Rabiya Bronekar

There was an escalation which was coming from client for increase in CTQ Verification This process involves calling and non-calling cases to get verification; by conducting brainstorming and analyzing the past data it has been observed in most of the cases where verification is required from vendor, those vendors remain same. Here, the problem was agent has to spend a time daily research task for calling and non-calling element which was impacting their productivity and unable to get successful verification. How would you ensure that the overall process continues to deliver the intended outcomes over time? So, with the help of Data Analytics team a AI model was built which could gather last 2 years of calling and non-calling cases and mapped all the vendor from where we get verification We were getting good result data verification of 30% for non-calling cases and 40% on calling and other cases resulting in 70% mapping and verification improvement. What would you monitor, and how would you respond when performance starts to slip? Post 6 months verification no. again was leading to issue, also mapping % got reduced, so a brainstorming was conducted with agents to understand the issue and also data was analyzed, the key cause to the problem was new clients gets added quarter on quarter for which AI model needs to upgrade. Solution Planned: Process has been set for upgradation of AI model every quarter. Hence, the control plan was created a checkpoint was added in Monthly Audit checklist to ensure the updated data base to be appended in the AI model for new client to get better mapping

There is a project which has been taken to improve sales conversion project by 10% Problem statement:- The project has been taken to improve sales conversion project by 10%, which will directly improve incentive level The apporach adaopted process based study Brainstorming conducted for Process based study and data collated on the causes identfied Post which hypothesis test was done to validate the impact of causes on Conversion Test 1 Ho: Covnersion rate is same across all agent H1: Conversion rate differs across agents Test used One way anova P>0.05 null stays Test 2 H0: AHT has not impact on conversion H1: AHT has impact on conversion Logistic regression used to check the impact, P>0.05 , null stays Also, tested (test 3) Is any specific product has an impact on Sales conversion HO: No specific product has an impact on sales conversion H1 : Specific product has an impact on sales conversion P<0.05 – product has an impact on conversion The key factor product has an impact on sales converion has given signnifcant impact Basis which Recommender ie prediction model has been suggested to create AI based model for specific products which lead to more conversion and also product combination which can be sold with other has been analyzed basis AI model to focus mainly on key combination product In the improvement phase 2 batches created There is no difference in sales conversion rate between a One batch of 5 for all any combination product to improve sales Another batch of 5 only for AI recommended combination product to improve sales Basis 2 proption test observed AI based combination product was gving better output as compared to all product So, have noticed hypthesis testng can be used to validate the AI outcome Summary of the above exercise:- There is a clear distinguish between hypothesis test and AI prediction AI has suggested combination product while Hypothesis test used to statisically validate the conversion uplift

Yes, DMAIC still holds with AI entering the picture CSAT for project is at 52% and the target for the process was 55%, though other metrics are in green such as AHT and Accuracy and when relationship of CSAT vs AHT and CSAT vs accuracy, tenure etc. checked with the Correlation tool, no correlation observed which are 0.45 and considering industry standard we should consider greater than or equal to 0.6. Hence to rightly understand the issue with the help of AI support we took the entire dump of DSAT comment in order to understand the exact issues with the help of AI interactive tool keywords and datamining was initiated considering manual study on 2000+ customer Dsat comments to be a time-consuming process and which we would have taken a month and we were able to get study within 3-4 days with its clear bifurcation of categories in to soft skill improvement , agent learning , long hold time, rudeness, no quick resolution , as a next step why why analysis was done on this main categories along with agents and cross functional team. The solution notes with respect to Coaching, modifying pitch, soft skill training, warning on rudeness was built, monthly dashboard, Audits on long hold calls, the implementation plan followed for a month and there was noticeable improvement in CSAT score from 52% to 57%. In control phase to ensure the sustainability of the metric, Weekly dashboard on CSAT score continued for top and bottom performer, Random test planning every quarter and DSAT comment study with AI sentiment analysis/interactive tool recommender every qtr. We followed all stages of DMAIC while utilizing AI tool as for any CTQ metric improvement we should adopt a structured approach.