Marketers are always looking for a competitive edge, the next new thing that will improve customer engagement, or just a better way to execute existing communication programs. To that end, we’ve outline three data attributes that nearly all B2C and B2B marketers can employ for targeting (or exclusion), content, and reporting purposes. Each of these has proven to be effective at improving communication performance and the customer experience, while reducing attrition.
The retail world often provides us with some of the best examples and each of the data attributes outlined below can be modified for most other industries. These examples demonstrate how data can be used to improve relevancy, reduce opt-out attrition, reduce message clutter, and deliver more useful metrics. However, they can be used to address other common business challenges beyond what we’ve outlined here.
How Often Are They Purchasing and When Did They Last Do So?
Purchase Cycle is a common metric in the world of high frequency retail (think grocery and CPG) that measures the average number of days between purchase events that can be applied to any reoccurring event, regardless of industry (ex. Days between whitepaper downloads, gym attendance, doctor visits, etc.). Last Purchase Date is simply the last date that a consumer purchased, regardless of channel. Combined, these attributes provide marketers with the ability to time communications based on an individual’s reoccurring need frequency. For example, if a customer of Domino’s orders on average every 21 days (purchase cycle), and they last ordered 14 days ago (based on today’s data minus the Last Purchase Date), they will probably purchase again in about 7 days. This is the time to start thinking about that promotional SMS, email, or app push.
Conversely, if the same customer ordered Domino’s yesterday the likelihood they will order today is extremely slim so excluding them from the daily promotional communications (email, push, SMS, etc.) reduces the number of opportunities they have to opt-out of future communications until there is a reasonable chance they will use the delivered promotion. It also frees them up for relationship building communications without being overwhelmed by multiple messages. While some marketers do not want to risk losing potential revenue, this logic fails to consider the negative impact that over communication has on customer LTV when customers revoke a brand’s right to communicate with them. This is something all marketers should test for short and long-term impact on retention.
Individually, these attributes have critical applications for trigger programs and reporting. As an example, for marketers trying to grow their sales by capturing a greater share of wallet, Purchase Cycle can show if less time is passing between purchase events, an indicator that this is happening. If you are utilizing an offer strategy that is designed to drive this kind of behavior, then this metric is critical to accurately measuring your strategy’s success.
Where Are Your Prospects in the Acquisition Funnel?
Despite being a critical point in the acquisition funnel many prospects are lost before their first purchase or immediately after as activation is given short shrift by many marketers or is frequently blended into the welcome program. Whether a brand has a digital welcome program or not, most new opt-ins are effectively dropped into the generic promotional message stream on day one. With little thought being given to the action marketers want them to take other than “buy now”, or how it might be different from established customers, this contributes to a significant drop off for most brands. To shepherd prospects down the path to becoming a buyer isn’t enough. Thought needs to be given to cultivating repeating purchase behaviors. This can be done by designing touch points and programs that specifically encourage 1st, 2nd, 3rd, etc. purchase events with the goal of building long term behaviors. A Total Purchase Events data attribute can be used to trigger touches on this path and can also be used to track what percent of prospects make it to each stage. This kind of insight can identify problem spots such as a high rate of drop off between the 1st and 2nd purchase event which could indicate dissatisfaction with the purchased product or service, the delivery of it, or poor customer service.
For each of these three attributes, specific time frames should be applied based on your unique business needs and overall average customer purchase cycle. When in doubt, a 12 month period is a good baseline as it removes seasonality. Brands with products and services that have longer purchase cycles like durable goods may want to look at something longer. To give yourself the best insights, use multiple time frames and create attributes such as “Total Purchase Events – Last 12 Months”, “Total Purchase Events – Last 30 Days”, etc. Data attributes like these provide marketers with powerful tools that improve marketing effectiveness and their ability to create audience insights through more meaningful reporting and testing.
What are you doing to leverage your transaction data assets?
For more on our Data Strategy Services click here or email us at email@example.com.
A few comments on the recommended data attributes: The term “purchase” was used to create consistency within this article but other terms such as “conversion”, “download”, “meeting”, etc. can easily be substituted for whatever is most relevant to your business. When creating new attributes such as Purchase Cycle, Last Purchase Date, and Total Purchase Events you will need to consider a few aspects. Here are two of the most critical. First, how does your business look at purchase or conversion events? If there are often more than one in a single day for a customer, you may want to consider calculating Purchase Cycle and Total Purchase Events based on unique days with a purchase or conversion event. Second, consider how you want to treat returns. Excluding returns generally gives marketers the kind of attributes they want to use for marketing, but this also masks customer engagement events so parallel purchase and returns attributes may make sense for your business. Regardless of how you decide to derive your attributes, it is critical that marketers have access to these definitions so they aren’t misinterpreted or misused.