An award-winning data science, analytics and strategic data solutions and consulting business: Beyond Analysis

Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system. Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click. Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. Beyond Analysis is a data science, analytics and strategic data solutions and consulting business. They are globally aligned, yet locally focused, in delivering advanced data solutions.​

Since 2007, Beyond Analysis has helped its clients to use data to drive efficiency gains and business improvements by enabling them to better understand their customers and business operations. Using the latest in artificial intelligence and machine learning, Beyond Analysis models and forecast behavior that, via its proprietary visualization tools, enables the sharing of insights for taking action and delivering competitive advantage.​ Beyond Analysis operates its data-led strategy consulting services globally and deliver data science, engineering and analytics through the highest quality data talent, borderless data services and best in class global thinking. Beyond Analysis’ award-winning team of data scientists find the patterns in your data using simple segmentation through to complex machine learning algorithms. Beyond Analysis transforms these patterns by automating your data into powerful analytic applications that land in your business to deliver financial returns, improved customer experience and employee engagement.​

Principles of putting data to work

​Today, data is disrupting and changing the competitive environment.  Many long established businesses are now seeing the rise of big data and how it pushes boundaries, driving increased competition from start-ups enabled by data, and how it is now a core component of market value. Data is not a new concept.  In fact it was sound data analytics that underpinned the post war recovery of Japan.  This was being practiced by the likes of William Edwards Deming and Masaaki Imai during this period.  Then it was called Kaizen. What has changed is the exponential growth in volume and velocity of data, making it a hot topic where everyone can see the possibilities.  Businesses that are able to put their data to work are outperforming their competitors by significant margins.  In retail, for example, Big Data has the potential to increase margins by as much as 60% and there are many great examples of companies over the past 30 years making huge strides using data. But getting results from data is fraught with difficulties.  It is no secret that the majority of data implementations tend to fail.

Customers churn modeling

It’s generally accepted that it costs more to acquire a customer than to retain one. Managing churn appropriately is a huge growth driver for any business. Using Churn Modeling it is possible to identify the key drivers and behavior changes that are pre-cursors to a customer leaving. Models can be created that continuously learn using historical customer data to identify the triggers and behavior changes that indicate potential churn. This can spot and score the risk of potential churn far more early than traditional regency and value models and this can have a big impact on costs. The process requires an initial piece of discovery analysis to properly identify how churn is happening, in which customer groups and what stage of the customer lifecycle.  Ideally all of this can happen using data, but sometimes it is not straightforward to integrate customer events across marketing, sales and customer services.  If this is the case it recommends quickly probing for potential issues from the teams at the customer coal face and then validating them in the data. Once it is clear where the problems lie the Data Scientists can go to work to identify the features of interest and figure out the right type of models to build that will score individual customers based on their propensity to churn.  They then test and validate the model with training data set before deploying and testing the models in real life.

Meet the leader behind the success of Beyond Analysis

Paul Alexander is the Founder and Chief Executive Officer of the UK’s leading customer insight group, Beyond Analysis. Responsible for sales and marketing, operations, partnerships and customer service, Paul established Beyond Analysis in 2007 and has grown the business into a highly successful and internationally recognized consumer insight and strategy business.

“We help our clients use data to transform their businesses.  By better understanding their customers and business operations they drive efficiency gains and business improvements, ultimately delivering competitive advantage.”