The retail industry continues to shine with great momentum. With its global value of 3 trillion dollars, its appeal is increasing day by day. This rise is strongly influenced by the emotional bonds established by the customers with the brand, rather than the seller-customer relationship. In order to create and strengthen these ties, to improve the shopping habits of customers, to improve the in-store experience and to strengthen customer retention, artificial intelligence technologies are used in the retail industry, as in all other sectors.
The retail industry continues to grow without slowing down, through in-depth analysis of big data such as customer retention, gaining new customers through strong customer relationship management, customer behavior analysis and customer journey analysis. The future of the retail industry is in the hands of accurate analysis and valuable artificial intelligence technologies. OptiWisdom plays an active role in the growth of the retail industry by using techniques such as customer relationship management, customer behavior analysis, customer journey, product trend scoring, customer lifetime value, predictive value segmentation, and behavior segmentation in its cutting-edge software.
Customer Retail Adventure Analysis
The customer behavior analysis technique used in OptiWisdom software maps the steps that customers can go through in a customer adventure, by mapping the critical points that customers go through from the beginning to the end, the problems experienced or likely to be experienced at these points, the estimation of the next steps of the customers with alternative routes in the future, how the customers will be met and how they will be met. It is an application that works how to establish a correct communication with customers. The steps taken by the customers, the time spent in these steps, the potential problems they may experience, provide the opportunity for early intervention. With the Customer Adventure Analysis technique used, it is determined early at which points customers are at risk of losing, and rescue interventions are carried out to eliminate all these risk points without losing customers.
Product Trend Scoring
According to the Pareto principle, 80% of the results are due to 20% of the causes, or conversely, 80% of the events affect 20% of the results. Based on this principle, OptiWisdom made an artificial intelligence engine called OptiScorer. OptiScorer can score according to the success criteria determined by product trend scoring, in finding the customers who have the most impact on the result. From the existing customer pool, it can estimate how much turnover or profit potential in which product according to the customer potentials. It can even open the door to work on products and services that are stagnating or not worked at all. Through customers’ product interest, it can provide customers with tailored personalized product advice. This principle was also used for problems and costs, by scoring the customers with the highest problems and costs, operational success was increased by avoiding customers with low success and high cost and problem risk, and operational difficulties were reduced at this rate. With the customer lifetime value study, it was successful in identifying and explaining the customers who create value for the company and their reasons, thus making interesting actions for customers and designing high value communication in communication.
Estimator Value Segmentation
One of the most important issues in value segmentation for retail is the rapid change in value. Rapidly changing rules, market structure, customer values in the retail area are going through a rapid change. Structures that can take quick action can be established to keep up with this changing segmentation problem. With the predictive value segmentations applied by OptiWisdom, it not only finds the current segmentation of the customers, but also provides the correct estimation of the future segments.
Classical approach such as customer acquisition, offering appropriate services and products to the customer, management of communication and relationship with the customer, and losing or retaining customers at the end of the road has been replaced by close behavioral analysis. Establishing the right communication at every stage from meeting the customer to the end with close behavior analysis and correct perception of customer behavior has gained importance. With behavioral segmentation, customers with high similarity in segments and high differences with other segments were gathered in the same groups. With this segmentation method, it enables you to establish a win-win relationship by determining strategies and ensuring that your company’s goals and customer needs overlap.