Data can shine like a brilliant diamond at the core of business success.
But if data is mishandled with poor processes, then it can become a cracked prism reflecting dysfunctional practices within a company – and no-one wants that.
You should be able to fish easily in your data lake for insights that can help measure the effectiveness of your marketing, prospecting, retention and other activity, all so you can make useful decisions.
But if data fatigue sets in there’ll be uncomfortable ripples across the business. Relevant teams and departments stop finding any joy in their work and the C-suite will turn round and start asking: “How is all this data actually helping us improve your business?”
Spot the symptoms of data fatigue
You need to be able to see the signs of fatigue, so you can act to prevent any decline in performance. It might manifest as a quality issue – when people start questioning the reliability of the data with which they’re presented.
For instance, a team might start puzzling over subscriber numbers and whether the source is the billing number or from the accounts inputted into the customer relationship management (CRM) system. It may not be clear whether the total subscriber number includes cancellations.
Or in ecommerce reports, there may be confusion over whether order numbers include returns or not. It might even be a simple hygiene problem to do with tagging and labelling. Just The mere presence of introducing typos into column headers can cause big headaches further down the line when it comes to data collation.
If a lack of clarity hovers around the data, the insights that help with business-critical decisions begin to fall away. Issues can snowball and a company then has to start spending unnecessary money playing Whac-A-Mole to fix problems across the board. There’ll then be an existential cry of despair from the CMO and a general feeling that data is more trouble than it’s worth.
Maybe it’s time to ditch the years of accumulated data and just hold on to summarized versions?
Don’t jettison your data gold dust
Don’t press that button. At Pickaxe we’ve accumulated many years of experience at helping organizations order their data, introduce best practices and find ways of using it to make business processes and operations both more efficient and effective.
Our background lies in being an operator, so we’ve worked through many of the same problems ourselves and have developed some rock-solid guidelines to ensure companies are in good data health and get the best from the full life cycle of their information.
These recommendations include:
- Ensuring there is data stewardship within a team and someone has the job to ensure data is processed correctly.
- Prioritizing data governance by setting rules and expectations around what data should appear and what formats it should appear in. This will help analysts spot the anomalies representing problems that need solutions.
- Identifying the internal users of your data and what use cases they focus on, so you can encourage them to become advocates for best practice and investment. A CRM marketer will focus on user purchase patterns and their history while a retention marketer will want to know who might be at risk of churn to target them with offers.
- Figuring out if you’re storing things in the right format at the right cost for the right users.
- Making sure your team is match fit. Have they had a refresh on which data is valid for which use case and when it will arrive? Has the legal department been briefed recently on what data you have, how it works and trained the team on regulations around use of data?
Winning with data – a Spartan success
A recent project with Spartan Race, the company that mounts endurance, obstacle races of varying difficulty, is a good example of how we helped a business find real returns with improved data practices
The CMO and the marketing team were having challenges with getting accurate attribution across all paid media channels, including TV ads and Out-Of-Home (OOH) ads. Recent changes in cookie blocking and iOS app tracking added confusion in the data, and made it difficult to identify what marketing actions were driving conversations.
We carried out a full breakdown of channels and tactics to ensure accurate mapping of attribution and introduced weekly updates and self-learning model to calculate the exact point of diminishing return on every single channel. Our expertise in data science and marketing practices then allowed us to create a comprehensive Media Mix Model.
A predictive Sandbox now allows Spartan to plan out different scenarios and understand how budgets can change over time and the outcome has been to increase effective Return on Ad Spend (ROAS) by 22%.
Feel like you're stuck in a data swamp or suffering from data fatigue?
If data fatigue sets in there’ll be uncomfortable ripples across the business. Relevant teams and departments stop finding any joy in their work and the C-suite will turn round and start asking: “How is all this data actually helping us improve your business?”
