Every company should have a strategy to record and collect data about their customers. It should not only determine what data points to capture, but how this data is used across marketing, sales, product, and services. This information should then allow you to better understand your customers and help your business.
In this blog post I want to talk about the three types of data that are essential to form this understanding of your customer and your product. What the key data points of each group are and why they are so important to your strategy. As well as examples of how you can use this data to drive decisions.
- Purchases and products
- Interest or preferences
- Contact information
These key data points that form the view of the customer as a person. Individual demographics like age, gender, location as well as product information of their purchases, subscriber preferences, or their average order value. It will tell you who your customer is and what they their interest are.
Profile attributes are extremely powerful for any data strategy as they are made mostly of first party data. This is data that you own or given to you directly by your customers. Most of these attributes are mostly static, specifically demographical data, that you don’t need to collect again.
Use these attributes for as part of a personalisation and segmentation strategy to increase engagement amongst your customers. Use key information to form relevant segments of your customers that can then be used across marketing and sales. Directing your marketing and products for a more personalised customer experience.
- Website or platform visits/usage
- Engagement; clicks, likes, opens, etc
- Call or email responses
- Frequency and reach
- Number and order of interactions
Data that you captured on any interaction an individual has with your business. Interactions such as using your product, exposure to your marketing, or contact with your staff. In marketing it can refer to the reach and effectiveness of your campaign, like the number of views, click through rates, and overall funnel conversion.
Behavioural data proves performance as well as informs future activities. This makes it crucial for any optimisation strategy on a micro and macro scale. By capturing data across channels and throughout their entire lifecycle, you can start optimising your customer’s experience from start to finish. One of the key benefits of collecting behavioural data is that it is usually done discreetly. This means optimisation through use of A/B or split testing can be accurately used, as your customers won’t know they’re being experimented on.
But above all else behavioural data is key to any attribution model you use. Knowing your customer interactions across different channels and devices creates a single unified history of the customer’s journey. And ultimately, where you should spend your money within that journey to deliver results.
- Recency of events
- Dates of purchase or subscription
- Transaction time, date that data was captured
- Date of change of data
- First and last touch
Unlike the other two types of data, temporal attributes are actually applied across all the data you have. It includes the date you captured that information as well as date values such as a customer’s birthday, or date of sale. It can also include when data is changed within your records, which is important in creating a history of your customer.
This makes temporal attributes important not just in forming a view of your customer now, but in a view of their past and future. As such Temporal attributes are the key data that is used in marketing automation. Used to trigger certain events such as marketing messaging after an expression of interest, but also to predict when these actionable periods will occur.