The afternoon session on day 1 started with a huddle focused on ‘creating a strong foundation for analytics success’.
In this session the main points that arose from it included:
- For any web analytics project, sometimes asking the customer service employees for their thoughts can allow you to get a real feel for business pressure points and current working environment.
- Always look ensure that your data is VALID before you start reaping insights from an analytics package.
- When setting KPI’s, identify if these are functionable and question what triggers them- what input leads to a change in KPI’s that you can influence?
- When making changes as a result of a web analytics audit, move in small steps- make small changes and remain agile.
Agility reared its head quite a few times during a number of sessions both as a requirement in those helping a business make the most of their web analytics package (consultant side) but also as something required on the client side. Both sides, in order to make web analytics a success need to be adaptive to change given the market is moving at such a fast pace.
Another area that was discussed in a number of huddle sessions was the need for any technical changes made to the website (as a result of a requirement for web analytics tracking) to be documented. From changes in code to naming conventions used in event tracking- documenting it means anyone new that dips into the web analytics account is clear on technical modifications while it also means that you’re not left clueless when someone leaves the organisation.
Participants at huddle ‘Creating a strong foundation for analytics success’: Image courtesy of @nicolasmalo
Whether we are on the consultancy side or client side, what we all can relate to is the regular requests for analytics stats and someone in this huddle recommended the way around this is to ask the person making the requests to document it by putting it into an email, following the ‘procedure’ for data requests. Challenge their request! They’ll start to question themselves WHY they want it, WHAT they are going to do with it (which in itself will make the request more actionable) and you’ll soon find out who has a real requirement for the data. Pernickety? Yes, but will benefit both sides in the long-term.
During lunch on day 1 the measure bowling team took the chance to meet-up to exchange bowling stories from last weeks MeasureBowling Europe event. More on that later!
Measure bowlers at X Change: Image courtesy of @nicolasmalo
The last topic of the day was on ‘elements of a strategic analytics framework’. This discussion looked at how you can communicate numbers and get people in an organisation motivated by analytics.
Some tips on how to create motivation across the business for analytics included influencing those in the ‘observation layer’- product managers/marketing managers/web developers/media agencies/creative agencies. Aim to adapt a change culture which is focused on becoming more action-orientated, where analytics is revised and recapped across teams via a ‘monthly market update’. One of the best ways I’ve found to get people excited in web analytics? Provide them with training sessions that show them reports and insights that are going to make them look really good and boost their profile in the organisation and they’ll start showing some interest.
Another great idea which was mentioned a number of times during day 1 was using gamification to get employees to look at reports on a more regular basis and relate it back to bonus. So essentially using healthy competition to get departments talking and using web analytics. For example each employee gets 10 tokens every time they implement an insight from web analytics and another 10 for reporting on the change success. Then at the end of each month or quarter a prize or reward is given to the person who has the most points.
Lastly, the benefit of using alerts on accounts was discussed and while this can ‘eliminate patrol duty’ it was argued it can create laziness as some people start relying on these and only deep-dive in their accounts when there is an irregular pattern in the data identified. Something which is key to developing web analytics ineffectiveness!Share