Coremetrics / IBM Digital Analytics (discontinued)

For our clients, IBM Digital Analytics serves as our primary digital analytics solution. We use the CMS platform to construct the solution, and we've created an environment that enables us to act on any data gathered by IBM Digital Analytics (via Exact Target for example). After the deployment is complete, we audit the tagging permanently and offer advice to our clients. For our clients that are in the finance industry, we often provide dashboards and in-depth analyses using IBM Digital Analytics.

• UI
• Automated reports
• Easy to use

• Better optimisation
• Pricier than competition
• Learning curve and set up curve is steep

Kissmetrics, Google Analytics and Adobe Analytics

Kissflow, Google Analytics, Adobe Analytics

We leverage IBM CXA predominantly in support of clients. We do, however, use it against our own web assets to understand customer journeys. Predominantly, we help clients first create a simulation to identify customer experience issues or opportunities and create a hypothesis. In collaboration with the company, we then establish a proof of concept using real customer transactions to validate our initial findings and confirm areas of opportunity.

Whilst there are many low-cost tools that provide insight into user activity and indeed offer session replay, this is an invalid basis for analysis; the focus is on raw data and not drawing conclusions from user activity. IBM CXA is particularly strong for transactional assets such as an eStore. It can, however, be applied to any situation involving user activity.

A particularly strong aspect of IBM CXA is the core foundation it sits upon - UBX or Universal Behaviour Exchange. Not only is the capture of user behaviour through activity automatically recorded, but other, physical activity can also be channeled into the solution e.g. physical store purchases, call centre interactions, etc.

IBM CXA is, therefore, in a strong position to represent entire customer journeys and not simply digital ones.

• The ROI question needs to be focused on clients and not our use internally. We leverage this as a platform for designing customer journeys for our clients.
• The ROI case is far easier to make for eCommerce operators. Customer journey failings can be easily quantified and seemingly simple issues can generate large ROI outcomes. For web assets without a trading bias, ROI can be established using notional value attribution. For example, a lead for motor insurance that is lost can be costed based on average policy value over a typical lifetime; struggle on a customer self-service portal can be quantified on the cost of handling through a call centre; etc.

Adobe Analytics and Lucky Orange

HubSpot, Microsoft Dynamics CRM, ARIS Business Process Management (BPM)

We are currently testing a couple projects using ML Algorithms in Watson to improve the customer experience. Watson Customer Experience Analytics excels at journey analytics, automatically identifying the pain points of customer experience. We collaborate with business and technology teams and come up with predictive algorithms to get better business results from our existing analytics program. It provides us actionable omnichannel customer behavioral insights and also guides decision making.

• Positive: Faster solution
• Positive: Better prediction and service
• Positive: Secure and private

IBM Cognos

Tableau Desktop, IBM SPSS, IBM Cognos

• Heavy amount of time for training and setting up
• Increased optimizations of the conversion funnel
• Costlier than other similar web analytical solutions

Kissmetrics and Google Analytics

Kissmetrics, Google Analytics, MetricStream

• Overall, the positive impact has typically been on improving the reliability of data driven decisions to improve path to conversion.