Friday, December 4, 2015

Helping Celebrus Clients Make The Most Of Mobile

In the last six months we have witnessed a tipping point in mobile deployments of Celebrus. It is pretty clear to us that for many customers mobile traffic now exceeds traditional desktop traffic.  For example, the brand owned by our customer Shop Direct saw mobile device sales grow 51% between June 2014 and 2015 and mobile now accounts for 59% of their total online sales. We have offered data collection from mobile apps for some time now, and with our latest release of the Celebrus Big Data Engine Version 8 Update 14 we have expanded and improved this support dramatically.

Here are the highlights of our latest Celebrus software release:

Windows Phone

It might only have small market share but there a host of companies out there offering apps for Windows Phone. Not surprisingly we now support Windows Phone with full data collection capabilities.

Automatic Instrumentation

This is possibly one of the coolest features we have in the mobile space. In the website world, we provide a complete understanding of customer behaviour with essentially no changes to the application (apart from including a single script tag in the app). We now provide this same simplicity for mobile. A single API call to us and we instrument your entire application so that data is collected across all controls. This is available for all platforms on iOS, Android and Windows Phone.

Enhanced Battery Life

As we all know, battery life is vital to the end user experience. Data collection on the other hand is a continuous process and can easily consume battery power even when the user isn’t actively using the app. With this release we’ve provided new options so your app can completely control when data is collected and when it is transmitted.

Native/WebView Linkage

Another key feature requested by our users. The ability to link activity across native and WebView components in your app. The collected data forms a single integrated session which is perfect for analysis, decisioning and discovery.

Developer Ease Of Use

Being a developer myself, this feature lands really well with me. We’ve added a host of new features to simplify the developer experience. You can now run your mobile apps with data collection enabled and without a Celebrus deployment, essentially a dry run environment. Equally useful are new options for logging and diagnostics.

This is another really exciting Celebrus release for us moving the state-of-the-art forwards in the mobile space will be a great benefit to our customers in every industry.

Tuesday, April 28, 2015

Ask Any Question

One of the most striking trends we have seen recently is how customers are using our product for multi-channel personalisation. Historically, the offers and promotions presented to web site visitors have been focused around an individual and their behaviours. For example, a visitor could be identified as interested in car insurance if they browse to pages involving car insurance, or search for the words car insurance and quote in the site search. Based on this segmentation, an appropriate offer could be presented. The key point in this example is that the segmentation is based on characteristics of that individual, in isolation.

Now consider a case where you want to offer your existing customers a discount or coupon, but only if they show an interest and haven’t yet purchased a car insurance product from you via any channel. Perhaps this offer is for just a few select customers based on their value to the business. So how best to calculate this value? Well a first pass might segment the customers by their location (for example, country), add up their purchases over the last few months, and then rank them with only the top n in each country receiving the offer. You can see straight away that this is a very different approach to the earlier example. To calculate this list requires a view across all the visitors and their activity on the site and other channels.

Total purchases is one measure but it misses some important facts. For example, how profitable are the products they purchased? To calculate profitability requires a view on the supply chain. Equally, you might also include in the calculation how much support have they required, either in calls to a contact center or call-outs (for example, home visits), to understand the profitability of that particular customer. To calculate this kind of most profitable customer requires many data sources to be integrated from across an enterprise.

The requirement to integrate many data sources and then action on the results has driven the latest integration in Celebrus, we call it Ask Any Question. With our latest release we can feed customer data into Hadoop and Teradata Aster, run wide ranging queries across huge data sets, and then action off the results.

Furthermore we provide a playbook which shows you step-by-step how to achieve this. One example feeds customer data into HDFS using Apache Avro. The data is loaded into Apache Hive where analytical queries determine the most valuable customers. The results are formatted using Apache Pig and pushed into Celebrus ready for presentation when the customers next visit.

