how customers respond to specific products and which stores sell these products the most. Through this, retailers can stock the stores as per the customer’s response. BI and big data help you look at these trends and correlate your brand with geographic location and customer responses. Without this, you can miss a small window of opportunity to sell your products.
Initially, many decisions were based on historical data, and the trend may have already passed by the time it happened. But Big Data Analytics enables a thundering speed. This is achieved by integrating open source technologies, that is, the sources of big data platforms. Today, the cloud can readily capture and store big quantities of non-transactional data that was once not appreciated, or individuals had no clue how to handle it.
Unstructured data is often seen as the catalyst behind big data. It hardly plays a role in this, however. Usually, this information is correlated with geographic data, integrated with flat files of existing structured customer data and adding streams from new sources. This allows you to understand how users of a certain social media platform respond to your product.
There are two things that happened to big data. It helps to bring in more data from distinct sources, and this data can be micro-optimized. For example, at the stage where this tactical business decision has to be taken, you can easily change behavior using tools like smartphones.
The biggest benefit of this type of analytics is to reduce the response time. Data scientists now require the shortest possible time to respond to queries and models that took them months to build to answer.
Big data and business intelligence technologies have made it all possible as they enable information to be worked with before it is optimized, rationalized or re-rationalized.
So, what is customer retention? It refers to the actions and strategies that a company incorporates to attempt and maintain existing customers. To enable these actions, customer retention analytics provide predictive analytics from which customers may churn — which allows them to get ahead of it.
Better Big Data Analytics enables to boost customer loyalty. You will be able to act on the insights you gain quickly, providing you the chance to satisfy the consumer’s needs easily. One can use big data to build brand loyalty and gain more business. Here are some of the big data strategies that your business can use to derive insights from customer satisfaction, drive customer loyalty, and become more competitive.
With the help of predictive analysis, big data can predict which customers may experience problems or any type of inconvenience with a product or service, so the customer service can contact them before they call customer service line themselves. This offers the business with several benefits as the service representative will have a good idea of what the issue is before contacting the customer, so they can come up with a solution quicker. It also prevents customer annoyance with the held and long call waiting on the customer service line. Finally, customers who may have switched products due to a problem may be tempted to stay because the organization is proactive.
Big data also has the ability to define the requirements of new customers, so companies can match new clients with the best product choice for them. For instance, an insurance company can use call center notes, insurance claims, and other behavioral data to predict what type of insurance policy would be the best suited for a new customer. Using big data to increase customer retention is indeed beneficial. By immediately matching new customers with the right policy, customers are less likely to have complaints or begin looking for a better match anywhere else.
All thanks to social media that social media many companies can have a more personal conversation with their customers, but that completely depends on customers getting on and interacting on a particular social media platform.
Did you ever imagine being able to synchronize what a customer thinks even if they’re not on the Facebook page of the company? You can! With big data, you get access to real-time data about what a customer is purchasing, clicking on, and talking about. Big Data Analytics enables businesses to have a continuous discussion with the customer through tailored landing pages, mobile apps, and advertisements.
A contemporary customer wants to feel valued, so if in any case, they feel their demands aren’t met, they would go for other options. So with big data, you can access information such as each customer’s sex, place, and social data and enable marketers to send different offers to each person depending on their interests. Say a marketer utilizes location-based marketing to send an offer when a potential client is near a store. Instead of sending a generic promotional offer with Big Data, the marketer could send a unique deal with the bag, the person talked about on Twitter along with several other complementary product suggestions to go with it.
What most businesses do to retain customers and cater to new ones is by providing more product alternative. If businesses were able to limit their products to what the consumer actually wants to purchase, instead of attempting to provide everything to ensure sales, customer loyalty, and revenue would be much higher. It can be understood by the instance that all the choices on the toothpaste aisle, however, often leave customers overwhelmed, so instead of making a decision, they grab something that looks fine and get out of there. By evaluating the data found in customer transactions and inventory turnover, businesses can determine consumer preference, then narrow their product offering to what is most profitable. Companies that take the time to understand the need of a customer will see a tangible benefit over their competition as the product that the customer really wants will not be buried in all the other decisions.
Here are three ways in which companies can use business intelligence to better understand their customer base and promote customer retention:
Business intelligence can deliver countless analytical capabilities for evaluating customer profitability, as well as the ranking and score carding customers across a number of fields including net profitability, month-to-month client results, and channel productivity. For example, BI software could be used to rank existing customers based on the timeliness, frequency, and value of their purchases. This provides the company with the capacity to better organize its customers into more targeted opportunities groups for up-selling and cross-selling. Micro strategies business intelligence ensures that their sales and marketing efforts are targeted at retaining and optimizing the right customers and attracting the right prospects with the right offers, whether it is an upsell to an existing customer or an incentive to attract new customers.
Business intelligence can facilitate company planners to enhance forecast accuracy and the overall scheduling process for sales and operations. For example, BI software can monitor order and order line fill rates so the business is ready to manage seasonal spikes and drops in orders. A high-end food and beverage manufacturer can use this data to store their online (and in-store) shelves with the right products at times when demand is highest. When a decline in demand is expected, the producer understands how to cut back on production and save inventory space for other products. Business intelligence software also helps businesses to set the outlier limit to eradicate past events that could influence future customer demand forecasts so that the data is more accurate.
BI software encourages businesses easily plan, track and evaluate the success of their promotional activities to see what marketing campaigns/promotions your customer base reacts best. Marketing budgets can thus be shuffled and assigned to the more successful campaigns that generate the best possible ROI. For example, that same high-end food and beverage producer might like to track the effectiveness of a free shipping offer for the holidays versus the effectiveness of a 10 percent off coupon. What offer was used more frequently? What offer spurned clients to add more products to their online shopping cart? By comparing campaign companies ‘ actual outcomes with expected outcomes, they can “market” more smartly and better recognize opportunities to boost sales and growth.
By developing customer understanding, building targeted and personalized product offers and picking the right retention audience, customer journey analytics can maximize your understanding of customer retention model and make better decisions about how to improve it. For even a better customer retention, every customer interaction should be differentiated on the basis of what worked in the past and what is predicted to work well in the future. Most importantly, it should be tailored to each individual customer and their distinctive customer journey.