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How to Predict Customer Behaviour 
4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 1/11 
How to...
How to Predict Customer Behaviour 
4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 2/11 
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How to Predict Customer Behaviour 
4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 3/11 
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How to Predict Customer Behaviour 
4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 4/11 
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How to Predict Customer Behaviour 
4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 5/11 
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How to Predict Customer Behaviour 
4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 6/11 
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How to Predict Customer Behaviour 
4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 7/11 
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How to Predict Customer Behaviour 
4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 8/11 
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How to Predict Customer Behaviour 
4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 9/11 
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How to Predict Customer Behaviour 
4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 10/11 
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Optare solutions - Predicting Customer Behaviour

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Summary of the presentation made by Optare Solutions team during the “4th Annual Enhancing Customer Loyalty and Retention in Telecom” in Barcelona, 18-20 Nov 2014 talking about Optare's innovative solutions to predict Customers Behaviour in the Telecoms Market.

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Optare solutions - Predicting Customer Behaviour

  1. 1. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 1/11 How to Predict Customer Behaviour A Project Alberto Moraña, Head of Solutions and Integration amorana@optaresolutions.com Eva Endress, Sales Manager Europe eendress@optaresolutions.com November 19th, 2014 Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com
  2. 2. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 2/11 Table of Contents How it all began - page 3 Predicting Churn - page 4 Obtaining Comprehensive Data to allow Targeted Campaigns - page 7 Simulating the Lifetime Value - page 8 Using new Data Sources: Perceived Quality of Experience - page 9 Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com
  3. 3. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 3/11 How it all began A small cable operator in Galicia, a region in the north of Spain, located above Portugal at the Atlantic coast asked us two years ago: “There are fancy tools and applications out there in terms of Data Science and Analytics but a lot of them are expensive and complicated. Can you help us to find a simpler and faster way to target our retention campaigns on those customers who really can be retained ?” They didn’t want to start with a full blown solution immediately, but they were rather looking for somebody with specific Telecom knowledge in the Analytics area to start with a small customized solution that could be implemented quickly and fit their budget constraints. With that challenge, a project was born in cooperation with different stakeholders: two Spanish operators, the University of Vigo, Optare Solutions and other research teams. Of course, things are never simple, and our first attempts implementing statistical formulas available on the market, combined with the subscriber data we obtained from the operators, turned out to be rather useless because they predicted for instance 0 churn at a 94% accuracy. Digging deeper, not losing the enthusiasm despite our initial difficulties, after a few tries they were thrilled by the simplicity and usefulness of our results. Today, we have the great pleasure to share some of these results with you: 1. Predicting Churn 2. Obtaining Comprehensive Data to allow Targeted Campaigns 3. Simulating the Lifetime Value 4. Using new Data Sources: Perceived Quality of Experience Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com
  4. 4. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 4/11 Predicting Churn To predict churn, the question is: “For each of my subscribers, one by one, what is the probability that he/she leaves within the next quarter ?” First, we need to gather, retrieve, fetch, obtain subscriber data from somewhere. Here, our extensive technical knowledge of the OSS/BSS landscape benefits us a lot to help the operators implementing so called ‘connectors’ to their network. On top of that, thanks to our experience with Telco business models and the underlying data models, we are able to provide the operators with efficient and simple solutions for Data Cleansing transforming it to clean input data for modeling. The statistical modeling is based on subscriber data of the two previous quarters: Figure 1: Statistical modeling In the example above, with the subscriber data of Q1 and the real churn in Q2 we generate the models. These models are then applied to the subscriber data of Q2 to predict the churn of Q3. Step 1 of our simulations includes basic input data, in step 2 we include complementary data from different sources and finally in step 3 data from a new tool we have created (for step 3 see chapter “New Data Sources: Perceived Quality of Experience”): Figure 2: Input Data Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com
  5. 5. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 5/11 There are four possible types of classifications for each individual subscriber: Customer (e.g.) Prediction Real behavior Classification Peter Churn Churn True Positive (TP) Paul Churn Non-churn False Positive (FP) Mary Non-churn Churn False Negative (FN) Elena Non-churn Non-churn True Negative (TN) Figure 3: Statistical Classification In other words, the sum of all four categories is the sum of all subscribers: Figure 4: TP, FP, FN and TN Since we predict a churn probability for each individual subscriber, the ‘Decision Threshold’ comes into the game. The question is: Where do I draw the line to decide if a subscriber will leave or will stay ? At >30% probability ? Or at >50% or >75% ? End of September, when Q3 is over, we compare the predicted churn to the real churn of Q3 as a function of the Decision Threshold: Figure 5: Predicted Churn as function of the Decision Threshold Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com
