Econsultancy recently surveyed 225 execs at manager-level and above in ecommerce, marketing and analytics in North America (with reported 2015 revenues above $250m).
The responses form part of our new report in partnership with IBM, entitled Secrets of Elite Analytics Practices.
Here are some of the findings…
Only 30% have a ‘thorough, up-to-date view of the customer journey’
Remember, these are companies with more than $250m revenue in 2015.
Though customer journeys are increasingly complex, across owned media, social and advertising (on different devices and platforms), there’s still a whopping 34% who have only ‘some view’, ‘limited view’ or ‘no view’ of that journey.
Customers rarely interact with a business in a single channel or in one visit and organisations are still coming to terms with this online.
Attribution, user testing and customer preferences all being neglected
The chart below splits out the 30% with a thorough view of the customer journey and the rest of the sample, asking each about component parts of their analysis.
Outside of the top performers, only 29% of respondents include customer preferences as part of their analysis. Without this focus on customer centricity, improving user experience is hard to achieve.
Similarly, in the same group, only 29% analyse marketing touchpoints (essentially attribution) or seek to understand customer struggle (through analysis of individual sessions).
41% of businesses are doing too much leg work
Part of the difficulty businesses face during a digital skills shortage is maintaining business as usual, whilst improving processes and upgrading legacy technology.
41% of respondents to our survey said their analytics solution requires analyst involvement to produce insight.
This was par for the course as digital marketing was maturing, but in order to concentrate on innovation of product and experience, businesses must now have analytics software that provides insights to practitioners and stakeholders on a regular basis.
Indeed, 80% of those with an analytics solution with some degree of automation said they made significant time savings.
This automation provides an early-warning system and frees up analysts to fry bigger fish.
Three sequential challenges of analytics maturity – tying together data, increasing budget, getting senior buy-in
The table below shows challenges faced by analytical elites, the average and the laggards (as defined by number of tools at their disposal and the sophistication of their use).
The greatest challenge for each respective segment is highlighted, showing that tying disparate data sets together is the initial big challenge, followed by securing more budget, and ultimately senior buy-in.
Laggards are in a chicken-and-egg scenario, the average are scaling up, and elites have realised the strategic gains to be made.
QED perhaps, but further evidence to show that data comes first and everything else follows.
For more on this topic, subscribers can download the report, Secrets of Elite Analytics Practices.
And for further reading, check out IBM’s white paper on Customer Experience Analytics: From Data to Insights to Opportunities.