Ahmed Nour Predicts Lower Costs Through Analytics
•
1 like•56 views
Opex Chapter Meeting-Symbios Consulting While lots of companies race to implement new technologies (Artificial Intelligence, digital self-service, data analytics), they overlook obvious and easy ways to fully utilize what they have. -Opex chapter meeting video on youtube: https://www.youtube.com/watch?v=2AUMl4nC0UU - Symbios Consulting Youtube Channel: https://www.youtube.com/channel/UCx7HKNnSIlEEE5JKI4JsA4A
Recommended
Recommended
More Related Content
What's hot
What's hot (18)
Similar to Ahmed Nour Predicts Lower Costs Through Analytics
Similar to Ahmed Nour Predicts Lower Costs Through Analytics (20)
Recently uploaded
Recently uploaded (20)
Ahmed Nour Predicts Lower Costs Through Analytics
- 1. Ahmed Nour Prediction to lower cost to Serve 20 June 20191
- 2. Index 2 20 June 2019 • Intro • Strategy for prediction. • How to Predict. • Results of predictive models.
- 3. 3 Profile 16 years of overall experience across variety of markets, led deployment of experience strategies in the gulf region with heavy exposure to the overall EMEA region. Certified Lean/Six Sigma Master BlackBelt. Digital strategist certified by Google (squared online), understands how to build cutting edge digital strategies and bridge them to the physical world to ensure maximum return. Winner of more than 20 CX awards including, Best CX Brand, best CX Transformation Program and Best Customer Service Strategy to mention a few. Ahmed Nour 20 June 2019
- 4. Is it useful to predict? 20 June 20194
- 5. 20 June 20195 “ Predictive Analytics is the process of learning from historical data in order to make predictions about the future.” Insight Maturity of analysis FuturePast “Sense & Respond” “Predict & Act” Hindsight Insight Foresight Reporting What Happened? Analysis Why it happened? Predictive What could happen? perspective What should we do?
- 7. 7 Four steps to formulate your strategy Service Demand Contact Strategy Customer Preference Customer Management “Who is going to contact you and why?” • Predict which customers are in demand for service and the root cause for their issue(s). “Which customers should we act on and which should we “sleep”?” • Predict which customers need to be proactively contacted to deflect/ avoid issue. “What are the right time and right channel to engage with customers?” • Predict the right time and channel(s) to engage with customers for both inbound and outbound contacts “What should we do to best serve our customers?” • Predict the right service action in response to customer demand Step 1: Who & Why Step 2: Act or Sleep Step 3: Right Channel / Time Step 4: Right Action 20 June 20197
- 8. 20 June 20198 Proactive Reactive Proactive actions aiming to reduce frequency of contact (FOC) Maximize efficiency when receiving inbound contacts and optimize experience All customer interactions would be more effective if we could know upfront which customers are affected by an issue and why
- 10. 20 June 201910
- 11. 20 June 201911 2
- 12. 20 June 2019
- 13. How to Predict
- 17. 17 Precision Recall Accuracy 1 2 3 20 June 2019 17
- 18. No silver bullet, iterative approach to test what works. 20 June 201918
- 19. 20 June 201919 Pilots, POC’s & consultancy approach actually work. Know Construct Show Sustain
- 20. Results of Predictive models
- 21. 21 Next Best Action doesn’t mean offering fries with every order, or… Network & Usage Customer Interactions Handset & Tenure We take everything we know about and Only offer what is relevant NBA 20 June 2019 21
- 22. 22 Target TNPS with NBATNPS without NBA +71%+53% Made 20 million USD from previously unmonetized channels 20 June 2019 22
- 23. 23 Unexpected Extras Operation People 1 second improvement in AHT SLA improved by 2 points Abandonment Rate improved by 1 point Employee engagement improved by 8 points 20 June 2019 23
- 24. 24 92 96 97 105 116 120 151 163 181 197 211 275 364 364 405 560 507 560 616 6.6 6.8 6.6 6.7 7.1 6.9 7.8 8.6 9.5 10.6 10.2 9.9 9.9 11.0 11.4 10.5 14.4 15.4 18.6 0.0 5.0 10.0 15.0 85 185 285 385 485 585 Sept'16 Oct'16 Nov'16 Dec'16 Jan'17 Feb'17 M ar'17 Apr'17 M ay'17 June'17 Jul'17 Aug'17 Sep'17 Oct'17 Nov'17 Dec'17 Jan'18 Feb'18 M ar'18 Thousands Unique Customer and Freq of Visit Unique customer Frequency of Visit Prediction on it’s own is not really useful unless you know what to do with predicted data!
- 25. Contact by Design … Complexity & predictability 25 20 June 2019 High Value Medium Value Mass 50% Chat&AppIVR&USSD 10% 20% 10% 10% 10% 20% 20% 40% * 100% outbound * 20% outbound 10% 10% 10% 40% 15% 10% 5% Mass Market
- 26. What was also made possible by prediction is drastically lowering cost to serve by using a RFT commercial model. 26 20 June 2019
- 27. 27 20 June 2019 Not Scary Multipurpose Useless on its own Prediction is…
- 28. Thank you