- Head of CRM
- Chief Marketing Officer
- Chief Sales Officer
- Chief Strategy Officer
- Board Member
- Marketing Manager
- Business Analysts
- CRM Director
- Retention Manager
- Data Warehouse specialist
- IT Manager
To whom do we recommend?
- Telecom/Online companies who want to achieve improvements on traditional data mining churn prediction/acquisition models
- Telecom companies who have a large prepaid portfolio where other rich segmentation data is not available, but aggregated CDRs can be extracted
- MVNOs who don’t have resources to implement large data warehouse projects and -similarly to prepaid dominated operators- have less data available on customers in structured format other than CDR
- Telecom companies who try to develop and diffuse socially contagious Value Added Services, or boost mobile advertising efficiency and click-through
- Banks to estimate risk of loss of SME universe when big corporations churn
Aim of the project
Mining the warehouse of user calling and other communication and product adoption behavior offers the ability to improve targeting, retention and acquisition activities. This is new to an approach based not only on traditional measures of monthly spend, length of loyalty contract, handset subsidies but also combining these measures with clustering calling behavior, centrality, telecom adjusted PageRank© type influence score, spreading of VAS usage and affinity. With these measures we will help you reduce churn, identify false rotational churn, decrease your retention costs, and achieve double digit percentage increase in social service diffusion.
The following example is given for a product diffusion project. During data understanding process and pre-analysis, building the social graphs and situating product diffusion around nodes at points in time are done. We are using the data of past and current call data records and other communication patterns to build the social networks in points in time and identify the past and current graph measures of the persons around whom service adoption accelerated. To weigh the links between individuals, we use intensity of communications between members. We will cut the graph according to business objectives if necessary, to get evening/weekend graphs for example. To improve the network information, we will also combine the service adoption data with demographic and other transactional and product affinity data. Each decision a customer made regarding a certain product as well as demographics characteristics will be included in a vector summarizing what we know about the customer. Using these vectors, we will assign similarity scores to pairs of consumers, and obtain a different set of weights, which, combined with the previous weights, will give us a more complete picture of interactions between customers. Then we will apply our clique-percolation algorithm to this network to identify communities and opinion leaders. This method allows us to build up closely connected group of members, who are likely to influence each other’s product adoption decisions. The building blocks of these “communities” are the so-called cliques. In a clique every member is connected to another one by link, not only based on social distance, but also based on service adoption history. Communities are obtained by identifying sets of cliques, which have large overlaps. In this way, every two member of a community is connected through a series of cliques. Combining these measures, we will determine each member’s potential to diffuse services/products. By analyzing the past growth of the network, we can identify the percentage each member has brought in from his viral potential and focus on the ones with adaptive campaign, who still have more potential.
The main findings of social network analysis projects are:
DOs and DON’Ts
Example on fingerprinting and rotational churn: This figure comes from an SNA dataset, represents the comparison of weighted calling patterns of 2 different IDs within Operator's network in 2 different points in time, indicating rotational churn as the first ID stopped communication, the second gradually increased. Calling graphs in points of time can be also analyzed to uncover internal and external tariff migration patterns and help designing plans for segment protection, price competition.
Why we are different