Deductive Data Analytics Creating relevance through data mining Kris Marshall – ICE 2015

Summary Building a team to deliver value The advent of ‘Big Data’ and cheaper memory Using 3 rd party data to enrich the customer experience Segmentation techniques and promoting relevance Predictive modelling – theory and reality Lifetime Value modelling Propensity to churn Utilising models in a near time environment

The advent of the Data Scientist Data scientists are now integral to delivering value from data. They combine IT knowledge, statistical modelling and programming skills to derive key messages from your data. Research has never been more prominent in data analytics Choosing the correct technique to solve your problem is crucial As data sizes continue to expand, we are able to increase the statistical significance of the results of analysis. The continued evolution of the programming language ‘R’ has ensured that we can harvest results from big data sets.

Big Data As an online business we have access to terabytes of data daily and we want to be able to interrogate this data quickly. The cost of memory has decreased significantly, and by putting data ‘in memory’ we can analyse data as quickly as we can receive it. Products like Kognitio have mastered the art of ‘Parallel’ architecture which enable us to run many models at once, without impacting performance. Big data shouldn’t be seen as a challenge, it should be seen as an opportunity. More data means more accuracy, as long as you have the correct data.

3 rd Party Data Even though we have a huge amount of customer level data, we can enrich this data by using 3 rd party data. Free products such as Google Analytics have API’s that allow you to create custom fields relating to your customer. This means you can garner a huge amount of value from pre acquisition data Using this data to enhance the conversion experience adds huge incremental value. Data from companies such as Oddschecker and Betfair is available on the open market and can improve decisions made by trading teams. You need to be selective with this data, as in many cases, the DW environments aren’t big enough to simply bring all of the data in.

Segmentation to improve relevance Effective one to one marketing can only be achieved by segmenting your customer base appropriately. Creating segments or cohorts based on value, product and player preferences allow you to tailor communications appropriately. Having self learning models in operation allows for these cohorts and segments to be changed as player behaviour changes. Using decision trees and random forests to allocate segments improves accuracy, when compared to simple three dimensional RFM models. These models should be underpinned with models based on historic customer interaction.

Predictive and Prescriptive Analytics Being able to, at a customer level, predict key measures such as Lifetime Value (LTV) and propensity to churn create a host of opportunities. Arming marketing leads with ROI at an acquisition channel level, and even down to an individual affiliate, allows for continuous optimisation. A 1% improvement in acquisition effectiveness can add millions of pounds to the bottom line. Prescribing treatments at a customer level, based on likelihood of churn can significantly boost LTV and minimise reactivation costs. LTV Modelling varies massively by product, so you need to choose the correct approach to ensure effectiveness. Applying your model retrospectively allows for optimisation in advance of release.

LTV Modelling Predicting activity rather than value provides a more accurate model Each product and, when looking at Sportsbook, each major sport should have an individual LTV model. The individual models roll up to give you a company level metric. This approach allows you to measure marketing effectiveness at a more granular, product and sport level. Seasonality in sports means that you have to use very different approaches across products. Regression techniques, such as Bell’s Regression, which has been used in Cancer research, is superb when addressing seasonality issues.

Utilising models in near time Your full architecture from data source, to DW staging and through to CRM delivery need to be capable of working in near time. An overnight load process is restrictive in applying model findings in near time. Trickle feeding topical data (such as bet data) in and out of your environment allows for improved relevance. Products like Exact Target and Adobe allow you to set up ‘if this happens, do this” models based on operational data, but using topical data is where the real value add comes. The effectiveness of the ‘do this’ model is improved greatly if there is some near time data available.

The keys to adding value Hire a data scientist or two Improve your architecture to allow for more data analysis in near time Don’t be scared of big data, see it as an opportunity Test, test, test your models to improve accuracy Model at the player level, it will allow you to roll up to any customer grouping. Bury your data insight teams in the business. Knowledge of the business for your analysts is integral. Build a single view of your customer, it will ensure you manage all touch points effectively.