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Learn how Business Intelligence (BI) and Predictive Analytics can drive and completely change your customer experience game. Learn how to gather, analyse, and impact data to create experiences, increase customer satisfaction, and drive more business.
In today’s competitive market, providing an outstanding customer experience is no longer a luxury—it’s a necessity. Innovative ways through which businesses are trying to understand their customers better, to keep up with the changing winds, include Business Intelligence and Predictive Analytics. These are very powerful tools that, in some instances, may even provide insight into not only the understanding of customer behaviour but into finding their needs and thus giving them a more personalised, satisfying experience.
Let's first define the terms before we go into how BI and Predictive Analytics can be used in improving the customer experience.
Business Intelligence is the use of technology in the analysis and presentation of data to provide actionable information for corporate executives, business managers, and other end-users toward better business decisions. These analyse business operations in terms of past and present data, customer behaviour, and market trends.
On the other hand, predictive analytics uses statistical algorithms and machine learning techniques to establish the likelihood of outcomes in the future based on available data. In predictive analytics, businesses can tell possible events that might happen in the future by analysing behaviours and trends from the past, hence making proactive decisions.
Put together, BI and Predictive Analytics paint a complete picture of your customers: what they did in the past, what they are doing now, and what they are likely to do in the future. This insight is, therefore, invaluable to improve the customer experience.
The greatest benefit of using BI and Predictive Analytics together, therefore, is the ability to engage with a customer at a personal level. In an information-intensive world, pressures are going to mount on business organisations to be more attuned to what customers would want and need. In this framework, BI tools enable businesses to filter various customer data for relevant buying behaviours, preferences, and patterns of engagement. This knowledge about customers is then used to predict what the individual customers would want or need next with the help of Predictive Analytics.
For example, in such a situation, Predictive Analytics may forecast the need for restocking at the right time or suggest complementing products to a customer who frequently buys products in some category. Personalisation of this nature makes a customer feel valued and cared for, increasing his loyalty and satisfaction with the company.
In the modern high-speed world, customers will not accept delayed or inefficient service. Delays in attending to customer inquiries or fixing problems might cause frustration and dissatisfaction. BI tools can help businesses analyse data on customer service to identify bottlenecks and areas for improvement. Predictive analytics will take this one step further by forecasting potential problems before they happen.
For instance, if data offers evidence that some questions take longer to answer than others, businesses can then make resources more available or train additional customer service representatives for effective answers to the questions. Predicting the needs and potential problems of a customer makes the company reduce the response time and enhance its overall customer experience.
Any business must understand its customers so that it can come up with products and services that relate to the customer's needs. In this manner, BI tools allow businesses to go through customer feedback, the pattern of their purchases, and market trends to know what is valued most by customers. Predictive Analytics can then use the coming trends and keep the businesses ahead in innovation or refinement of such offerings.
For instance, the analysis of customer data may reveal a growing interest in environmentally friendly products. Predictive analytics can allow these firms to predict potential future demand for such products and hence change their product lines in accordance. This way, the companies can stay ahead of the race and, accordingly, meet the demands of customers with products that exceed expectations.
Read More: Maximise Your ROI By Learning The Incredible Advantages Of Customised ERP Systems
Customer retention is key to maintaining profitability and growth. A high customer churn rate translates into a significant loss for the bottom line of any company. Business Intelligence and Predictive Analytics assist in the identification of customers who are at risk and allow for proactive action on the part of an organisation.
With tools based on business intelligence, organisations can flag customers who might be at risk based on some behaviour indicators, such as declining purchase frequency or inactivity. Predictive Analytics can then evaluate the likeliness of these customers churning and hence recommend targeted interventions, such as personalised offers or enhanced customer service to re-engage these customers. By solving possible causes of the problem in advance, businesses retain more customers and maintain better relationships.
Good marketing refers to delivering the right message to the right customer at the right time. The customer data might be analysed in BI to segment the audience, based on their demographics, purchasing behaviour, and preferences. Then predictive analytics can forecast what marketing message or campaigns would likely resonate with which segment.
For instance, the best ERP software in Kolkata can allow a business to analyse data from its earlier marketing campaigns to determine which particular strategies worked best. Predictive analytics can then be used to establish a prediction based on that data for the efficiency of the upcoming campaigns, therefore allowing an organisation to distribute its marketing budget more judiciously. This targeted approach does not just raise marketing efficacy but also improves the customer experience through the delivery of content that is relevant and timely.
