This is part four of the series titled Understanding Zero-party Data.
In part three of this series, I covered the key use cases as well as the personalization benefits of zero-party data. However, the biggest challenge with zero-party data (as with all other types of data) is to ensure that data is fresh when it’s activated or acted upon.
Data freshness refers to how up-to-date a specific data point is at a given point in time.
In this guide, I’ve covered some common scenarios that depict the importance of keeping zero-party data fresh.
Data can be accurate but not necessarily fresh — if you collect a user’s phone number accurately, it doesn’t come with a guarantee that the number will forever belong to that user. To maintain freshness, you’d need to explicitly ask the user to confirm their phone number on a schedule, like once or twice every year, depending on how critical customer phone numbers are for your business.
Therefore, it helps to keep in mind that data that’s fresh today may or may not remain fresh tomorrow.
Keeping zero-party data up-to-date ensures that it reflects a user’s identity or preferences at the point in time when the data is activated.
Without further ado, let’s explore some common B2B and B2C scenarios that depict the importance of the freshness of zero-party data:
Onboarding survey data is not forever
I come across a new tool, I create an account to try it out, I specify my role and team size in the onboarding survey, but the product doesn’t meet my needs so I don’t bother going back. But I then receive an automated email asking me to check out a case study relevant to my industry, and since I’m curious, I click the link open but get distracted.
After exhausting the nurture campaign set up for my persona, I stop receiving emails from the sales team. However, my actions as well as the role and team size I’d mentioned explicitly, resulted in a score that lives against my record in the CRM — a number that either remains static or increases based on future activity.
A couple of months later, an enthusiastic account executive finds me in the CRM, decides that I’m a marketing-qualified lead (MQL) based on my score, and starts emailing me asking me to schedule a meeting.
Now, a lot can happen in two months — my needs can completely change, and even my role can change. Instead of asking to schedule a meeting, if I was simply asked to confirm or update my current role and team size (data I’d shared explicitly earlier), I would have certainly done that if I had any interest in the product.
And this updated data is so valuable for the brand as it helps answer several key questions:
- Does this lead still meet the requirements for our ideal customer profile or ICP?
- Is this lead the decision maker or not?
- Is there an overlap between this lead and another from one from the same company?
- Are we even offering them the right product from our suite?
- And so on.
It’s much easier and cheaper to ask prospects to confirm their preferences than to sift through them in the CRM and keep following up without really understanding what they really want.
A customer is not forever
One of the biggest mistakes B2B brands make is to pay more attention to prospective customers than the current ones. Very few companies proactively reach out to paying customers (unless they’re looking to upsell) to make sure that they’re still able to derive enough value from the product to keep paying for it for the foreseeable future.
Periodically checking in with customers to confirm their preferences or to collect additional data points can help in so many ways:
- Better understand the future needs of customers
- Understand current usage to identify at-risk accounts
- Identify departing users who can potentially take the product to their next organization
- Recruit champions, advocates, and partners from among the most active users
- And so on
One could argue that behavioral data (collected implicitly) is meant to help with the above — it surely is. However, behavioral data is essentially historical data that tells you what someone has done in the past; it doesn’t tell you what someone wishes to do in the future.
Therefore relying solely on historical data to predict what users will do next is not the best strategy, and is certainly not enough if a brand is serious about personalization.
Once a buyer, not always a buyer
I like coffee so when I come across a new brand, I usually buy an assorted sampler. And before I know it, my email and phone number are dumped into the brand’s communication systems that start sending me offers on every occasion (or no occasion at all). This goes on until the day I get annoyed and decide to unsubscribe from the brand’s communication altogether.
Just because I bought from the brand, doesn’t mean I like their product and I want more. Oh and they did send me a CSAT (customer satisfaction) survey, and because I like surveys and since I didn’t hate their product, I responded with a 3/5.
Now, if they instead asked me explicitly if I liked any of the coffees I’d sampled, I would have definitely responded — heck, I’d even tell them my specific taste in coffee and what equipment I use so that they could recommend something I’d love. But they chose to send me a CSAT because, well, everybody is doing it — such a missed opportunity to collect super fresh explicit data directly from a prospective evangelist.
A loyalist can be forever
Displaying or communicating about similar products from other brands is common among multi-brand stores. But when it comes to certain products, some people are fiercely loyal toward certain brands or are just not interested in changing something that’s already working well for them.
By proactively asking customers for their most preferred brands across different product categories, multi-brand stores can not only deliver offers that convert, but also benefit from the following:
- Anticipate demand and negotiate better deals with brands they sell the most
- Determine which new brands to onboard and which existing ones to deprioritize
- Run engagement activities in collaboration with the best-selling brands to strengthen loyalty toward the store
Besides these obvious benefits, identifying brand loyalists and keeping them engaged can help stores add new revenue streams — from enabling brands to run advocacy campaigns to helping them gather feedback on new products — the possibilities are endless.
Conclusion and what’s next
Delivering “personalized” offers based on “past purchases” is passé.
Remember, people’s preferences are changing every day and brands have to do so much more to keep buyers from going astray.
A zero-party data strategy is key for brands to unlock new opportunities to better understand the needs and priorities of their audiences.
In the absence of zero-party data, brands have to rely solely on first-party data which is historical by nature and is never an accurate representation of the real world at any given point in time.
This brings us to the end of the series on zero-party data (for now). If you'd like us to cover more specific topics such as the consequences of ignoring zero-party data or how it can enrich a first-party data set, do let us know!