  6. 6. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 6/11 We can see that even with a relatively low decision threshold (30%) we make very few errors: FP in orange (all Pauls) is still very low. Now, why is it important to have a decision threshold as low as possible ? It’s a little bit complicated to explain, let’s try with a few examples. If your decision threshold is at 0%, which means everybody is a potential churner, the probability of guessing a churner correctly would be equal to the true churn rate, in our example 6%. At a higher decision threshold, you improve both: the probability that a chosen churner is really a churner (TP, the Peters) and you decrease the error you make on FP, the Pauls. But, at the same time you increase the error on the other side: the ‘False Negative’ (FN, the Marys), which are churners you miss out on. The challenge is to find the right compromise. For instance, in a very personalized campaign with a high investment per subscriber, you might want to increase the decision threshold to increase the probability to hit the right target subscribers. Or vice versa, in a cheaper campaign you might accept a lower probability in order to include more possible churners. Figure 6: Targeted versa Scattered Campaigns Coming back to figure 5, the results show that our method is very good because even at a low decision threshold (e.g. at 30% to included as many real churners as possible), the mistake we make on the other side is still very low. Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com
  7. 7. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 7/11 Obtaining Comprehensive Data to allow Targeted Campaigns One issue with analytics sometimes is that the results contain too much information that is not really comprehensive enough to target campaigns and other business processes, neither is it possible to measure the impact of campaigns. Again, depending on how targeted (and how expensive) your campaign is you might choose a method that is a little bit less accurate but gives you a lot more practical user data in return. Figure 7: Influence of each subscriber attribute Table 7 shows the output for fixed lines of one specific model that gives practical information: ● For instance, 28,3% of the fixed line subscribers don’t have a contract and have been with the operator more than 15 days. Out of this group, 6.2% will churn. ● On the other hand, even though the churn probability for those who have one or more products, and have been with the operator for more than 16 days, is higher (43,2%), it’s a lot less subscribers (only 1,4%). With his detailed information, very targeted campaigns are possible. Typical questions, where this information can help you, would be : ● What new products shall I offer to whom ? ● Who is most likely to buy them ? ● How do I know if a reduction in churn comes from a campaign ? ● What is the ROI for a campaign ? Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com
  8. 8. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 8/11 Simulating the Lifetime Value Using the same (or similar) data to predict churn, another useful idea is to calculate the Lifetime Value of each subscriber. The question in this case is: “From today onwards, how much money will I earn from this subscriber ?” The idea is: ● to estimate the lifetime (in days, months, years) ● to calculate the value (in €) from today until the subscriber leaves the operator ● to categorize them in terms of value that is left to come ● to react accordingly (privileged treatments, campaigns to increase the value, etc.) Figure 8: Example of Lifetime Value (in months) depending on the year of subscription And of course, it can be used in combination with the churn probability: Figure 9: Churn Probability and Lifetime Value Comparing both values gives you another view of whom you want to target. Again, since we have the real names (well, identifiers of course for data protection reasons) on the churn probability (it’s Peter and Paul, not Mary and Elena), you can look at each value they have for your company. Comparing this amount of money to the amount of money you’ll spend on any campaign or action, you can evaluate the impact of a campaign in terms of churn and/or lifetime value. Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com
  9. 9. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 9/11 Using new Data Sources: Perceived Quality of Experience A clever tool to obtain input data from each subscriber measures the Perceived Quality of Experience capturing technical parameters end2end. It’s again a joint project of Spanish operators, universities and R&D departments of different vendors where one of them is Optare Solutions. The way a subscriber uses communication services has changed during the last years and based on the information from participating operators there are mainly four different profiles: Gaming (“Juegos” in Spanish :-)), VoIP, Video and Internet. Figure 10: Four profile types for QoE The key idea is: Usually the operators have a lot of information about the Quality of Service at network level but they can’t see the user’s perception of the service. For instance, a good internet connection (e.g. 100Mbits/s) could still result in a bad Video Streaming experience because of an incorrectly configured WIFI connection. Many issues can easily be solved by a simple change in the WIFI configuration. The application itself: 1. can either be installed as stand-alone solution on any device (cell phone, PC, tablet, etc.) 2. or can be used behind another application or game by technicians, end-user or APIs. The next table shows our demo version. By choosing one or the other profile, the user can see the quality of the connection depending on the chosen profile. Since our demo version is in Spanish for the moment, you’ll find the translation of the keywords underneath the pictures. Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com
  10. 10. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 10/11 Bandwidth (Ancho de banda) Downloads (Cuadal de bajada) Uploads (Caudal de subida) Gaming (Juegos) How satisfied are you with the network ? (¿ Cuál es su satisfacción con la red ?) Diagnosis (Diagnóstico), Video Gaming (Juegos de red) Connectivity (Conectividad), Conditions of the network (Condiciones de red) Speed (Caudal), measures the downloading speed Jitter, measures the delay of the connection Scanning of the ports (Escaneo de puertos), availability of the ports Latency (latencia), measures the congestion of the network, low (baja) Figure 11: QoE application The subscriber sees a list of output parameters on the screen but a lot more data is sent to the operator. Since we talk about ‘real end2end measurements’ it includes the device configuration, the WIFI connection and network parameters. With these parameters, we calculate the perceived QoE depending on each profile. To give you an example, the formula for voice is the following: Figure 12: QoE for VoIP RTT: Round-Trip-Time, ln: latency, p: packet loss. According the output value of R, we can say: Figure 13: Perceived QoE of VoIP Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com
  11. 11. How to Predict Customer Behaviour 4th Annual Enhancing Customer Loyalty and Retention in Telecom Summit page 11/11 Key messages are: ● It’s not always bandwidth that is missing in case of a perceived low quality !! ● The tool makes available the complete data thanks to the true end2end measurements. ● It helps to reduce costs for troubleshooting: ○ Enhancing Self Care ○ Providing solutions for common issues with smartphones directly to the user ○ Providing call centers directly with the necessary data ○ Reducing the number of physical technician visits We are happy to discuss any related topic with you during the conference !! Head of Solutions and Integration Alberto Moraña amorana@optaresolutions.com +34 646 600 588 Sales Manager Europe Eva Endress eendress@optaresolutions.com +34 610 499 472 Feel free to find us ! Parque Tecnológico de Vigo, Calle C, Nave C7 36315 Vigo, Spain T/ +34 986 410 091 F/ +34 986 423 379 www.optaresolutions.com

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