Data, data everywhere, but not a drop to drink

19 Sep 2016

  • Analytics
  • Data

By: Andrew Birmingham for ADMA

Analytics is the biggest draw-card, according to the data compiled by Martech Advisor (MTA).

More than $US350 million worth of cash flowed into the data science game, with web and mobile analytics in particular finding favour with the money men.

But data is both a blessing and a curse. Too much data remains locked up in company silos, its availability subject to difficult technical integrations (which explains the popularity of APIs) or simply the vagaries of competing tribes within a business.

And the data itself is both structured and unstructured, sometimes owned by a brand and increasingly now owned by a third-party vendor or an agency.

But it all needs to be brought under control in order to meet the basic customer experience requirement.

The primacy of data has ushered in a golden age for data scientists whose salaries have risen accordingly.

IAPA for instance estimates that typical data scientists can command salaries north of $120,000 in Australian, while those with specialist social analytics skills occupy the stratospheric world of $200,000 plus packages.

It is little wonder, then, analytics outsourcing has emerged as one of the faster growing outsourcing disciplines in the world.

Web Analytics

Almost 90 per cent of the world’s commercial websites are running some form of web analytics, and almost 40 per cent of those sites are using only the simplest metrics (Thank you Google).

According to researcher Gartner, customer-based segmentation, data warehousing or targeted email don’t yet feature in the thinking of a large minority (40 per cent) of many web administrators.

While web analytics is one of the earliest form of business intelligence online, and the market exhibits many characteristics of maturity, beneath the surface innovation continues to bubble over, particularly around the mobile web, the app economy and increasingly advanced analytics, according to the analyst.

In its report, called ‘Market guide for web analytics’ Gartner defi nes web analytics as the market for specialised applications used to understand and improve the online channels’ user experience, visitor acquisition and actions. Products offer reporting and segmentation, analytics and performance management, historical storage and integration with other data sources and processes.

At its core, though, it is about understanding what customers are doing on the web and giving business leaders actionable insights to improve services.

Not surprisingly, web analytics is cloud friendly in the extreme and, indeed in Gartner’s view, the market has now almost entirely migrated away from on-premises solutions.

Further and deeper analysis of the data offers a number of opportunities, according to Gartner:

•  More insight. Solutions now provide attribution and recommendations, especially for ecommerce and anomaly detection. Adobe has integrated predictive analytics into its Web analytics platform. IBM has some industry-leading predictive capabilities in its SPSS platform, but has yet to fully integrate them. However, most Web analytic tools remain focused on historical, descriptive analytics on aggregated data

•  Greater understanding. Replay vendors provide session replay (with its deeper understanding of how customers are interacting with your website) than traditional Web analytic vendors provide. Session replay also provides insight into the customer experience

Faster responses. Some offerings, including Webtrends’ Streams, enable users to perform virtual real-time Web analytics. This can be useful, for example, in triggering an email offer to a known user on an e-commerce site who has just abandoned a shopping cart

•  Friendlier integration. Some offerings enable you to integrate with more data sources, such as CRM data, customer profi le data and data from other channels

The other big issue is capabilities. While often the tools are available, users are often poorly trained and usability and confi gurability is often hamstrung by the requirement for would-be web analysts to understand things like javascript, says Gartner.

Social Analytics

Facebook CEO Mark Zuckerberg has a very simple three-point plan for global advertising domination that he has happily been promoting to his investors for several years. Firstly, connect everybody in the world to the internet. Then learn everything he can about them from their behaviour. And finally, based on these insights, flog space.

With more than 2 billion people using social networking sites, Zuckerberg’s grand vision seems more realistic by the day.

A recent IBM study in social media analysts revealed some key data points describing the global reach of social media:
1.43 billion people worldwide visited a social networking site last year
•  3 million new blogs come online every month
• Last year, 1 million new accounts were added to Twitter everyday
• Facebook has 850 million active users every month (in 2015 it has grown past 1.4 billion if you include Instagram and WhatsApp)
80 per cent of internet users say they prefer to connect with brands via Facebook
65 per cent of social media users say they use it to learn more about brands, products and services

But like all the great plans, the simplicity belies some very serious complexity.

According to a joint MIT Sloan Management Review and IBM Institute of Business Value study:

“Organisations that excel in analytics often outperform those who are just beginning to adopt analytics by a factor of three to one. And top performers are 5.4 times more likely to use an analytic approach over intuition and gut instinct when making decisions.”

With social media likely to take an increasing bite of the advertising budget in the years ahead, according to a consistent view of most of the studies, that demand seems likely to continue.

As the name suggests, social media analytics involves hoovering up data from social networking services and blogs and then using the data to determine buyer sentiment and intent, and increasingly to predict buyer behaviour.

Social networks like Facebook and Twitter have encouraged companies to build services around this by making the data available and that in turn has seen the development of a healthy service ecosystem.

However, for the companies in that space (as in search) they are subject to the vagaries of the policies of the social networks themselves.

There is a critical difference between social analytics and earlier forms of data analysis and it is to do with the data itself, which is largely unstructured text from tweets and posts.

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CATEGORY Analytics Data

TYPE Article