These last couple of weeks have been a little crazy for folks here at Gmid Associates, throwing us off the track on almost everything apart from the project deadlines. I’ll give the same excuse for the delay in this post (and hope no one finds out the real reason!).
We pick up the discussion exactly where we left off in the last post – on how analytics is changing the value proposition in BPO industry. So it will be a good idea to read the last post before you proceed. Here it goes.
Clustering/ Segmentation and other descriptive analytics techniques
BPO companies sometimes work under such dynamic environments that the data they need to work on doesn’t stay with them for long. Although at most times they might get the analytical inputs for their campaign strategies from the clients (e.g. segments/ treatment plan etc.), even then most of the times the segments themselves are so broad based that further fine-tuning of strategies becomes imperative. They can use their own historical data repository to identify most important of the data variables, slice and dice the data further and overlay their operational team expertise on this. This leads to further improvement to their clients’ strategies and tighter operations- reducing costs, increasing revenues and over delivering on client expectations.
Depending on the business vertical of the process involved, various descriptive analytics techniques are leveraged for impriving efficiency- from RFM analysis (recency-frequency-monetary) to clustering schemes around important business attributes- a number of tools are used to add layers of intelligence over the data and processes and run tight segment focused campaigns. Data mining techniques are also used to understand strengths and limitations of their resources for optimal alignments and resourcing.
Predictive analytics solutions are very relevant in all BPO business simply because they help optimize processes by predicting end results (e.g customer response in case of a collection call) accurately and hence enabling optimal strategy implementation. The predictive models are developed on historical client shared data and the transactional data that a BPO provider generates by running these processes. To explain with an example, A BPO firm is asked to run an insurance retention campaign on a portfolio of 1 million customers. By developing a predictive model to this end helps them greatly in identifying various distinct segments of customers- those who just need a gentle reminder message to pay up, those who need intensive treatment on why it makes sense for them to pay up and those who probably would not pay up at all and higher intensity treatment on them would rather work negatively for brand equity. All these segments of customer need different treatment strategies. Based on business needs and relevance, predictive analytics solutions ranging from -regression models to time series forecasts to neural models- from simple to most advanced techniques are being used in BPO business today.
Recent advancements in the field of predictive analytics have considerably improved applicability of high end statistical techniques into real time outsourced processes. Solutions today are leveraging machine learning capabilities to identify changing trends on dynamic datasets under process and enabling actionable strategies in real time. These solutions present efficiency improvement areas previously unknown- all because of smart usage of analytics and computing techniques.
These new generation BPO analytics solutions are set to change the rules of the game in the BPO industry. As businesses continue to look out for more than just “cost reduction” and “operational efficiencies” from their BPO relationships, the BPO industry will have to try and make the best possible use of the most important asset they have- data gathered during years of their client relationships. And analytics will help them do just that and more.
Companies are getting more comfortable with engaging one BPO partner for end to end business processes; this gives providers a deeper access to their clients’ work flows and business. This gives them a perfect platform to leverage the data and this domain knowledge- probably in a better situation than their clients themselves- to generate actionable analytics and deploy it on business processes for everybody’s benefit -from the clients to the providers to the end customers. This is the next logical step for the BPO industry. One that makes the world a smarter place.
- posted by Mudit Chandra