In part one, we looked at the four types of customer data; Personal, Behavioural, Engagement and Attitudinal.
But collecting customer data is only half of the story. Once you have a system in place for collecting data, correct management of that data becomes imperative.
Join us as we take a deep dive into building out your strategy for proper management of your customer data. We will cover how to maximise the advantages this data can yield, as well as avoiding the pitfalls of improper data management.
This might seem like a dry topic, but it’s so important to protect your customer’s privacy and only use the data you collect to build a more effective customer engagement strategy that works for both you and your customers.
Building the strategy
The first step in building out a successful data management strategy, is understanding what your company would like to achieve from your data.
Having concrete goals enables your team to understand what they need to extract from your data, and how they need to activate it. This ensures that management of your data aligns with your company’s short and long term targets.
Successful SaaS companies align and measure their quarterly targets by their North Star Metric. Analysis and activation of customer data should therefore be considered alongside the NSM.
For example, if an App’s NSM is the number of messages a paying customer sends on their platform, the Product team’s data analysis and management should centre around this. The team could collect data on the personal characteristics common to activated users or assess the engagement journeys of successful clients through the App.
Once you have identified the personal, behavioural, engagement and attitudinal characteristics of your Ideal Customer Profile, you can then use this information to hone in on targeting and activating this specific segment. In other words, once you’ve set all this structure up, it becomes really easy to see your next steps in increasing customer engagement.
Avoiding the pitfalls
In contrast to the benefits successful data management processes can yield, there are many risks involved with poor data management.
The most obvious of these is the security risk of data breaches and improper data collection.
To collect user data, your platform must be in full GDPR compliance. In addition, if you operate in specific industries, such as Healthcare or government bodies, you may need to pass additional compliance certification. Failing to collect data within a compliant process can result in high costs and irreparable damage to reputation.
If you are considering using an external platform to store your customer data, you need to confirm that they meet sufficient security credentials.
Ensure your data storage system has passed Cyber Security certification, such as the certification provided by IASME. You should also check that the company has an appointed Data Protection Officer and Data Processing Arrangements available to sign. Finally, make sure to assess the infrastructure where your customer data will be stored. For example, at GoSquared, storage of our customer data is supported by Amazon Web Services to ensure maximum security and data compliance.
Over-collection of data
In part one of our series, we discussed the importance of collecting as much data on your customers as possible. There is, however, such a thing as ‘too much data’.
Think back to our previous point about designing your data collection strategy in alignment with your company goals and North Star Metric.
Otherwise, your teams risk getting lost in swathes of irrelevant data. This dilutes their focus and can also result in product iterations that aren’t relevant to the needs of your ICPs. Over time, this will impact your company’s growth, not to mention the waste of resources spent on the overcorrection of irrelevant data.
Before launching your data management processes, it is worthwhile to implement company-wide rules for data collection first. This ensures uniformity of collection across your team(s). For example, you could set hard rules on acceptable methods of tagging data, naming files, or categorising files. This ensures your data does not turn into a disorganised pile later down the line. Setting these processes in place now may take additional time, but it is well worth the headaches it can save!
Cleansing old data
In a similar vein to the above, data relevant one month ago may not still be relevant six months down the line. This is especially true with personal customer data. It is no good sending out expensive, highly-targeted marketing messages to outdated customer emails or social media channels. To avoid this, we recommend your team schedule ‘data cleansing events’ to clear out old data. The cadence of this will depend on your industry; for example, a B2C company might cleanse data on a more regular basis than a B2B company.
Multiple customer management tools, or siloed data processes
Many SaaS companies find themselves taking on more and more customer data platforms over time. But the less centralised your data management system is, the more likely it is to have cross-platform inaccuracies. In addition, if certain teams have access to one ‘source of truth’ for data but another doesn’t, your company risks a lack of cross-team communication and siloed data sources. This can lead to issues when deciding on which areas to prioritise, as the Product Team may have a completely different understanding of the customer data than the Customer Success team.
Letting data go to waste
Forrester calculates that anything between 60-73% of companies’ customer data goes unused. A report by the Harvard Business Review backs this up, finding that “less than half” of a company’s customer data is actively used in any decision-making process. If it is not coupled with appropriate analysis and action, there is no point in investing a wealth of resources in customer data. Ultimately, it is better to properly analyse and take action on a smaller set of data than to simply collect as much data as possible and leave it to gather dust.
It’s not scary, now that you know the rules.
In this article, we have taken a look at some of the common pitfalls in customer data management. But with careful observation of compliant collection processes, cross-team data access, and activation of customer data, you can ensure to make your data work for you and benefit from its insights to help grow your business.