However, the fact of the matter is that not all loyalty programs work well. The program does not have to necessarily reward existing customers but can stimulate long-term engagement with them. Business intelligence and predictive analytics may help the business in designing and optimising customer loyalty programs that work for their clients.
By studying information about the spending behaviour of its customers, their preferences, and levels of engagement, BI tools can guide a business in determining what kinds of rewards or incentives are most attractive to which kinds of customer segments. Predictive Analytics can then forecast which customers are most likely to participate in the program and what rewards will drive engagement. The results of such analysis through data are quite manageable, making sure that the loyal programs are effective and meet customer expectations.
Imagine the power of predictive analytics to act in advance before customers themselves realise an opportunity or threat exists. Predictive Analytics can track patterns and trends from history to infer future behaviour.
For example, an organisation selling seasonal products will be able to determine when the demand for certain products will increase. This will enable businesses to stock up their inventory, and due to targeted marketing, they are sure to meet the demand of customers without fail. More than that, this proactive approach enhances the satisfaction of customers and hence increases sales and profitability.
Read Also: Futureproof Your Business: The Importance of Industry-Specific ERP Solutions
Understand the customer journey: A BI solution allows a business to map out various touchpoints of a customer, from an initial point of contact to post-purchase support. This data could be drawn by Predictive Analysis to predict the most likely paths customers would take based on their behaviour and preferences.
For example, if the data shows that many customers tend to drop out during checkout, then Predictive Analytics can attribute a reason such as 'Are the forms too complicated?', 'are the prices not clear?', or 'Are they concerned about shipping?'. It helps the business eliminate these pain points to make their customer journeys frictionless and delightful for customers. This results in an improved customer experience; hence, more chances of converting and making repetitive purchases.
Cross-selling and upselling can be really good ways of making money, but it should be done with caution so that it doesn't become pushy or irrelevant.BI analyses customer preferences and purchase history for cross-sell and upsell opportunities. Thereafter, predictive analytics determines what other products or services the client would likely be interested in based on the client's past behaviour and current trends.
For example, suppose a customer is frequently purchasing electronic gadgets of high-end quality. In that case, Predictive Analytics might support the sale of an extended warranty or other complementary accessories at the time of purchase. By surfacing these kinds of offers precisely at the right time, businesses can do much more than push another product line to customers and add value to their experience. In turn, this creates a better customer experience, which further increases overall sales.
While there is a goldmine of information within the customer feedback, it is overwhelming to manually analyse reams of data. BI will aggregate and analyse feedback from multiple sources. With predictive analytics, it will assess customer sentiment so that it can predict any potential shift in customer satisfaction.
For example, a system recognizes a customer who is one of the best for a business and identifies when they start fading, so the business can take prior actions to give special incentives or reach out with personalised messages. Through predictive insights, businesses can raise the level of staying in touch, hence elevating the experiences and loyalty so that more valued customers keep coming back.
Customer loyalty is the key to long-term business success. Usually, it is more expensive to bring in a new customer than it is to maintain an existing one, and existing customers are often more profitable over time. Some use BI tools for recognising repeat purchasing behaviour or other indicators of deeper loyalty, such as interaction with a loyalty program. Predictive Analytics can then anticipate when and how to engage these loyal customers to maintain and even increase their loyalty.
For instance, it may be able to predict in advance when a loyal customer is most likely to be primed for his next purchase according to past behaviour and recommend sending a personalised discount or exclusive offer at just the right time. For example, it would recognise when the engagement of a loyal customer begins to taper off and alert the business to take proactive measures, such as special incentives or reaching out with personalised messages. Using predictive insight in keeping contact with loyal customers enhances the experience and further deepens the loyalty with the valued customers continuing to come back.
Excellent experiences only help in this customer-driven world to stay ahead of the competition. BI and Predictive Analytics, in this regard, help businesses know more about the inclinations and behaviours of their customers and thus allow further personalisation of interactions, better services, and even anticipation of needs. This eventually drives a more satisfactory customer experience.
Whether you're using manufacturing industry-based ERP or documentation software for export, both BI and Predictive Analytics take your operation to a place where engagement with the customer is no longer about meeting their expectations but superseding them. If you're ready to unlock your full potential and maximise customer experience, adopt BI and Predictive Analytics in your strategy starting today.
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