Next Best Offer for Banks
The Next Best Offer iERP Module is perfect for banks.
Our software solution is automatically evaluating customer and transactional data and helping to increase revenue by predicting the most relevant products or services and up-sell/co-sell opportunities to the individual customers. iERP will help you reduce customer churn by analysing customer behaviour and predicting the probability of customer churn.
The Next best action / Next best offer module uses data from across the business to predict the next best action required to enhance customer satisfaction. The solution can handle more than 200 different parameters including a customer’s interactions with bank, current and historical products and services, demographic information, and other inputs. Data, which can also include website and internet banking visits, incomplete transactions, abandoned baskets, call centre calls and email history, is all handled in real time to predict the products and services to upsell and co-sell that will best meet the customer’s needs.
Use case 1: Creating More Effective Marketing Campaigns
In this scenario the algorithm is being used to predict the best ways and times to deliver enhanced customer satisfaction.
Predicting: The algorithm identified opportunities to enhance customer satisfaction and revenue by predicting the products and services that are most relevant to the customer’s interest, allowing the business to promote offers that most closely meet the customers needs. This is achieved by reviewing up to 200 different parameters such as previously used products and services, demographic information and combined with customers interaction history online, through call centres and by email.
Resolving: This approach minimises the chances of irritating customers with irrelevant products and services, while providing much better opportunities to generate revenue and add value to each customer relationship.
Result: Having a granular understanding of customer preferences and interests adds significant value to ERP and CRM systems, allowing the business to integrate data to deliver more targeted and effective personalized campaigns and landing pages. In turn this delivers a better response to marketing campaigns and delivers a higher ROI.
Use case 2: Enhancing Customer Retention in Call Centre Environments
In this scenario our algorithm predicts what the potential customer might do next. Options could include closing a bank account or ordering a new service.
Predicting: Once our algorithm has identified the customers possible actions it predicts the highest probability for the action the customer will take next and aligns this with the defines set of rules for what should then happen. For example, if the algorithm predicts the customer might close a bank account it will put in place the options available that would help to retain the customer.
Resolving: Call centre staff would be able to see the real-time prediction and be ready to respond accordingly with a range of potential activities which would help to retain the customer. The same activity can also be translated to digital communications, allowing the firm to contact a client digitally with the same customer retention strategy.
Results: Compared to predicted customer churn, our solution increased customer retention by 1.5%. For a large business this could represent thousands of individuals.
iERP’s mission is to provide an end-to-end business prediction platform with modules that address multiple business scenarios and ZERO required knowledge of artificial intelligence or machine learning technologies.
We are helping companies to predict sales and inventory demands, what their customers are going to purchase next, or identify customers that are going to be late with the payment.