Adobe and Station10: Your data questions, answered
Station10 recently held a joint webinar with our partner, Adobe. The session – ‘Do more with your data’ - aimed to help our listeners understand how to better use data to drive their marketing campaigns and influence business decisions.
It was a great session with over 100 attendees, many of whom asked some interesting questions. We’ve heard a number of these before, so we thought we’d write up our answers so we can all learn how to do more with data:
- What is the difference between a Data Scientist and an Analyst?
- What is NPS and how is it used?
- How do organisations collect cross-device and multiple device-user data?
- How easy is it to implement data-driven changes within an organisation?
- Are there any pitfalls to avoid on the journey to becoming a data-driven organisation?
What is the difference between a Data Scientist and an Analyst?
The term “Data Scientist” is one that we are always wary of using at Station10. It’s a term that can be easily misconstrued as it sounds expensive, and even slightly pretentious.
We like to think of the data scientist and the analyst as being a part of a continuum.
Data scientists and analysts both work with, and interpret data. One is not better or smarter than the other – they just have a slightly different focus within an organisation.
The analyst can be seen as the “everyday” data interpreter, focused on reporting on business as usual activities. The data scientist, in contrast, plays more of a “big-picture” role. These guys are usually tasked with completing project-based analyses through the building of statistical models.
Which is more important?
It is important to have a balance of analysts and scientists within any organisation. You need both to get the best results from your data. A number of organisations may find that the data scientist fits less easily within their business model. In this situation, data consultancies like Station10 are bought in to provide data scientist expertise.
What is NPS and how is it used?
NPS stands for “Net Promoter Score”. It is a score used to measure customer satisfaction.
When an organisation works out the NPS of their product or service, the following questions are usually asked:
- Would you recommend this product/service to a friend or colleague?
- How would you rate this product/service from 0-10?
Customers that rank a product or service from 9-10 are called “Promoters”.
Customers that rank a product or service from 7-8 are “Neutral”
Customers that rank a product or service from 0-6 are “Detractors”.
NPS = % Promoters - % Detractors
The result is an arbitrary score of customer satisfaction, that can be used to uncover the value of allocating budgets for improving relationships with customers.
By comparing the NPS before and after budget spend, marketing teams are able to uncover whether further investment is worthwhile.
How do organisations collect cross-device and multiple device-user data?
One question we’re often asked is how can organisations collect data on a single customer that logs in across multiple devices – their phone, their iPad, their laptop (for example)? Equally, how can we know which user to track when customers log-in to a shared device?
Multiple devices …
Many organisations address this issue by using a “single sign on”. You’ll all have used this before – when you visit a web page such as Facebook and have been asked to sign up or log in. A single sign on joins up all visitor data, so that if a customer logs into another device, that information is captured and stitched up to create an individual or “Single Customer view”.
Whilst the “single sign on” helps to join together customer data – it is not failsafe. It cannot pick up data when customers visit a web page anonymously. That’s why many websites will remember your log in information and automatically keep you logged in for a repeat visit.
The Adobe Premium Data Workbench can be used to aggregate customer data across devices. It is a powerful tool, used to understand exactly which customers are visiting a website, how, and when.
Tracking one particular user when there is more than one using a device, is more difficult.
This is common in a family or an office situation, when multiple users regularly use the same computer. The single sign on is only useful in this situation when users log in/out of each other’s accounts.
Customer research is often the most effective way to predict who is visiting a page.
For example, if a hire car company knows that its vehicles can only be rented by those with a driving license, this eliminates young children from the family mix. If they also know that in 80% of family situations it is the man of the house that will rent a vehicle; they can narrow down further. From this kind of customer insight, it is easier to make a judgement as to who the visitor might have been.
How easy is it to implement a data-driven changes within an organisation?
When data projects are completed, and insight is gained, it is not unusual for data to suggest changes to strategy.
At Station10, we believe that if data is being used purely to back up gut instincts and current processes, it is not being used to its full advantage. In fact, it is being undermined.
Data insights need to be repeatedly applied to current practices and allowed to influence decisions as to how to improve.
These insights can, however, be difficult to apply to an organisation that is used to working in a particular way. In order to implement a new way of working, behaviours need to be changed.
A sudden enforcement of change to the way that business is used to working – like changes to the way that marketing budget has always been allocated – may be detrimental to overall activity. Teams running at highest capability, based on an older model, may initially be more effective than teams facing sudden upheaval to the way they work.
Implementing new models can therefore be a slow process. But it is completely worthwhile.
New models based upon data insights should be applied gradually, for initial benefits. These benefits need to then be communicated, understood and shared clearly across the entire organisation to ensure that internal teams are on board with this data-driven change.
Are there any pitfalls to avoid on the journey to become a data-driven organisation?
To succeed as a data-driven organisation, data needs to be used to inform future decisions, rather than support current ones.
However, it can be difficult to encourage individuals within an organisation to listen to the data.
Therefore, one of the biggest challenges that we see on the journey towards data-driven optimisation is the changing of culture and individual mind-sets within an organisation.
To succeed, it is no longer enough to have the correct people, processes and technology in place – organisations need to shake up their internal culture to become data-driven.
More often than not, this responsibility lies with senior leadership, because without high-level buy-in from the guys at that top it can become very difficult to drive data-driven change.
It is also important to note that communication is key and that culture must be passed down through all organisational teams, to ensure successful implementation of data-driven changes in the future.
You can read more about how to implement cultural changes to become a data-driven organisation in Station10’s blog “Manage instinctive reactions to change to embrace data-driven innovation”.
If you're interested in listening to our Webinar with Adobe, you can do so at the following link.
If you have any more questions that you would like us to answer, please do get in touch!