Apache Avro is one of the latest generation of file formats adopted by the Hadoop ecosystem. It is designed to solve many of the problems inherent in binary sequence files (versioning, language independence and schema awareness). The Hadoop Data Loader creates Avro files and pushes them directly into HDFS using webhdfs. Once the files are in HDFS they can be added into Hive tables (and by extension, Impala) with the LOAD DATA INPATH command. This command is very efficient because it simply moves the files in HDFS into the Hive warehouse directory.

If you would like to learn more about the Celebrus Data Loader, take a look at these slides.

Monday, April 27, 2015

Data Visualisation

Multi-channel personalisation is just one use of the customer data Celebrus provides. Another equally valuable use of the data is for reporting and analytics. This can vary from high level dashboards for executives, through to deep path analysis in Teradata Aster. Making our data easy to use is a major focus for us. This is especially true with relational databases where data has to be joined across tables, a common cause of errors.

With all that in mind we now provide a simple to use set of data visualisation views. These views sit on top of the standard Celebrus data tables and do all the tricky stuff for you. They make it an absolute breeze to create compelling workbooks, dashboards and reports in whatever business intelligence tool you choose.

Along with these great product improvements we are also announcing our new partnership with Qlik. This is a major step forward for us, putting the right platform and data directly into the hands of decision makers across the business.

Wednesday, February 25, 2015

Data Loader for Celebrus

It is just a few weeks since we completed the acquisition of Celebrus by IS Solutions. Since then we’ve moved offices from Newbury to Sunbury-on-Thames. Not surprisingly that involved clearing out a whole heap of stuff which has been lying around for far too long. Along the way we unearthed several dusty product release CDs going back over a decade.

Looking back at those earlier releases, it’s interesting to contrast the focus back then with our latest release (v8 update 11). Ten years ago, data collection was focused primarily around reporting. Lots of totals, averages and aggregations of one kind or another. And the technology matched those requirements. In Celebrus terms, this was, and still is, implemented by our Analytics Server, part of our v8 Big Data Engine. Every so often the Analytics Server fires up and calculates summary information from activity in the last five minutes, hour, day etc. The results of that processing is written to a set of database tables. There’s nothing inherently wrong with the Analytics Server approach, it is simply that the world has moved on. 

The focus today is almost exclusively on highly detailed data about individuals, not just summary information. The data also needs to be available in near real-time. This information is crucial to understand each and every journey a customer has had with your brand. Armed with this insight into customer behaviour, a whole slew of possibilities unfold which enable you to understand and optimise your business, whether that be to offer a discount to a valuable customer, or to understand why someone chose a competitor’s product. All these use cases and many more start with data.

As Sherlock Holmes once said:

It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.

So with all this in mind, you will see in our latest release our new Data Loader. The Data Loader is our go-forward architecture for loading data at lightning fast speeds. The Data Loader scales to support huge amounts of traffic on some of the busiest web sites in the world, and can process 10’s of thousands of events per second (sustained). Not just that, it delivers the data into your systems in less than a minute, making new use cases around streaming analytics possible. We support MySQL, Microsoft SQL Server, Oracle and Teradata out of the box. Better yet, the Data Loader includes a pre-defined database schema covering some 75+ tables and models: everything you might want to understand about your digital customers.

In addition we’ve been working hard with the folks over at MongoDB. This release has been fully certified with MongoDB Enterprise Edition. This makes MongoDB the perfect data store for Celebrus customer journey data. This customer journey data is focused towards operational applications. For example, contact centre staff use this information to help them understand a customer’s interactions with your brand.

This is the first release where we have worked with a document database, and it has been a really good experience. The flexibility, simplicity and productivity of MongoDB is tremendous. For example, in MongoDB we simply store all business events in a single collection (rather than lots of normalised relational tables). Each type of business event contains some common attributes (timestamp, session number, customer identifier, event type and so on). Each event type also contains some more specific information, for example the purchase price, quantity and SKU code for a purchase transaction event. All of this just works with MongoDB, no friction, no joins, no complexity. Job done!

Interested in reading more? These slides walk through the Data Loader.